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1# This file is part of ip_isr. 

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 math 

23import numpy 

24 

25import lsst.geom 

26import lsst.afw.image as afwImage 

27import lsst.afw.math as afwMath 

28import lsst.pex.config as pexConfig 

29import lsst.pipe.base as pipeBase 

30import lsst.pipe.base.connectionTypes as cT 

31 

32from contextlib import contextmanager 

33from lsstDebug import getDebugFrame 

34 

35from lsst.afw.cameraGeom import (PIXELS, FOCAL_PLANE, NullLinearityType, 

36 ReadoutCorner) 

37from lsst.afw.display import getDisplay 

38from lsst.afw.geom import Polygon 

39from lsst.daf.persistence import ButlerDataRef 

40from lsst.daf.persistence.butler import NoResults 

41from lsst.meas.algorithms.detection import SourceDetectionTask 

42from lsst.meas.algorithms import Defects 

43 

44from . import isrFunctions 

45from . import isrQa 

46from . import linearize 

47 

48from .assembleCcdTask import AssembleCcdTask 

49from .crosstalk import CrosstalkTask 

50from .fringe import FringeTask 

51from .isr import maskNans 

52from .masking import MaskingTask 

53from .overscan import OverscanCorrectionTask 

54from .straylight import StrayLightTask 

55from .vignette import VignetteTask 

56 

57 

58__all__ = ["IsrTask", "IsrTaskConfig", "RunIsrTask", "RunIsrConfig"] 

59 

60 

61class IsrTaskConnections(pipeBase.PipelineTaskConnections, 

62 dimensions={"instrument", "exposure", "detector"}, 

63 defaultTemplates={}): 

64 ccdExposure = cT.Input( 

65 name="raw", 

66 doc="Input exposure to process.", 

67 storageClass="Exposure", 

68 dimensions=["instrument", "detector", "exposure"], 

69 ) 

70 camera = cT.PrerequisiteInput( 

71 name="camera", 

72 storageClass="Camera", 

73 doc="Input camera to construct complete exposures.", 

74 dimensions=["instrument", "calibration_label"], 

75 ) 

76 bias = cT.PrerequisiteInput( 

77 name="bias", 

78 doc="Input bias calibration.", 

79 storageClass="ExposureF", 

80 dimensions=["instrument", "calibration_label", "detector"], 

81 ) 

82 dark = cT.PrerequisiteInput( 

83 name='dark', 

84 doc="Input dark calibration.", 

85 storageClass="ExposureF", 

86 dimensions=["instrument", "calibration_label", "detector"], 

87 ) 

88 flat = cT.PrerequisiteInput( 

89 name="flat", 

90 doc="Input flat calibration.", 

91 storageClass="ExposureF", 

92 dimensions=["instrument", "physical_filter", "calibration_label", "detector"], 

93 ) 

94 fringes = cT.PrerequisiteInput( 

95 name="fringe", 

96 doc="Input fringe calibration.", 

97 storageClass="ExposureF", 

98 dimensions=["instrument", "physical_filter", "calibration_label", "detector"], 

99 ) 

100 strayLightData = cT.PrerequisiteInput( 

101 name='yBackground', 

102 doc="Input stray light calibration.", 

103 storageClass="StrayLightData", 

104 dimensions=["instrument", "physical_filter", "calibration_label", "detector"], 

105 ) 

106 bfKernel = cT.PrerequisiteInput( 

107 name='bfKernel', 

108 doc="Input brighter-fatter kernel.", 

109 storageClass="NumpyArray", 

110 dimensions=["instrument", "calibration_label"], 

111 ) 

112 newBFKernel = cT.PrerequisiteInput( 

113 name='brighterFatterKernel', 

114 doc="Newer complete kernel + gain solutions.", 

115 storageClass="BrighterFatterKernel", 

116 dimensions=["instrument", "calibration_label", "detector"], 

117 ) 

118 defects = cT.PrerequisiteInput( 

119 name='defects', 

120 doc="Input defect tables.", 

121 storageClass="Defects", 

122 dimensions=["instrument", "calibration_label", "detector"], 

123 ) 

124 opticsTransmission = cT.PrerequisiteInput( 

125 name="transmission_optics", 

126 storageClass="TransmissionCurve", 

127 doc="Transmission curve due to the optics.", 

128 dimensions=["instrument", "calibration_label"], 

129 ) 

130 filterTransmission = cT.PrerequisiteInput( 

131 name="transmission_filter", 

132 storageClass="TransmissionCurve", 

133 doc="Transmission curve due to the filter.", 

134 dimensions=["instrument", "physical_filter", "calibration_label"], 

135 ) 

136 sensorTransmission = cT.PrerequisiteInput( 

137 name="transmission_sensor", 

138 storageClass="TransmissionCurve", 

139 doc="Transmission curve due to the sensor.", 

140 dimensions=["instrument", "calibration_label", "detector"], 

141 ) 

142 atmosphereTransmission = cT.PrerequisiteInput( 

143 name="transmission_atmosphere", 

144 storageClass="TransmissionCurve", 

145 doc="Transmission curve due to the atmosphere.", 

146 dimensions=["instrument"], 

147 ) 

148 illumMaskedImage = cT.PrerequisiteInput( 

149 name="illum", 

150 doc="Input illumination correction.", 

151 storageClass="MaskedImageF", 

152 dimensions=["instrument", "physical_filter", "calibration_label", "detector"], 

153 ) 

154 

155 outputExposure = cT.Output( 

156 name='postISRCCD', 

157 doc="Output ISR processed exposure.", 

158 storageClass="ExposureF", 

159 dimensions=["instrument", "exposure", "detector"], 

160 ) 

161 preInterpExposure = cT.Output( 

162 name='preInterpISRCCD', 

163 doc="Output ISR processed exposure, with pixels left uninterpolated.", 

164 storageClass="ExposureF", 

165 dimensions=["instrument", "exposure", "detector"], 

166 ) 

167 outputOssThumbnail = cT.Output( 

168 name="OssThumb", 

169 doc="Output Overscan-subtracted thumbnail image.", 

170 storageClass="Thumbnail", 

171 dimensions=["instrument", "exposure", "detector"], 

172 ) 

173 outputFlattenedThumbnail = cT.Output( 

174 name="FlattenedThumb", 

175 doc="Output flat-corrected thumbnail image.", 

176 storageClass="Thumbnail", 

177 dimensions=["instrument", "exposure", "detector"], 

178 ) 

179 

180 def __init__(self, *, config=None): 

181 super().__init__(config=config) 

182 

183 if config.doBias is not True: 

184 self.prerequisiteInputs.discard("bias") 

185 if config.doLinearize is not True: 

186 self.prerequisiteInputs.discard("linearizer") 

187 if config.doCrosstalk is not True: 

188 self.prerequisiteInputs.discard("crosstalkSources") 

189 if config.doBrighterFatter is not True: 

190 self.prerequisiteInputs.discard("bfKernel") 

191 self.prerequisiteInputs.discard("newBFKernel") 

192 if config.doDefect is not True: 

193 self.prerequisiteInputs.discard("defects") 

194 if config.doDark is not True: 

195 self.prerequisiteInputs.discard("dark") 

196 if config.doFlat is not True: 

197 self.prerequisiteInputs.discard("flat") 

198 if config.doAttachTransmissionCurve is not True: 

199 self.prerequisiteInputs.discard("opticsTransmission") 

200 self.prerequisiteInputs.discard("filterTransmission") 

201 self.prerequisiteInputs.discard("sensorTransmission") 

202 self.prerequisiteInputs.discard("atmosphereTransmission") 

203 if config.doUseOpticsTransmission is not True: 

204 self.prerequisiteInputs.discard("opticsTransmission") 

205 if config.doUseFilterTransmission is not True: 

206 self.prerequisiteInputs.discard("filterTransmission") 

207 if config.doUseSensorTransmission is not True: 

208 self.prerequisiteInputs.discard("sensorTransmission") 

209 if config.doUseAtmosphereTransmission is not True: 

210 self.prerequisiteInputs.discard("atmosphereTransmission") 

211 if config.doIlluminationCorrection is not True: 

212 self.prerequisiteInputs.discard("illumMaskedImage") 

213 

214 if config.doWrite is not True: 

215 self.outputs.discard("outputExposure") 

216 self.outputs.discard("preInterpExposure") 

217 self.outputs.discard("outputFlattenedThumbnail") 

218 self.outputs.discard("outputOssThumbnail") 

219 if config.doSaveInterpPixels is not True: 

220 self.outputs.discard("preInterpExposure") 

221 if config.qa.doThumbnailOss is not True: 

222 self.outputs.discard("outputOssThumbnail") 

223 if config.qa.doThumbnailFlattened is not True: 

224 self.outputs.discard("outputFlattenedThumbnail") 

225 

226 

227class IsrTaskConfig(pipeBase.PipelineTaskConfig, 

228 pipelineConnections=IsrTaskConnections): 

229 """Configuration parameters for IsrTask. 

230 

231 Items are grouped in the order in which they are executed by the task. 

232 """ 

233 datasetType = pexConfig.Field( 

234 dtype=str, 

235 doc="Dataset type for input data; users will typically leave this alone, " 

236 "but camera-specific ISR tasks will override it", 

237 default="raw", 

238 ) 

239 

240 fallbackFilterName = pexConfig.Field( 

241 dtype=str, 

242 doc="Fallback default filter name for calibrations.", 

243 optional=True 

244 ) 

245 useFallbackDate = pexConfig.Field( 

246 dtype=bool, 

247 doc="Pass observation date when using fallback filter.", 

248 default=False, 

249 ) 

250 expectWcs = pexConfig.Field( 

251 dtype=bool, 

252 default=True, 

253 doc="Expect input science images to have a WCS (set False for e.g. spectrographs)." 

254 ) 

255 fwhm = pexConfig.Field( 

256 dtype=float, 

257 doc="FWHM of PSF in arcseconds.", 

258 default=1.0, 

259 ) 

260 qa = pexConfig.ConfigField( 

261 dtype=isrQa.IsrQaConfig, 

262 doc="QA related configuration options.", 

263 ) 

264 

265 # Image conversion configuration 

266 doConvertIntToFloat = pexConfig.Field( 

267 dtype=bool, 

268 doc="Convert integer raw images to floating point values?", 

269 default=True, 

270 ) 

271 

272 # Saturated pixel handling. 

273 doSaturation = pexConfig.Field( 

274 dtype=bool, 

275 doc="Mask saturated pixels? NB: this is totally independent of the" 

276 " interpolation option - this is ONLY setting the bits in the mask." 

277 " To have them interpolated make sure doSaturationInterpolation=True", 

278 default=True, 

279 ) 

280 saturatedMaskName = pexConfig.Field( 

281 dtype=str, 

282 doc="Name of mask plane to use in saturation detection and interpolation", 

283 default="SAT", 

284 ) 

285 saturation = pexConfig.Field( 

286 dtype=float, 

287 doc="The saturation level to use if no Detector is present in the Exposure (ignored if NaN)", 

288 default=float("NaN"), 

289 ) 

290 growSaturationFootprintSize = pexConfig.Field( 

291 dtype=int, 

292 doc="Number of pixels by which to grow the saturation footprints", 

293 default=1, 

294 ) 

295 

296 # Suspect pixel handling. 

297 doSuspect = pexConfig.Field( 

298 dtype=bool, 

299 doc="Mask suspect pixels?", 

300 default=False, 

301 ) 

302 suspectMaskName = pexConfig.Field( 

303 dtype=str, 

304 doc="Name of mask plane to use for suspect pixels", 

305 default="SUSPECT", 

306 ) 

307 numEdgeSuspect = pexConfig.Field( 

308 dtype=int, 

309 doc="Number of edge pixels to be flagged as untrustworthy.", 

310 default=0, 

311 ) 

312 

313 # Initial masking options. 

314 doSetBadRegions = pexConfig.Field( 

315 dtype=bool, 

316 doc="Should we set the level of all BAD patches of the chip to the chip's average value?", 

317 default=True, 

318 ) 

319 badStatistic = pexConfig.ChoiceField( 

320 dtype=str, 

321 doc="How to estimate the average value for BAD regions.", 

322 default='MEANCLIP', 

323 allowed={ 

324 "MEANCLIP": "Correct using the (clipped) mean of good data", 

325 "MEDIAN": "Correct using the median of the good data", 

326 }, 

327 ) 

328 

329 # Overscan subtraction configuration. 

330 doOverscan = pexConfig.Field( 

331 dtype=bool, 

332 doc="Do overscan subtraction?", 

333 default=True, 

334 ) 

335 overscan = pexConfig.ConfigurableField( 

336 target=OverscanCorrectionTask, 

337 doc="Overscan subtraction task for image segments.", 

338 ) 

339 

340 overscanFitType = pexConfig.ChoiceField( 

341 dtype=str, 

342 doc="The method for fitting the overscan bias level.", 

343 default='MEDIAN', 

344 allowed={ 

345 "POLY": "Fit ordinary polynomial to the longest axis of the overscan region", 

346 "CHEB": "Fit Chebyshev polynomial to the longest axis of the overscan region", 

347 "LEG": "Fit Legendre polynomial to the longest axis of the overscan region", 

348 "NATURAL_SPLINE": "Fit natural spline to the longest axis of the overscan region", 

349 "CUBIC_SPLINE": "Fit cubic spline to the longest axis of the overscan region", 

350 "AKIMA_SPLINE": "Fit Akima spline to the longest axis of the overscan region", 

351 "MEAN": "Correct using the mean of the overscan region", 

352 "MEANCLIP": "Correct using a clipped mean of the overscan region", 

353 "MEDIAN": "Correct using the median of the overscan region", 

354 "MEDIAN_PER_ROW": "Correct using the median per row of the overscan region", 

355 }, 

356 deprecated=("Please configure overscan via the OverscanCorrectionConfig interface." + 

357 " This option will no longer be used, and will be removed after v20.") 

358 ) 

359 overscanOrder = pexConfig.Field( 

360 dtype=int, 

361 doc=("Order of polynomial or to fit if overscan fit type is a polynomial, " + 

362 "or number of spline knots if overscan fit type is a spline."), 

363 default=1, 

364 deprecated=("Please configure overscan via the OverscanCorrectionConfig interface." + 

365 " This option will no longer be used, and will be removed after v20.") 

366 ) 

367 overscanNumSigmaClip = pexConfig.Field( 

368 dtype=float, 

369 doc="Rejection threshold (sigma) for collapsing overscan before fit", 

370 default=3.0, 

371 deprecated=("Please configure overscan via the OverscanCorrectionConfig interface." + 

372 " This option will no longer be used, and will be removed after v20.") 

373 ) 

374 overscanIsInt = pexConfig.Field( 

375 dtype=bool, 

376 doc="Treat overscan as an integer image for purposes of overscan.FitType=MEDIAN" + 

377 " and overscan.FitType=MEDIAN_PER_ROW.", 

378 default=True, 

379 deprecated=("Please configure overscan via the OverscanCorrectionConfig interface." + 

380 " This option will no longer be used, and will be removed after v20.") 

381 ) 

382 # These options do not get deprecated, as they define how we slice up the image data. 

383 overscanNumLeadingColumnsToSkip = pexConfig.Field( 

384 dtype=int, 

385 doc="Number of columns to skip in overscan, i.e. those closest to amplifier", 

386 default=0, 

387 ) 

388 overscanNumTrailingColumnsToSkip = pexConfig.Field( 

389 dtype=int, 

390 doc="Number of columns to skip in overscan, i.e. those farthest from amplifier", 

391 default=0, 

392 ) 

393 overscanMaxDev = pexConfig.Field( 393 ↛ exitline 393 didn't jump to the function exit

394 dtype=float, 

395 doc="Maximum deviation from the median for overscan", 

396 default=1000.0, check=lambda x: x > 0 

397 ) 

398 overscanBiasJump = pexConfig.Field( 

399 dtype=bool, 

400 doc="Fit the overscan in a piecewise-fashion to correct for bias jumps?", 

401 default=False, 

402 ) 

403 overscanBiasJumpKeyword = pexConfig.Field( 

404 dtype=str, 

405 doc="Header keyword containing information about devices.", 

406 default="NO_SUCH_KEY", 

407 ) 

408 overscanBiasJumpDevices = pexConfig.ListField( 

409 dtype=str, 

410 doc="List of devices that need piecewise overscan correction.", 

411 default=(), 

412 ) 

413 overscanBiasJumpLocation = pexConfig.Field( 

414 dtype=int, 

415 doc="Location of bias jump along y-axis.", 

416 default=0, 

417 ) 

418 

419 # Amplifier to CCD assembly configuration 

420 doAssembleCcd = pexConfig.Field( 

421 dtype=bool, 

422 default=True, 

423 doc="Assemble amp-level exposures into a ccd-level exposure?" 

424 ) 

425 assembleCcd = pexConfig.ConfigurableField( 

426 target=AssembleCcdTask, 

427 doc="CCD assembly task", 

428 ) 

429 

430 # General calibration configuration. 

431 doAssembleIsrExposures = pexConfig.Field( 

432 dtype=bool, 

433 default=False, 

434 doc="Assemble amp-level calibration exposures into ccd-level exposure?" 

435 ) 

436 doTrimToMatchCalib = pexConfig.Field( 

437 dtype=bool, 

438 default=False, 

439 doc="Trim raw data to match calibration bounding boxes?" 

440 ) 

441 

442 # Bias subtraction. 

443 doBias = pexConfig.Field( 

444 dtype=bool, 

445 doc="Apply bias frame correction?", 

446 default=True, 

447 ) 

448 biasDataProductName = pexConfig.Field( 

449 dtype=str, 

450 doc="Name of the bias data product", 

451 default="bias", 

452 ) 

453 

454 # Variance construction 

455 doVariance = pexConfig.Field( 

456 dtype=bool, 

457 doc="Calculate variance?", 

458 default=True 

459 ) 

460 gain = pexConfig.Field( 

461 dtype=float, 

462 doc="The gain to use if no Detector is present in the Exposure (ignored if NaN)", 

463 default=float("NaN"), 

464 ) 

465 readNoise = pexConfig.Field( 

466 dtype=float, 

467 doc="The read noise to use if no Detector is present in the Exposure", 

468 default=0.0, 

469 ) 

470 doEmpiricalReadNoise = pexConfig.Field( 

471 dtype=bool, 

472 default=False, 

473 doc="Calculate empirical read noise instead of value from AmpInfo data?" 

474 ) 

475 

476 # Linearization. 

477 doLinearize = pexConfig.Field( 

478 dtype=bool, 

479 doc="Correct for nonlinearity of the detector's response?", 

480 default=True, 

481 ) 

482 

483 # Crosstalk. 

484 doCrosstalk = pexConfig.Field( 

485 dtype=bool, 

486 doc="Apply intra-CCD crosstalk correction?", 

487 default=False, 

488 ) 

489 doCrosstalkBeforeAssemble = pexConfig.Field( 

490 dtype=bool, 

491 doc="Apply crosstalk correction before CCD assembly, and before trimming?", 

492 default=False, 

493 ) 

494 crosstalk = pexConfig.ConfigurableField( 

495 target=CrosstalkTask, 

496 doc="Intra-CCD crosstalk correction", 

497 ) 

498 

499 # Masking options. 

500 doDefect = pexConfig.Field( 

501 dtype=bool, 

502 doc="Apply correction for CCD defects, e.g. hot pixels?", 

503 default=True, 

504 ) 

505 doNanMasking = pexConfig.Field( 

506 dtype=bool, 

507 doc="Mask NAN pixels?", 

508 default=True, 

509 ) 

510 doWidenSaturationTrails = pexConfig.Field( 

511 dtype=bool, 

512 doc="Widen bleed trails based on their width?", 

513 default=True 

514 ) 

515 

516 # Brighter-Fatter correction. 

517 doBrighterFatter = pexConfig.Field( 

518 dtype=bool, 

519 default=False, 

520 doc="Apply the brighter fatter correction" 

521 ) 

522 brighterFatterLevel = pexConfig.ChoiceField( 

523 dtype=str, 

524 default="DETECTOR", 

525 doc="The level at which to correct for brighter-fatter.", 

526 allowed={ 

527 "AMP": "Every amplifier treated separately.", 

528 "DETECTOR": "One kernel per detector", 

529 } 

530 ) 

531 brighterFatterMaxIter = pexConfig.Field( 

532 dtype=int, 

533 default=10, 

534 doc="Maximum number of iterations for the brighter fatter correction" 

535 ) 

536 brighterFatterThreshold = pexConfig.Field( 

537 dtype=float, 

538 default=1000, 

539 doc="Threshold used to stop iterating the brighter fatter correction. It is the " 

540 " absolute value of the difference between the current corrected image and the one" 

541 " from the previous iteration summed over all the pixels." 

542 ) 

543 brighterFatterApplyGain = pexConfig.Field( 

544 dtype=bool, 

545 default=True, 

546 doc="Should the gain be applied when applying the brighter fatter correction?" 

547 ) 

548 brighterFatterMaskGrowSize = pexConfig.Field( 

549 dtype=int, 

550 default=0, 

551 doc="Number of pixels to grow the masks listed in config.maskListToInterpolate " 

552 " when brighter-fatter correction is applied." 

553 ) 

554 

555 # Dark subtraction. 

556 doDark = pexConfig.Field( 

557 dtype=bool, 

558 doc="Apply dark frame correction?", 

559 default=True, 

560 ) 

561 darkDataProductName = pexConfig.Field( 

562 dtype=str, 

563 doc="Name of the dark data product", 

564 default="dark", 

565 ) 

566 

567 # Camera-specific stray light removal. 

568 doStrayLight = pexConfig.Field( 

569 dtype=bool, 

570 doc="Subtract stray light in the y-band (due to encoder LEDs)?", 

571 default=False, 

572 ) 

573 strayLight = pexConfig.ConfigurableField( 

574 target=StrayLightTask, 

575 doc="y-band stray light correction" 

576 ) 

577 

578 # Flat correction. 

579 doFlat = pexConfig.Field( 

580 dtype=bool, 

581 doc="Apply flat field correction?", 

582 default=True, 

583 ) 

584 flatDataProductName = pexConfig.Field( 

585 dtype=str, 

586 doc="Name of the flat data product", 

587 default="flat", 

588 ) 

589 flatScalingType = pexConfig.ChoiceField( 

590 dtype=str, 

591 doc="The method for scaling the flat on the fly.", 

592 default='USER', 

593 allowed={ 

594 "USER": "Scale by flatUserScale", 

595 "MEAN": "Scale by the inverse of the mean", 

596 "MEDIAN": "Scale by the inverse of the median", 

597 }, 

598 ) 

599 flatUserScale = pexConfig.Field( 

600 dtype=float, 

601 doc="If flatScalingType is 'USER' then scale flat by this amount; ignored otherwise", 

602 default=1.0, 

603 ) 

604 doTweakFlat = pexConfig.Field( 

605 dtype=bool, 

606 doc="Tweak flats to match observed amplifier ratios?", 

607 default=False 

608 ) 

609 

610 # Amplifier normalization based on gains instead of using flats configuration. 

611 doApplyGains = pexConfig.Field( 

612 dtype=bool, 

613 doc="Correct the amplifiers for their gains instead of applying flat correction", 

614 default=False, 

615 ) 

616 normalizeGains = pexConfig.Field( 

617 dtype=bool, 

618 doc="Normalize all the amplifiers in each CCD to have the same median value.", 

619 default=False, 

620 ) 

621 

622 # Fringe correction. 

623 doFringe = pexConfig.Field( 

624 dtype=bool, 

625 doc="Apply fringe correction?", 

626 default=True, 

627 ) 

628 fringe = pexConfig.ConfigurableField( 

629 target=FringeTask, 

630 doc="Fringe subtraction task", 

631 ) 

632 fringeAfterFlat = pexConfig.Field( 

633 dtype=bool, 

634 doc="Do fringe subtraction after flat-fielding?", 

635 default=True, 

636 ) 

637 

638 # Initial CCD-level background statistics options. 

639 doMeasureBackground = pexConfig.Field( 

640 dtype=bool, 

641 doc="Measure the background level on the reduced image?", 

642 default=False, 

643 ) 

644 

645 # Camera-specific masking configuration. 

646 doCameraSpecificMasking = pexConfig.Field( 

647 dtype=bool, 

648 doc="Mask camera-specific bad regions?", 

649 default=False, 

650 ) 

651 masking = pexConfig.ConfigurableField( 

652 target=MaskingTask, 

653 doc="Masking task." 

654 ) 

655 

656 # Interpolation options. 

657 

658 doInterpolate = pexConfig.Field( 

659 dtype=bool, 

660 doc="Interpolate masked pixels?", 

661 default=True, 

662 ) 

663 doSaturationInterpolation = pexConfig.Field( 

664 dtype=bool, 

665 doc="Perform interpolation over pixels masked as saturated?" 

666 " NB: This is independent of doSaturation; if that is False this plane" 

667 " will likely be blank, resulting in a no-op here.", 

668 default=True, 

669 ) 

670 doNanInterpolation = pexConfig.Field( 

671 dtype=bool, 

672 doc="Perform interpolation over pixels masked as NaN?" 

673 " NB: This is independent of doNanMasking; if that is False this plane" 

674 " will likely be blank, resulting in a no-op here.", 

675 default=True, 

676 ) 

677 doNanInterpAfterFlat = pexConfig.Field( 

678 dtype=bool, 

679 doc=("If True, ensure we interpolate NaNs after flat-fielding, even if we " 

680 "also have to interpolate them before flat-fielding."), 

681 default=False, 

682 ) 

683 maskListToInterpolate = pexConfig.ListField( 

684 dtype=str, 

685 doc="List of mask planes that should be interpolated.", 

686 default=['SAT', 'BAD', 'UNMASKEDNAN'], 

687 ) 

688 doSaveInterpPixels = pexConfig.Field( 

689 dtype=bool, 

690 doc="Save a copy of the pre-interpolated pixel values?", 

691 default=False, 

692 ) 

693 

694 # Default photometric calibration options. 

695 fluxMag0T1 = pexConfig.DictField( 

696 keytype=str, 

697 itemtype=float, 

698 doc="The approximate flux of a zero-magnitude object in a one-second exposure, per filter.", 

699 default=dict((f, pow(10.0, 0.4*m)) for f, m in (("Unknown", 28.0), 

700 )) 

701 ) 

702 defaultFluxMag0T1 = pexConfig.Field( 

703 dtype=float, 

704 doc="Default value for fluxMag0T1 (for an unrecognized filter).", 

705 default=pow(10.0, 0.4*28.0) 

706 ) 

707 

708 # Vignette correction configuration. 

709 doVignette = pexConfig.Field( 

710 dtype=bool, 

711 doc="Apply vignetting parameters?", 

712 default=False, 

713 ) 

714 vignette = pexConfig.ConfigurableField( 

715 target=VignetteTask, 

716 doc="Vignetting task.", 

717 ) 

718 

719 # Transmission curve configuration. 

720 doAttachTransmissionCurve = pexConfig.Field( 

721 dtype=bool, 

722 default=False, 

723 doc="Construct and attach a wavelength-dependent throughput curve for this CCD image?" 

724 ) 

725 doUseOpticsTransmission = pexConfig.Field( 

726 dtype=bool, 

727 default=True, 

728 doc="Load and use transmission_optics (if doAttachTransmissionCurve is True)?" 

729 ) 

730 doUseFilterTransmission = pexConfig.Field( 

731 dtype=bool, 

732 default=True, 

733 doc="Load and use transmission_filter (if doAttachTransmissionCurve is True)?" 

734 ) 

735 doUseSensorTransmission = pexConfig.Field( 

736 dtype=bool, 

737 default=True, 

738 doc="Load and use transmission_sensor (if doAttachTransmissionCurve is True)?" 

739 ) 

740 doUseAtmosphereTransmission = pexConfig.Field( 

741 dtype=bool, 

742 default=True, 

743 doc="Load and use transmission_atmosphere (if doAttachTransmissionCurve is True)?" 

744 ) 

745 

746 # Illumination correction. 

747 doIlluminationCorrection = pexConfig.Field( 

748 dtype=bool, 

749 default=False, 

750 doc="Perform illumination correction?" 

751 ) 

752 illuminationCorrectionDataProductName = pexConfig.Field( 

753 dtype=str, 

754 doc="Name of the illumination correction data product.", 

755 default="illumcor", 

756 ) 

757 illumScale = pexConfig.Field( 

758 dtype=float, 

759 doc="Scale factor for the illumination correction.", 

760 default=1.0, 

761 ) 

762 illumFilters = pexConfig.ListField( 

763 dtype=str, 

764 default=[], 

765 doc="Only perform illumination correction for these filters." 

766 ) 

767 

768 # Write the outputs to disk. If ISR is run as a subtask, this may not be needed. 

769 doWrite = pexConfig.Field( 

770 dtype=bool, 

771 doc="Persist postISRCCD?", 

772 default=True, 

773 ) 

774 

775 def validate(self): 

776 super().validate() 

777 if self.doFlat and self.doApplyGains: 

778 raise ValueError("You may not specify both doFlat and doApplyGains") 

779 if self.doSaturationInterpolation and "SAT" not in self.maskListToInterpolate: 

780 self.config.maskListToInterpolate.append("SAT") 

781 if self.doNanInterpolation and "UNMASKEDNAN" not in self.maskListToInterpolate: 

782 self.config.maskListToInterpolate.append("UNMASKEDNAN") 

783 

784 

785class IsrTask(pipeBase.PipelineTask, pipeBase.CmdLineTask): 

786 """Apply common instrument signature correction algorithms to a raw frame. 

787 

788 The process for correcting imaging data is very similar from 

789 camera to camera. This task provides a vanilla implementation of 

790 doing these corrections, including the ability to turn certain 

791 corrections off if they are not needed. The inputs to the primary 

792 method, `run()`, are a raw exposure to be corrected and the 

793 calibration data products. The raw input is a single chip sized 

794 mosaic of all amps including overscans and other non-science 

795 pixels. The method `runDataRef()` identifies and defines the 

796 calibration data products, and is intended for use by a 

797 `lsst.pipe.base.cmdLineTask.CmdLineTask` and takes as input only a 

798 `daf.persistence.butlerSubset.ButlerDataRef`. This task may be 

799 subclassed for different camera, although the most camera specific 

800 methods have been split into subtasks that can be redirected 

801 appropriately. 

802 

803 The __init__ method sets up the subtasks for ISR processing, using 

804 the defaults from `lsst.ip.isr`. 

805 

806 Parameters 

807 ---------- 

808 args : `list` 

809 Positional arguments passed to the Task constructor. None used at this time. 

810 kwargs : `dict`, optional 

811 Keyword arguments passed on to the Task constructor. None used at this time. 

812 """ 

813 ConfigClass = IsrTaskConfig 

814 _DefaultName = "isr" 

815 

816 def __init__(self, **kwargs): 

817 super().__init__(**kwargs) 

818 self.makeSubtask("assembleCcd") 

819 self.makeSubtask("crosstalk") 

820 self.makeSubtask("strayLight") 

821 self.makeSubtask("fringe") 

822 self.makeSubtask("masking") 

823 self.makeSubtask("overscan") 

824 self.makeSubtask("vignette") 

825 

826 def runQuantum(self, butlerQC, inputRefs, outputRefs): 

827 inputs = butlerQC.get(inputRefs) 

828 

829 try: 

830 inputs['detectorNum'] = inputRefs.ccdExposure.dataId['detector'] 

831 except Exception as e: 

832 raise ValueError("Failure to find valid detectorNum value for Dataset %s: %s." % 

833 (inputRefs, e)) 

834 

835 inputs['isGen3'] = True 

836 

837 detector = inputs['ccdExposure'].getDetector() 

838 

839 if self.doLinearize(detector) is True: 

840 if 'linearizer' in inputs and isinstance(inputs['linearizer'], dict): 

841 linearizer = linearize.Linearizer(detector=detector, log=self.log) 

842 linearizer.fromYaml(inputs['linearizer']) 

843 else: 

844 linearizer = linearize.Linearizer(table=inputs.get('linearizer', None), detector=detector, 

845 log=self.log) 

846 inputs['linearizer'] = linearizer 

847 

848 if self.config.doDefect is True: 

849 if "defects" in inputs and inputs['defects'] is not None: 

850 # defects is loaded as a BaseCatalog with columns x0, y0, width, height. 

851 # masking expects a list of defects defined by their bounding box 

852 if not isinstance(inputs["defects"], Defects): 

853 inputs["defects"] = Defects.fromTable(inputs["defects"]) 

854 

855 # Load the correct style of brighter fatter kernel, and repack 

856 # the information as a numpy array. 

857 if self.config.doBrighterFatter: 

858 brighterFatterKernel = inputs.pop('newBFKernel', None) 

859 if brighterFatterKernel is None: 

860 brighterFatterKernel = inputs.get('bfKernel', None) 

861 

862 if brighterFatterKernel is not None and not isinstance(brighterFatterKernel, numpy.ndarray): 

863 detId = detector.getId() 

864 inputs['bfGains'] = brighterFatterKernel.gain 

865 # If the kernel is not an ndarray, it's the cp_pipe version 

866 # so extract the kernel for this detector, or raise an error 

867 if self.config.brighterFatterLevel == 'DETECTOR': 

868 if brighterFatterKernel.detectorKernel: 

869 inputs['bfKernel'] = brighterFatterKernel.detectorKernel[detId] 

870 elif brighterFatterKernel.detectorKernelFromAmpKernels: 

871 inputs['bfKernel'] = brighterFatterKernel.detectorKernelFromAmpKernels[detId] 

872 else: 

873 raise RuntimeError("Failed to extract kernel from new-style BF kernel.") 

874 else: 

875 # TODO DM-15631 for implementing this 

876 raise NotImplementedError("Per-amplifier brighter-fatter correction not implemented") 

877 

878 # Broken: DM-17169 

879 # ci_hsc does not use crosstalkSources, as it's intra-CCD CT only. This needs to be 

880 # fixed for non-HSC cameras in the future. 

881 # inputs['crosstalkSources'] = (self.crosstalk.prepCrosstalk(inputsIds['ccdExposure']) 

882 # if self.config.doCrosstalk else None) 

883 

884 if self.config.doFringe is True and self.fringe.checkFilter(inputs['ccdExposure']): 

885 expId = inputs['ccdExposure'].getInfo().getVisitInfo().getExposureId() 

886 inputs['fringes'] = self.fringe.loadFringes(inputs['fringes'], 

887 expId=expId, 

888 assembler=self.assembleCcd 

889 if self.config.doAssembleIsrExposures else None) 

890 else: 

891 inputs['fringes'] = pipeBase.Struct(fringes=None) 

892 

893 if self.config.doStrayLight is True and self.strayLight.checkFilter(inputs['ccdExposure']): 

894 if 'strayLightData' not in inputs: 

895 inputs['strayLightData'] = None 

896 

897 outputs = self.run(**inputs) 

898 butlerQC.put(outputs, outputRefs) 

899 

900 def readIsrData(self, dataRef, rawExposure): 

901 """!Retrieve necessary frames for instrument signature removal. 

902 

903 Pre-fetching all required ISR data products limits the IO 

904 required by the ISR. Any conflict between the calibration data 

905 available and that needed for ISR is also detected prior to 

906 doing processing, allowing it to fail quickly. 

907 

908 Parameters 

909 ---------- 

910 dataRef : `daf.persistence.butlerSubset.ButlerDataRef` 

911 Butler reference of the detector data to be processed 

912 rawExposure : `afw.image.Exposure` 

913 The raw exposure that will later be corrected with the 

914 retrieved calibration data; should not be modified in this 

915 method. 

916 

917 Returns 

918 ------- 

919 result : `lsst.pipe.base.Struct` 

920 Result struct with components (which may be `None`): 

921 - ``bias``: bias calibration frame (`afw.image.Exposure`) 

922 - ``linearizer``: functor for linearization (`ip.isr.linearize.LinearizeBase`) 

923 - ``crosstalkSources``: list of possible crosstalk sources (`list`) 

924 - ``dark``: dark calibration frame (`afw.image.Exposure`) 

925 - ``flat``: flat calibration frame (`afw.image.Exposure`) 

926 - ``bfKernel``: Brighter-Fatter kernel (`numpy.ndarray`) 

927 - ``defects``: list of defects (`lsst.meas.algorithms.Defects`) 

928 - ``fringes``: `lsst.pipe.base.Struct` with components: 

929 - ``fringes``: fringe calibration frame (`afw.image.Exposure`) 

930 - ``seed``: random seed derived from the ccdExposureId for random 

931 number generator (`uint32`). 

932 - ``opticsTransmission``: `lsst.afw.image.TransmissionCurve` 

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

934 to be evaluated in focal-plane coordinates. 

935 - ``filterTransmission`` : `lsst.afw.image.TransmissionCurve` 

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

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

938 - ``sensorTransmission`` : `lsst.afw.image.TransmissionCurve` 

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

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

941 - ``atmosphereTransmission`` : `lsst.afw.image.TransmissionCurve` 

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

943 atmosphere, assumed to be spatially constant. 

944 - ``strayLightData`` : `object` 

945 An opaque object containing calibration information for 

946 stray-light correction. If `None`, no correction will be 

947 performed. 

948 - ``illumMaskedImage`` : illumination correction image (`lsst.afw.image.MaskedImage`) 

949 

950 Raises 

951 ------ 

952 NotImplementedError : 

953 Raised if a per-amplifier brighter-fatter kernel is requested by the configuration. 

954 """ 

955 try: 

956 dateObs = rawExposure.getInfo().getVisitInfo().getDate() 

957 dateObs = dateObs.toPython().isoformat() 

958 except RuntimeError: 

959 self.log.warn("Unable to identify dateObs for rawExposure.") 

960 dateObs = None 

961 

962 ccd = rawExposure.getDetector() 

963 filterName = afwImage.Filter(rawExposure.getFilter().getId()).getName() # Canonical name for filter 

964 rawExposure.mask.addMaskPlane("UNMASKEDNAN") # needed to match pre DM-15862 processing. 

965 biasExposure = (self.getIsrExposure(dataRef, self.config.biasDataProductName) 

966 if self.config.doBias else None) 

967 # immediate=True required for functors and linearizers are functors; see ticket DM-6515 

968 linearizer = (dataRef.get("linearizer", immediate=True) 

969 if self.doLinearize(ccd) else None) 

970 if linearizer is not None and not isinstance(linearizer, numpy.ndarray): 

971 linearizer.log = self.log 

972 if isinstance(linearizer, numpy.ndarray): 

973 linearizer = linearize.Linearizer(table=linearizer, detector=ccd) 

974 crosstalkSources = (self.crosstalk.prepCrosstalk(dataRef) 

975 if self.config.doCrosstalk else None) 

976 darkExposure = (self.getIsrExposure(dataRef, self.config.darkDataProductName) 

977 if self.config.doDark else None) 

978 flatExposure = (self.getIsrExposure(dataRef, self.config.flatDataProductName, 

979 dateObs=dateObs) 

980 if self.config.doFlat else None) 

981 

982 brighterFatterKernel = None 

983 brighterFatterGains = None 

984 if self.config.doBrighterFatter is True: 

985 try: 

986 # Use the new-style cp_pipe version of the kernel if it exists 

987 # If using a new-style kernel, always use the self-consistent 

988 # gains, i.e. the ones inside the kernel object itself 

989 brighterFatterKernel = dataRef.get("brighterFatterKernel") 

990 brighterFatterGains = brighterFatterKernel.gain 

991 self.log.info("New style bright-fatter kernel (brighterFatterKernel) loaded") 

992 except NoResults: 

993 try: # Fall back to the old-style numpy-ndarray style kernel if necessary. 

994 brighterFatterKernel = dataRef.get("bfKernel") 

995 self.log.info("Old style bright-fatter kernel (np.array) loaded") 

996 except NoResults: 

997 brighterFatterKernel = None 

998 if brighterFatterKernel is not None and not isinstance(brighterFatterKernel, numpy.ndarray): 

999 # If the kernel is not an ndarray, it's the cp_pipe version 

1000 # so extract the kernel for this detector, or raise an error 

1001 if self.config.brighterFatterLevel == 'DETECTOR': 

1002 if brighterFatterKernel.detectorKernel: 

1003 brighterFatterKernel = brighterFatterKernel.detectorKernel[ccd.getId()] 

1004 elif brighterFatterKernel.detectorKernelFromAmpKernels: 

1005 brighterFatterKernel = brighterFatterKernel.detectorKernelFromAmpKernels[ccd.getId()] 

1006 else: 

1007 raise RuntimeError("Failed to extract kernel from new-style BF kernel.") 

1008 else: 

1009 # TODO DM-15631 for implementing this 

1010 raise NotImplementedError("Per-amplifier brighter-fatter correction not implemented") 

1011 

1012 defectList = (dataRef.get("defects") 

1013 if self.config.doDefect else None) 

1014 fringeStruct = (self.fringe.readFringes(dataRef, assembler=self.assembleCcd 

1015 if self.config.doAssembleIsrExposures else None) 

1016 if self.config.doFringe and self.fringe.checkFilter(rawExposure) 

1017 else pipeBase.Struct(fringes=None)) 

1018 

1019 if self.config.doAttachTransmissionCurve: 

1020 opticsTransmission = (dataRef.get("transmission_optics") 

1021 if self.config.doUseOpticsTransmission else None) 

1022 filterTransmission = (dataRef.get("transmission_filter") 

1023 if self.config.doUseFilterTransmission else None) 

1024 sensorTransmission = (dataRef.get("transmission_sensor") 

1025 if self.config.doUseSensorTransmission else None) 

1026 atmosphereTransmission = (dataRef.get("transmission_atmosphere") 

1027 if self.config.doUseAtmosphereTransmission else None) 

1028 else: 

1029 opticsTransmission = None 

1030 filterTransmission = None 

1031 sensorTransmission = None 

1032 atmosphereTransmission = None 

1033 

1034 if self.config.doStrayLight: 

1035 strayLightData = self.strayLight.readIsrData(dataRef, rawExposure) 

1036 else: 

1037 strayLightData = None 

1038 

1039 illumMaskedImage = (self.getIsrExposure(dataRef, 

1040 self.config.illuminationCorrectionDataProductName).getMaskedImage() 

1041 if (self.config.doIlluminationCorrection and 

1042 filterName in self.config.illumFilters) 

1043 else None) 

1044 

1045 # Struct should include only kwargs to run() 

1046 return pipeBase.Struct(bias=biasExposure, 

1047 linearizer=linearizer, 

1048 crosstalkSources=crosstalkSources, 

1049 dark=darkExposure, 

1050 flat=flatExposure, 

1051 bfKernel=brighterFatterKernel, 

1052 bfGains=brighterFatterGains, 

1053 defects=defectList, 

1054 fringes=fringeStruct, 

1055 opticsTransmission=opticsTransmission, 

1056 filterTransmission=filterTransmission, 

1057 sensorTransmission=sensorTransmission, 

1058 atmosphereTransmission=atmosphereTransmission, 

1059 strayLightData=strayLightData, 

1060 illumMaskedImage=illumMaskedImage 

1061 ) 

1062 

1063 @pipeBase.timeMethod 

1064 def run(self, ccdExposure, camera=None, bias=None, linearizer=None, crosstalkSources=None, 

1065 dark=None, flat=None, bfKernel=None, bfGains=None, defects=None, 

1066 fringes=pipeBase.Struct(fringes=None), opticsTransmission=None, filterTransmission=None, 

1067 sensorTransmission=None, atmosphereTransmission=None, 

1068 detectorNum=None, strayLightData=None, illumMaskedImage=None, 

1069 isGen3=False, 

1070 ): 

1071 """!Perform instrument signature removal on an exposure. 

1072 

1073 Steps included in the ISR processing, in order performed, are: 

1074 - saturation and suspect pixel masking 

1075 - overscan subtraction 

1076 - CCD assembly of individual amplifiers 

1077 - bias subtraction 

1078 - variance image construction 

1079 - linearization of non-linear response 

1080 - crosstalk masking 

1081 - brighter-fatter correction 

1082 - dark subtraction 

1083 - fringe correction 

1084 - stray light subtraction 

1085 - flat correction 

1086 - masking of known defects and camera specific features 

1087 - vignette calculation 

1088 - appending transmission curve and distortion model 

1089 

1090 Parameters 

1091 ---------- 

1092 ccdExposure : `lsst.afw.image.Exposure` 

1093 The raw exposure that is to be run through ISR. The 

1094 exposure is modified by this method. 

1095 camera : `lsst.afw.cameraGeom.Camera`, optional 

1096 The camera geometry for this exposure. Required if ``isGen3`` is 

1097 `True` and one or more of ``ccdExposure``, ``bias``, ``dark``, or 

1098 ``flat`` does not have an associated detector. 

1099 bias : `lsst.afw.image.Exposure`, optional 

1100 Bias calibration frame. 

1101 linearizer : `lsst.ip.isr.linearize.LinearizeBase`, optional 

1102 Functor for linearization. 

1103 crosstalkSources : `list`, optional 

1104 List of possible crosstalk sources. 

1105 dark : `lsst.afw.image.Exposure`, optional 

1106 Dark calibration frame. 

1107 flat : `lsst.afw.image.Exposure`, optional 

1108 Flat calibration frame. 

1109 bfKernel : `numpy.ndarray`, optional 

1110 Brighter-fatter kernel. 

1111 bfGains : `dict` of `float`, optional 

1112 Gains used to override the detector's nominal gains for the 

1113 brighter-fatter correction. A dict keyed by amplifier name for 

1114 the detector in question. 

1115 defects : `lsst.meas.algorithms.Defects`, optional 

1116 List of defects. 

1117 fringes : `lsst.pipe.base.Struct`, optional 

1118 Struct containing the fringe correction data, with 

1119 elements: 

1120 - ``fringes``: fringe calibration frame (`afw.image.Exposure`) 

1121 - ``seed``: random seed derived from the ccdExposureId for random 

1122 number generator (`uint32`) 

1123 opticsTransmission: `lsst.afw.image.TransmissionCurve`, optional 

1124 A ``TransmissionCurve`` that represents the throughput of the optics, 

1125 to be evaluated in focal-plane coordinates. 

1126 filterTransmission : `lsst.afw.image.TransmissionCurve` 

1127 A ``TransmissionCurve`` that represents the throughput of the filter 

1128 itself, to be evaluated in focal-plane coordinates. 

1129 sensorTransmission : `lsst.afw.image.TransmissionCurve` 

1130 A ``TransmissionCurve`` that represents the throughput of the sensor 

1131 itself, to be evaluated in post-assembly trimmed detector coordinates. 

1132 atmosphereTransmission : `lsst.afw.image.TransmissionCurve` 

1133 A ``TransmissionCurve`` that represents the throughput of the 

1134 atmosphere, assumed to be spatially constant. 

1135 detectorNum : `int`, optional 

1136 The integer number for the detector to process. 

1137 isGen3 : bool, optional 

1138 Flag this call to run() as using the Gen3 butler environment. 

1139 strayLightData : `object`, optional 

1140 Opaque object containing calibration information for stray-light 

1141 correction. If `None`, no correction will be performed. 

1142 illumMaskedImage : `lsst.afw.image.MaskedImage`, optional 

1143 Illumination correction image. 

1144 

1145 Returns 

1146 ------- 

1147 result : `lsst.pipe.base.Struct` 

1148 Result struct with component: 

1149 - ``exposure`` : `afw.image.Exposure` 

1150 The fully ISR corrected exposure. 

1151 - ``outputExposure`` : `afw.image.Exposure` 

1152 An alias for `exposure` 

1153 - ``ossThumb`` : `numpy.ndarray` 

1154 Thumbnail image of the exposure after overscan subtraction. 

1155 - ``flattenedThumb`` : `numpy.ndarray` 

1156 Thumbnail image of the exposure after flat-field correction. 

1157 

1158 Raises 

1159 ------ 

1160 RuntimeError 

1161 Raised if a configuration option is set to True, but the 

1162 required calibration data has not been specified. 

1163 

1164 Notes 

1165 ----- 

1166 The current processed exposure can be viewed by setting the 

1167 appropriate lsstDebug entries in the `debug.display` 

1168 dictionary. The names of these entries correspond to some of 

1169 the IsrTaskConfig Boolean options, with the value denoting the 

1170 frame to use. The exposure is shown inside the matching 

1171 option check and after the processing of that step has 

1172 finished. The steps with debug points are: 

1173 

1174 doAssembleCcd 

1175 doBias 

1176 doCrosstalk 

1177 doBrighterFatter 

1178 doDark 

1179 doFringe 

1180 doStrayLight 

1181 doFlat 

1182 

1183 In addition, setting the "postISRCCD" entry displays the 

1184 exposure after all ISR processing has finished. 

1185 

1186 """ 

1187 

1188 if isGen3 is True: 

1189 # Gen3 currently cannot automatically do configuration overrides. 

1190 # DM-15257 looks to discuss this issue. 

1191 # Configure input exposures; 

1192 if detectorNum is None: 

1193 raise RuntimeError("Must supply the detectorNum if running as Gen3.") 

1194 

1195 ccdExposure = self.ensureExposure(ccdExposure, camera, detectorNum) 

1196 bias = self.ensureExposure(bias, camera, detectorNum) 

1197 dark = self.ensureExposure(dark, camera, detectorNum) 

1198 flat = self.ensureExposure(flat, camera, detectorNum) 

1199 else: 

1200 if isinstance(ccdExposure, ButlerDataRef): 

1201 return self.runDataRef(ccdExposure) 

1202 

1203 ccd = ccdExposure.getDetector() 

1204 filterName = afwImage.Filter(ccdExposure.getFilter().getId()).getName() # Canonical name for filter 

1205 

1206 if not ccd: 

1207 assert not self.config.doAssembleCcd, "You need a Detector to run assembleCcd." 

1208 ccd = [FakeAmp(ccdExposure, self.config)] 

1209 

1210 # Validate Input 

1211 if self.config.doBias and bias is None: 

1212 raise RuntimeError("Must supply a bias exposure if config.doBias=True.") 

1213 if self.doLinearize(ccd) and linearizer is None: 

1214 raise RuntimeError("Must supply a linearizer if config.doLinearize=True for this detector.") 

1215 if self.config.doBrighterFatter and bfKernel is None: 

1216 raise RuntimeError("Must supply a kernel if config.doBrighterFatter=True.") 

1217 if self.config.doDark and dark is None: 

1218 raise RuntimeError("Must supply a dark exposure if config.doDark=True.") 

1219 if self.config.doFlat and flat is None: 

1220 raise RuntimeError("Must supply a flat exposure if config.doFlat=True.") 

1221 if self.config.doDefect and defects is None: 

1222 raise RuntimeError("Must supply defects if config.doDefect=True.") 

1223 if (self.config.doFringe and filterName in self.fringe.config.filters and 

1224 fringes.fringes is None): 

1225 # The `fringes` object needs to be a pipeBase.Struct, as 

1226 # we use it as a `dict` for the parameters of 

1227 # `FringeTask.run()`. The `fringes.fringes` `list` may 

1228 # not be `None` if `doFringe=True`. Otherwise, raise. 

1229 raise RuntimeError("Must supply fringe exposure as a pipeBase.Struct.") 

1230 if (self.config.doIlluminationCorrection and filterName in self.config.illumFilters and 

1231 illumMaskedImage is None): 

1232 raise RuntimeError("Must supply an illumcor if config.doIlluminationCorrection=True.") 

1233 

1234 # Begin ISR processing. 

1235 if self.config.doConvertIntToFloat: 

1236 self.log.info("Converting exposure to floating point values.") 

1237 ccdExposure = self.convertIntToFloat(ccdExposure) 

1238 

1239 # Amplifier level processing. 

1240 overscans = [] 

1241 for amp in ccd: 

1242 # if ccdExposure is one amp, check for coverage to prevent performing ops multiple times 

1243 if ccdExposure.getBBox().contains(amp.getBBox()): 

1244 # Check for fully masked bad amplifiers, and generate masks for SUSPECT and SATURATED values. 

1245 badAmp = self.maskAmplifier(ccdExposure, amp, defects) 

1246 

1247 if self.config.doOverscan and not badAmp: 

1248 # Overscan correction on amp-by-amp basis. 

1249 overscanResults = self.overscanCorrection(ccdExposure, amp) 

1250 self.log.debug("Corrected overscan for amplifier %s.", amp.getName()) 

1251 if overscanResults is not None and \ 

1252 self.config.qa is not None and self.config.qa.saveStats is True: 

1253 if isinstance(overscanResults.overscanFit, float): 

1254 qaMedian = overscanResults.overscanFit 

1255 qaStdev = float("NaN") 

1256 else: 

1257 qaStats = afwMath.makeStatistics(overscanResults.overscanFit, 

1258 afwMath.MEDIAN | afwMath.STDEVCLIP) 

1259 qaMedian = qaStats.getValue(afwMath.MEDIAN) 

1260 qaStdev = qaStats.getValue(afwMath.STDEVCLIP) 

1261 

1262 self.metadata.set(f"ISR OSCAN {amp.getName()} MEDIAN", qaMedian) 

1263 self.metadata.set(f"ISR OSCAN {amp.getName()} STDEV", qaStdev) 

1264 self.log.debug(" Overscan stats for amplifer %s: %f +/- %f", 

1265 amp.getName(), qaMedian, qaStdev) 

1266 ccdExposure.getMetadata().set('OVERSCAN', "Overscan corrected") 

1267 else: 

1268 if badAmp: 

1269 self.log.warn("Amplifier %s is bad.", amp.getName()) 

1270 overscanResults = None 

1271 

1272 overscans.append(overscanResults if overscanResults is not None else None) 

1273 else: 

1274 self.log.info("Skipped OSCAN for %s.", amp.getName()) 

1275 

1276 if self.config.doCrosstalk and self.config.doCrosstalkBeforeAssemble: 

1277 self.log.info("Applying crosstalk correction.") 

1278 self.crosstalk.run(ccdExposure, crosstalkSources=crosstalkSources) 

1279 self.debugView(ccdExposure, "doCrosstalk") 

1280 

1281 if self.config.doAssembleCcd: 

1282 self.log.info("Assembling CCD from amplifiers.") 

1283 ccdExposure = self.assembleCcd.assembleCcd(ccdExposure) 

1284 

1285 if self.config.expectWcs and not ccdExposure.getWcs(): 

1286 self.log.warn("No WCS found in input exposure.") 

1287 self.debugView(ccdExposure, "doAssembleCcd") 

1288 

1289 ossThumb = None 

1290 if self.config.qa.doThumbnailOss: 

1291 ossThumb = isrQa.makeThumbnail(ccdExposure, isrQaConfig=self.config.qa) 

1292 

1293 if self.config.doBias: 

1294 self.log.info("Applying bias correction.") 

1295 isrFunctions.biasCorrection(ccdExposure.getMaskedImage(), bias.getMaskedImage(), 

1296 trimToFit=self.config.doTrimToMatchCalib) 

1297 self.debugView(ccdExposure, "doBias") 

1298 

1299 if self.config.doVariance: 

1300 for amp, overscanResults in zip(ccd, overscans): 

1301 if ccdExposure.getBBox().contains(amp.getBBox()): 

1302 self.log.debug("Constructing variance map for amplifer %s.", amp.getName()) 

1303 ampExposure = ccdExposure.Factory(ccdExposure, amp.getBBox()) 

1304 if overscanResults is not None: 

1305 self.updateVariance(ampExposure, amp, 

1306 overscanImage=overscanResults.overscanImage) 

1307 else: 

1308 self.updateVariance(ampExposure, amp, 

1309 overscanImage=None) 

1310 if self.config.qa is not None and self.config.qa.saveStats is True: 

1311 qaStats = afwMath.makeStatistics(ampExposure.getVariance(), 

1312 afwMath.MEDIAN | afwMath.STDEVCLIP) 

1313 self.metadata.set(f"ISR VARIANCE {amp.getName()} MEDIAN", 

1314 qaStats.getValue(afwMath.MEDIAN)) 

1315 self.metadata.set(f"ISR VARIANCE {amp.getName()} STDEV", 

1316 qaStats.getValue(afwMath.STDEVCLIP)) 

1317 self.log.debug(" Variance stats for amplifer %s: %f +/- %f.", 

1318 amp.getName(), qaStats.getValue(afwMath.MEDIAN), 

1319 qaStats.getValue(afwMath.STDEVCLIP)) 

1320 

1321 if self.doLinearize(ccd): 

1322 self.log.info("Applying linearizer.") 

1323 linearizer.applyLinearity(image=ccdExposure.getMaskedImage().getImage(), 

1324 detector=ccd, log=self.log) 

1325 

1326 if self.config.doCrosstalk and not self.config.doCrosstalkBeforeAssemble: 

1327 self.log.info("Applying crosstalk correction.") 

1328 self.crosstalk.run(ccdExposure, crosstalkSources=crosstalkSources, isTrimmed=True) 

1329 self.debugView(ccdExposure, "doCrosstalk") 

1330 

1331 # Masking block. Optionally mask known defects, NAN pixels, widen trails, and do 

1332 # anything else the camera needs. Saturated and suspect pixels have already been masked. 

1333 if self.config.doDefect: 

1334 self.log.info("Masking defects.") 

1335 self.maskDefect(ccdExposure, defects) 

1336 

1337 if self.config.numEdgeSuspect > 0: 

1338 self.log.info("Masking edges as SUSPECT.") 

1339 self.maskEdges(ccdExposure, numEdgePixels=self.config.numEdgeSuspect, 

1340 maskPlane="SUSPECT") 

1341 

1342 if self.config.doNanMasking: 

1343 self.log.info("Masking NAN value pixels.") 

1344 self.maskNan(ccdExposure) 

1345 

1346 if self.config.doWidenSaturationTrails: 

1347 self.log.info("Widening saturation trails.") 

1348 isrFunctions.widenSaturationTrails(ccdExposure.getMaskedImage().getMask()) 

1349 

1350 if self.config.doCameraSpecificMasking: 

1351 self.log.info("Masking regions for camera specific reasons.") 

1352 self.masking.run(ccdExposure) 

1353 

1354 if self.config.doBrighterFatter: 

1355 # We need to apply flats and darks before we can interpolate, and we 

1356 # need to interpolate before we do B-F, but we do B-F without the 

1357 # flats and darks applied so we can work in units of electrons or holes. 

1358 # This context manager applies and then removes the darks and flats. 

1359 # 

1360 # We also do not want to interpolate values here, so operate on temporary 

1361 # images so we can apply only the BF-correction and roll back the 

1362 # interpolation. 

1363 interpExp = ccdExposure.clone() 

1364 with self.flatContext(interpExp, flat, dark): 

1365 isrFunctions.interpolateFromMask( 

1366 maskedImage=interpExp.getMaskedImage(), 

1367 fwhm=self.config.fwhm, 

1368 growSaturatedFootprints=self.config.growSaturationFootprintSize, 

1369 maskNameList=self.config.maskListToInterpolate 

1370 ) 

1371 bfExp = interpExp.clone() 

1372 

1373 self.log.info("Applying brighter fatter correction using kernel type %s / gains %s.", 

1374 type(bfKernel), type(bfGains)) 

1375 bfResults = isrFunctions.brighterFatterCorrection(bfExp, bfKernel, 

1376 self.config.brighterFatterMaxIter, 

1377 self.config.brighterFatterThreshold, 

1378 self.config.brighterFatterApplyGain, 

1379 bfGains) 

1380 if bfResults[1] == self.config.brighterFatterMaxIter: 

1381 self.log.warn("Brighter fatter correction did not converge, final difference %f.", 

1382 bfResults[0]) 

1383 else: 

1384 self.log.info("Finished brighter fatter correction in %d iterations.", 

1385 bfResults[1]) 

1386 image = ccdExposure.getMaskedImage().getImage() 

1387 bfCorr = bfExp.getMaskedImage().getImage() 

1388 bfCorr -= interpExp.getMaskedImage().getImage() 

1389 image += bfCorr 

1390 

1391 # Applying the brighter-fatter correction applies a 

1392 # convolution to the science image. At the edges this 

1393 # convolution may not have sufficient valid pixels to 

1394 # produce a valid correction. Mark pixels within the size 

1395 # of the brighter-fatter kernel as EDGE to warn of this 

1396 # fact. 

1397 self.log.info("Ensuring image edges are masked as SUSPECT to the brighter-fatter kernel size.") 

1398 self.maskEdges(ccdExposure, numEdgePixels=numpy.max(bfKernel.shape) // 2, 

1399 maskPlane="EDGE") 

1400 

1401 if self.config.brighterFatterMaskGrowSize > 0: 

1402 self.log.info("Growing masks to account for brighter-fatter kernel convolution.") 

1403 for maskPlane in self.config.maskListToInterpolate: 

1404 isrFunctions.growMasks(ccdExposure.getMask(), 

1405 radius=self.config.brighterFatterMaskGrowSize, 

1406 maskNameList=maskPlane, 

1407 maskValue=maskPlane) 

1408 

1409 self.debugView(ccdExposure, "doBrighterFatter") 

1410 

1411 if self.config.doDark: 

1412 self.log.info("Applying dark correction.") 

1413 self.darkCorrection(ccdExposure, dark) 

1414 self.debugView(ccdExposure, "doDark") 

1415 

1416 if self.config.doFringe and not self.config.fringeAfterFlat: 

1417 self.log.info("Applying fringe correction before flat.") 

1418 self.fringe.run(ccdExposure, **fringes.getDict()) 

1419 self.debugView(ccdExposure, "doFringe") 

1420 

1421 if self.config.doStrayLight and self.strayLight.check(ccdExposure): 

1422 self.log.info("Checking strayLight correction.") 

1423 self.strayLight.run(ccdExposure, strayLightData) 

1424 self.debugView(ccdExposure, "doStrayLight") 

1425 

1426 if self.config.doFlat: 

1427 self.log.info("Applying flat correction.") 

1428 self.flatCorrection(ccdExposure, flat) 

1429 self.debugView(ccdExposure, "doFlat") 

1430 

1431 if self.config.doApplyGains: 

1432 self.log.info("Applying gain correction instead of flat.") 

1433 isrFunctions.applyGains(ccdExposure, self.config.normalizeGains) 

1434 

1435 if self.config.doFringe and self.config.fringeAfterFlat: 

1436 self.log.info("Applying fringe correction after flat.") 

1437 self.fringe.run(ccdExposure, **fringes.getDict()) 

1438 

1439 if self.config.doVignette: 

1440 self.log.info("Constructing Vignette polygon.") 

1441 self.vignettePolygon = self.vignette.run(ccdExposure) 

1442 

1443 if self.config.vignette.doWriteVignettePolygon: 

1444 self.setValidPolygonIntersect(ccdExposure, self.vignettePolygon) 

1445 

1446 if self.config.doAttachTransmissionCurve: 

1447 self.log.info("Adding transmission curves.") 

1448 isrFunctions.attachTransmissionCurve(ccdExposure, opticsTransmission=opticsTransmission, 

1449 filterTransmission=filterTransmission, 

1450 sensorTransmission=sensorTransmission, 

1451 atmosphereTransmission=atmosphereTransmission) 

1452 

1453 flattenedThumb = None 

1454 if self.config.qa.doThumbnailFlattened: 

1455 flattenedThumb = isrQa.makeThumbnail(ccdExposure, isrQaConfig=self.config.qa) 

1456 

1457 if self.config.doIlluminationCorrection and filterName in self.config.illumFilters: 

1458 self.log.info("Performing illumination correction.") 

1459 isrFunctions.illuminationCorrection(ccdExposure.getMaskedImage(), 

1460 illumMaskedImage, illumScale=self.config.illumScale, 

1461 trimToFit=self.config.doTrimToMatchCalib) 

1462 

1463 preInterpExp = None 

1464 if self.config.doSaveInterpPixels: 

1465 preInterpExp = ccdExposure.clone() 

1466 

1467 # Reset and interpolate bad pixels. 

1468 # 

1469 # Large contiguous bad regions (which should have the BAD mask 

1470 # bit set) should have their values set to the image median. 

1471 # This group should include defects and bad amplifiers. As the 

1472 # area covered by these defects are large, there's little 

1473 # reason to expect that interpolation would provide a more 

1474 # useful value. 

1475 # 

1476 # Smaller defects can be safely interpolated after the larger 

1477 # regions have had their pixel values reset. This ensures 

1478 # that the remaining defects adjacent to bad amplifiers (as an 

1479 # example) do not attempt to interpolate extreme values. 

1480 if self.config.doSetBadRegions: 

1481 badPixelCount, badPixelValue = isrFunctions.setBadRegions(ccdExposure) 

1482 if badPixelCount > 0: 

1483 self.log.info("Set %d BAD pixels to %f.", badPixelCount, badPixelValue) 

1484 

1485 if self.config.doInterpolate: 

1486 self.log.info("Interpolating masked pixels.") 

1487 isrFunctions.interpolateFromMask( 

1488 maskedImage=ccdExposure.getMaskedImage(), 

1489 fwhm=self.config.fwhm, 

1490 growSaturatedFootprints=self.config.growSaturationFootprintSize, 

1491 maskNameList=list(self.config.maskListToInterpolate) 

1492 ) 

1493 

1494 self.roughZeroPoint(ccdExposure) 

1495 

1496 if self.config.doMeasureBackground: 

1497 self.log.info("Measuring background level.") 

1498 self.measureBackground(ccdExposure, self.config.qa) 

1499 

1500 if self.config.qa is not None and self.config.qa.saveStats is True: 

1501 for amp in ccd: 

1502 ampExposure = ccdExposure.Factory(ccdExposure, amp.getBBox()) 

1503 qaStats = afwMath.makeStatistics(ampExposure.getImage(), 

1504 afwMath.MEDIAN | afwMath.STDEVCLIP) 

1505 self.metadata.set("ISR BACKGROUND {} MEDIAN".format(amp.getName()), 

1506 qaStats.getValue(afwMath.MEDIAN)) 

1507 self.metadata.set("ISR BACKGROUND {} STDEV".format(amp.getName()), 

1508 qaStats.getValue(afwMath.STDEVCLIP)) 

1509 self.log.debug(" Background stats for amplifer %s: %f +/- %f", 

1510 amp.getName(), qaStats.getValue(afwMath.MEDIAN), 

1511 qaStats.getValue(afwMath.STDEVCLIP)) 

1512 

1513 self.debugView(ccdExposure, "postISRCCD") 

1514 

1515 return pipeBase.Struct( 

1516 exposure=ccdExposure, 

1517 ossThumb=ossThumb, 

1518 flattenedThumb=flattenedThumb, 

1519 

1520 preInterpolatedExposure=preInterpExp, 

1521 outputExposure=ccdExposure, 

1522 outputOssThumbnail=ossThumb, 

1523 outputFlattenedThumbnail=flattenedThumb, 

1524 ) 

1525 

1526 @pipeBase.timeMethod 

1527 def runDataRef(self, sensorRef): 

1528 """Perform instrument signature removal on a ButlerDataRef of a Sensor. 

1529 

1530 This method contains the `CmdLineTask` interface to the ISR 

1531 processing. All IO is handled here, freeing the `run()` method 

1532 to manage only pixel-level calculations. The steps performed 

1533 are: 

1534 - Read in necessary detrending/isr/calibration data. 

1535 - Process raw exposure in `run()`. 

1536 - Persist the ISR-corrected exposure as "postISRCCD" if 

1537 config.doWrite=True. 

1538 

1539 Parameters 

1540 ---------- 

1541 sensorRef : `daf.persistence.butlerSubset.ButlerDataRef` 

1542 DataRef of the detector data to be processed 

1543 

1544 Returns 

1545 ------- 

1546 result : `lsst.pipe.base.Struct` 

1547 Result struct with component: 

1548 - ``exposure`` : `afw.image.Exposure` 

1549 The fully ISR corrected exposure. 

1550 

1551 Raises 

1552 ------ 

1553 RuntimeError 

1554 Raised if a configuration option is set to True, but the 

1555 required calibration data does not exist. 

1556 

1557 """ 

1558 self.log.info("Performing ISR on sensor %s.", sensorRef.dataId) 

1559 

1560 ccdExposure = sensorRef.get(self.config.datasetType) 

1561 

1562 camera = sensorRef.get("camera") 

1563 isrData = self.readIsrData(sensorRef, ccdExposure) 

1564 

1565 result = self.run(ccdExposure, camera=camera, **isrData.getDict()) 

1566 

1567 if self.config.doWrite: 

1568 sensorRef.put(result.exposure, "postISRCCD") 

1569 if result.preInterpolatedExposure is not None: 

1570 sensorRef.put(result.preInterpolatedExposure, "postISRCCD_uninterpolated") 

1571 if result.ossThumb is not None: 

1572 isrQa.writeThumbnail(sensorRef, result.ossThumb, "ossThumb") 

1573 if result.flattenedThumb is not None: 

1574 isrQa.writeThumbnail(sensorRef, result.flattenedThumb, "flattenedThumb") 

1575 

1576 return result 

1577 

1578 def getIsrExposure(self, dataRef, datasetType, dateObs=None, immediate=True): 

1579 """!Retrieve a calibration dataset for removing instrument signature. 

1580 

1581 Parameters 

1582 ---------- 

1583 

1584 dataRef : `daf.persistence.butlerSubset.ButlerDataRef` 

1585 DataRef of the detector data to find calibration datasets 

1586 for. 

1587 datasetType : `str` 

1588 Type of dataset to retrieve (e.g. 'bias', 'flat', etc). 

1589 dateObs : `str`, optional 

1590 Date of the observation. Used to correct butler failures 

1591 when using fallback filters. 

1592 immediate : `Bool` 

1593 If True, disable butler proxies to enable error handling 

1594 within this routine. 

1595 

1596 Returns 

1597 ------- 

1598 exposure : `lsst.afw.image.Exposure` 

1599 Requested calibration frame. 

1600 

1601 Raises 

1602 ------ 

1603 RuntimeError 

1604 Raised if no matching calibration frame can be found. 

1605 """ 

1606 try: 

1607 exp = dataRef.get(datasetType, immediate=immediate) 

1608 except Exception as exc1: 

1609 if not self.config.fallbackFilterName: 

1610 raise RuntimeError("Unable to retrieve %s for %s: %s." % (datasetType, dataRef.dataId, exc1)) 

1611 try: 

1612 if self.config.useFallbackDate and dateObs: 

1613 exp = dataRef.get(datasetType, filter=self.config.fallbackFilterName, 

1614 dateObs=dateObs, immediate=immediate) 

1615 else: 

1616 exp = dataRef.get(datasetType, filter=self.config.fallbackFilterName, immediate=immediate) 

1617 except Exception as exc2: 

1618 raise RuntimeError("Unable to retrieve %s for %s, even with fallback filter %s: %s AND %s." % 

1619 (datasetType, dataRef.dataId, self.config.fallbackFilterName, exc1, exc2)) 

1620 self.log.warn("Using fallback calibration from filter %s.", self.config.fallbackFilterName) 

1621 

1622 if self.config.doAssembleIsrExposures: 

1623 exp = self.assembleCcd.assembleCcd(exp) 

1624 return exp 

1625 

1626 def ensureExposure(self, inputExp, camera, detectorNum): 

1627 """Ensure that the data returned by Butler is a fully constructed exposure. 

1628 

1629 ISR requires exposure-level image data for historical reasons, so if we did 

1630 not recieve that from Butler, construct it from what we have, modifying the 

1631 input in place. 

1632 

1633 Parameters 

1634 ---------- 

1635 inputExp : `lsst.afw.image.Exposure`, `lsst.afw.image.DecoratedImageU`, or 

1636 `lsst.afw.image.ImageF` 

1637 The input data structure obtained from Butler. 

1638 camera : `lsst.afw.cameraGeom.camera` 

1639 The camera associated with the image. Used to find the appropriate 

1640 detector. 

1641 detectorNum : `int` 

1642 The detector this exposure should match. 

1643 

1644 Returns 

1645 ------- 

1646 inputExp : `lsst.afw.image.Exposure` 

1647 The re-constructed exposure, with appropriate detector parameters. 

1648 

1649 Raises 

1650 ------ 

1651 TypeError 

1652 Raised if the input data cannot be used to construct an exposure. 

1653 """ 

1654 if isinstance(inputExp, afwImage.DecoratedImageU): 

1655 inputExp = afwImage.makeExposure(afwImage.makeMaskedImage(inputExp)) 

1656 elif isinstance(inputExp, afwImage.ImageF): 

1657 inputExp = afwImage.makeExposure(afwImage.makeMaskedImage(inputExp)) 

1658 elif isinstance(inputExp, afwImage.MaskedImageF): 

1659 inputExp = afwImage.makeExposure(inputExp) 

1660 elif isinstance(inputExp, afwImage.Exposure): 

1661 pass 

1662 elif inputExp is None: 

1663 # Assume this will be caught by the setup if it is a problem. 

1664 return inputExp 

1665 else: 

1666 raise TypeError("Input Exposure is not known type in isrTask.ensureExposure: %s." % 

1667 (type(inputExp), )) 

1668 

1669 if inputExp.getDetector() is None: 

1670 inputExp.setDetector(camera[detectorNum]) 

1671 

1672 return inputExp 

1673 

1674 def convertIntToFloat(self, exposure): 

1675 """Convert exposure image from uint16 to float. 

1676 

1677 If the exposure does not need to be converted, the input is 

1678 immediately returned. For exposures that are converted to use 

1679 floating point pixels, the variance is set to unity and the 

1680 mask to zero. 

1681 

1682 Parameters 

1683 ---------- 

1684 exposure : `lsst.afw.image.Exposure` 

1685 The raw exposure to be converted. 

1686 

1687 Returns 

1688 ------- 

1689 newexposure : `lsst.afw.image.Exposure` 

1690 The input ``exposure``, converted to floating point pixels. 

1691 

1692 Raises 

1693 ------ 

1694 RuntimeError 

1695 Raised if the exposure type cannot be converted to float. 

1696 

1697 """ 

1698 if isinstance(exposure, afwImage.ExposureF): 

1699 # Nothing to be done 

1700 self.log.debug("Exposure already of type float.") 

1701 return exposure 

1702 if not hasattr(exposure, "convertF"): 

1703 raise RuntimeError("Unable to convert exposure (%s) to float." % type(exposure)) 

1704 

1705 newexposure = exposure.convertF() 

1706 newexposure.variance[:] = 1 

1707 newexposure.mask[:] = 0x0 

1708 

1709 return newexposure 

1710 

1711 def maskAmplifier(self, ccdExposure, amp, defects): 

1712 """Identify bad amplifiers, saturated and suspect pixels. 

1713 

1714 Parameters 

1715 ---------- 

1716 ccdExposure : `lsst.afw.image.Exposure` 

1717 Input exposure to be masked. 

1718 amp : `lsst.afw.table.AmpInfoCatalog` 

1719 Catalog of parameters defining the amplifier on this 

1720 exposure to mask. 

1721 defects : `lsst.meas.algorithms.Defects` 

1722 List of defects. Used to determine if the entire 

1723 amplifier is bad. 

1724 

1725 Returns 

1726 ------- 

1727 badAmp : `Bool` 

1728 If this is true, the entire amplifier area is covered by 

1729 defects and unusable. 

1730 

1731 """ 

1732 maskedImage = ccdExposure.getMaskedImage() 

1733 

1734 badAmp = False 

1735 

1736 # Check if entire amp region is defined as a defect (need to use amp.getBBox() for correct 

1737 # comparison with current defects definition. 

1738 if defects is not None: 

1739 badAmp = bool(sum([v.getBBox().contains(amp.getBBox()) for v in defects])) 

1740 

1741 # In the case of a bad amp, we will set mask to "BAD" (here use amp.getRawBBox() for correct 

1742 # association with pixels in current ccdExposure). 

1743 if badAmp: 

1744 dataView = afwImage.MaskedImageF(maskedImage, amp.getRawBBox(), 

1745 afwImage.PARENT) 

1746 maskView = dataView.getMask() 

1747 maskView |= maskView.getPlaneBitMask("BAD") 

1748 del maskView 

1749 return badAmp 

1750 

1751 # Mask remaining defects after assembleCcd() to allow for defects that cross amplifier boundaries. 

1752 # Saturation and suspect pixels can be masked now, though. 

1753 limits = dict() 

1754 if self.config.doSaturation and not badAmp: 

1755 limits.update({self.config.saturatedMaskName: amp.getSaturation()}) 

1756 if self.config.doSuspect and not badAmp: 

1757 limits.update({self.config.suspectMaskName: amp.getSuspectLevel()}) 

1758 if math.isfinite(self.config.saturation): 

1759 limits.update({self.config.saturatedMaskName: self.config.saturation}) 

1760 

1761 for maskName, maskThreshold in limits.items(): 

1762 if not math.isnan(maskThreshold): 

1763 dataView = maskedImage.Factory(maskedImage, amp.getRawBBox()) 

1764 isrFunctions.makeThresholdMask( 

1765 maskedImage=dataView, 

1766 threshold=maskThreshold, 

1767 growFootprints=0, 

1768 maskName=maskName 

1769 ) 

1770 

1771 # Determine if we've fully masked this amplifier with SUSPECT and SAT pixels. 

1772 maskView = afwImage.Mask(maskedImage.getMask(), amp.getRawDataBBox(), 

1773 afwImage.PARENT) 

1774 maskVal = maskView.getPlaneBitMask([self.config.saturatedMaskName, 

1775 self.config.suspectMaskName]) 

1776 if numpy.all(maskView.getArray() & maskVal > 0): 

1777 badAmp = True 

1778 maskView |= maskView.getPlaneBitMask("BAD") 

1779 

1780 return badAmp 

1781 

1782 def overscanCorrection(self, ccdExposure, amp): 

1783 """Apply overscan correction in place. 

1784 

1785 This method does initial pixel rejection of the overscan 

1786 region. The overscan can also be optionally segmented to 

1787 allow for discontinuous overscan responses to be fit 

1788 separately. The actual overscan subtraction is performed by 

1789 the `lsst.ip.isr.isrFunctions.overscanCorrection` function, 

1790 which is called here after the amplifier is preprocessed. 

1791 

1792 Parameters 

1793 ---------- 

1794 ccdExposure : `lsst.afw.image.Exposure` 

1795 Exposure to have overscan correction performed. 

1796 amp : `lsst.afw.table.AmpInfoCatalog` 

1797 The amplifier to consider while correcting the overscan. 

1798 

1799 Returns 

1800 ------- 

1801 overscanResults : `lsst.pipe.base.Struct` 

1802 Result struct with components: 

1803 - ``imageFit`` : scalar or `lsst.afw.image.Image` 

1804 Value or fit subtracted from the amplifier image data. 

1805 - ``overscanFit`` : scalar or `lsst.afw.image.Image` 

1806 Value or fit subtracted from the overscan image data. 

1807 - ``overscanImage`` : `lsst.afw.image.Image` 

1808 Image of the overscan region with the overscan 

1809 correction applied. This quantity is used to estimate 

1810 the amplifier read noise empirically. 

1811 

1812 Raises 

1813 ------ 

1814 RuntimeError 

1815 Raised if the ``amp`` does not contain raw pixel information. 

1816 

1817 See Also 

1818 -------- 

1819 lsst.ip.isr.isrFunctions.overscanCorrection 

1820 """ 

1821 if amp.getRawHorizontalOverscanBBox().isEmpty(): 

1822 self.log.info("ISR_OSCAN: No overscan region. Not performing overscan correction.") 

1823 return None 

1824 

1825 statControl = afwMath.StatisticsControl() 

1826 statControl.setAndMask(ccdExposure.mask.getPlaneBitMask("SAT")) 

1827 

1828 # Determine the bounding boxes 

1829 dataBBox = amp.getRawDataBBox() 

1830 oscanBBox = amp.getRawHorizontalOverscanBBox() 

1831 dx0 = 0 

1832 dx1 = 0 

1833 

1834 prescanBBox = amp.getRawPrescanBBox() 

1835 if (oscanBBox.getBeginX() > prescanBBox.getBeginX()): # amp is at the right 

1836 dx0 += self.config.overscanNumLeadingColumnsToSkip 

1837 dx1 -= self.config.overscanNumTrailingColumnsToSkip 

1838 else: 

1839 dx0 += self.config.overscanNumTrailingColumnsToSkip 

1840 dx1 -= self.config.overscanNumLeadingColumnsToSkip 

1841 

1842 # Determine if we need to work on subregions of the amplifier and overscan. 

1843 imageBBoxes = [] 

1844 overscanBBoxes = [] 

1845 

1846 if ((self.config.overscanBiasJump and 

1847 self.config.overscanBiasJumpLocation) and 

1848 (ccdExposure.getMetadata().exists(self.config.overscanBiasJumpKeyword) and 

1849 ccdExposure.getMetadata().getScalar(self.config.overscanBiasJumpKeyword) in 

1850 self.config.overscanBiasJumpDevices)): 

1851 if amp.getReadoutCorner() in (ReadoutCorner.LL, ReadoutCorner.LR): 

1852 yLower = self.config.overscanBiasJumpLocation 

1853 yUpper = dataBBox.getHeight() - yLower 

1854 else: 

1855 yUpper = self.config.overscanBiasJumpLocation 

1856 yLower = dataBBox.getHeight() - yUpper 

1857 

1858 imageBBoxes.append(lsst.geom.Box2I(dataBBox.getBegin(), 

1859 lsst.geom.Extent2I(dataBBox.getWidth(), yLower))) 

1860 overscanBBoxes.append(lsst.geom.Box2I(oscanBBox.getBegin() + 

1861 lsst.geom.Extent2I(dx0, 0), 

1862 lsst.geom.Extent2I(oscanBBox.getWidth() - dx0 + dx1, 

1863 yLower))) 

1864 

1865 imageBBoxes.append(lsst.geom.Box2I(dataBBox.getBegin() + lsst.geom.Extent2I(0, yLower), 

1866 lsst.geom.Extent2I(dataBBox.getWidth(), yUpper))) 

1867 overscanBBoxes.append(lsst.geom.Box2I(oscanBBox.getBegin() + lsst.geom.Extent2I(dx0, yLower), 

1868 lsst.geom.Extent2I(oscanBBox.getWidth() - dx0 + dx1, 

1869 yUpper))) 

1870 else: 

1871 imageBBoxes.append(lsst.geom.Box2I(dataBBox.getBegin(), 

1872 lsst.geom.Extent2I(dataBBox.getWidth(), dataBBox.getHeight()))) 

1873 overscanBBoxes.append(lsst.geom.Box2I(oscanBBox.getBegin() + lsst.geom.Extent2I(dx0, 0), 

1874 lsst.geom.Extent2I(oscanBBox.getWidth() - dx0 + dx1, 

1875 oscanBBox.getHeight()))) 

1876 

1877 # Perform overscan correction on subregions, ensuring saturated pixels are masked. 

1878 for imageBBox, overscanBBox in zip(imageBBoxes, overscanBBoxes): 

1879 ampImage = ccdExposure.maskedImage[imageBBox] 

1880 overscanImage = ccdExposure.maskedImage[overscanBBox] 

1881 

1882 overscanArray = overscanImage.image.array 

1883 median = numpy.ma.median(numpy.ma.masked_where(overscanImage.mask.array, overscanArray)) 

1884 bad = numpy.where(numpy.abs(overscanArray - median) > self.config.overscanMaxDev) 

1885 overscanImage.mask.array[bad] = overscanImage.mask.getPlaneBitMask("SAT") 

1886 

1887 statControl = afwMath.StatisticsControl() 

1888 statControl.setAndMask(ccdExposure.mask.getPlaneBitMask("SAT")) 

1889 

1890 overscanResults = self.overscan.run(ampImage.getImage(), overscanImage) 

1891 

1892 # Measure average overscan levels and record them in the metadata. 

1893 levelStat = afwMath.MEDIAN 

1894 sigmaStat = afwMath.STDEVCLIP 

1895 

1896 sctrl = afwMath.StatisticsControl(self.config.qa.flatness.clipSigma, 

1897 self.config.qa.flatness.nIter) 

1898 metadata = ccdExposure.getMetadata() 

1899 ampNum = amp.getName() 

1900 # if self.config.overscanFitType in ("MEDIAN", "MEAN", "MEANCLIP"): 

1901 if isinstance(overscanResults.overscanFit, float): 

1902 metadata.set("ISR_OSCAN_LEVEL%s" % ampNum, overscanResults.overscanFit) 

1903 metadata.set("ISR_OSCAN_SIGMA%s" % ampNum, 0.0) 

1904 else: 

1905 stats = afwMath.makeStatistics(overscanResults.overscanFit, levelStat | sigmaStat, sctrl) 

1906 metadata.set("ISR_OSCAN_LEVEL%s" % ampNum, stats.getValue(levelStat)) 

1907 metadata.set("ISR_OSCAN_SIGMA%s" % ampNum, stats.getValue(sigmaStat)) 

1908 

1909 return overscanResults 

1910 

1911 def updateVariance(self, ampExposure, amp, overscanImage=None): 

1912 """Set the variance plane using the amplifier gain and read noise 

1913 

1914 The read noise is calculated from the ``overscanImage`` if the 

1915 ``doEmpiricalReadNoise`` option is set in the configuration; otherwise 

1916 the value from the amplifier data is used. 

1917 

1918 Parameters 

1919 ---------- 

1920 ampExposure : `lsst.afw.image.Exposure` 

1921 Exposure to process. 

1922 amp : `lsst.afw.table.AmpInfoRecord` or `FakeAmp` 

1923 Amplifier detector data. 

1924 overscanImage : `lsst.afw.image.MaskedImage`, optional. 

1925 Image of overscan, required only for empirical read noise. 

1926 

1927 See also 

1928 -------- 

1929 lsst.ip.isr.isrFunctions.updateVariance 

1930 """ 

1931 maskPlanes = [self.config.saturatedMaskName, self.config.suspectMaskName] 

1932 gain = amp.getGain() 

1933 

1934 if math.isnan(gain): 

1935 gain = 1.0 

1936 self.log.warn("Gain set to NAN! Updating to 1.0 to generate Poisson variance.") 

1937 elif gain <= 0: 

1938 patchedGain = 1.0 

1939 self.log.warn("Gain for amp %s == %g <= 0; setting to %f.", 

1940 amp.getName(), gain, patchedGain) 

1941 gain = patchedGain 

1942 

1943 if self.config.doEmpiricalReadNoise and overscanImage is None: 

1944 self.log.info("Overscan is none for EmpiricalReadNoise.") 

1945 

1946 if self.config.doEmpiricalReadNoise and overscanImage is not None: 

1947 stats = afwMath.StatisticsControl() 

1948 stats.setAndMask(overscanImage.mask.getPlaneBitMask(maskPlanes)) 

1949 readNoise = afwMath.makeStatistics(overscanImage, afwMath.STDEVCLIP, stats).getValue() 

1950 self.log.info("Calculated empirical read noise for amp %s: %f.", 

1951 amp.getName(), readNoise) 

1952 else: 

1953 readNoise = amp.getReadNoise() 

1954 

1955 isrFunctions.updateVariance( 

1956 maskedImage=ampExposure.getMaskedImage(), 

1957 gain=gain, 

1958 readNoise=readNoise, 

1959 ) 

1960 

1961 def darkCorrection(self, exposure, darkExposure, invert=False): 

1962 """!Apply dark correction in place. 

1963 

1964 Parameters 

1965 ---------- 

1966 exposure : `lsst.afw.image.Exposure` 

1967 Exposure to process. 

1968 darkExposure : `lsst.afw.image.Exposure` 

1969 Dark exposure of the same size as ``exposure``. 

1970 invert : `Bool`, optional 

1971 If True, re-add the dark to an already corrected image. 

1972 

1973 Raises 

1974 ------ 

1975 RuntimeError 

1976 Raised if either ``exposure`` or ``darkExposure`` do not 

1977 have their dark time defined. 

1978 

1979 See Also 

1980 -------- 

1981 lsst.ip.isr.isrFunctions.darkCorrection 

1982 """ 

1983 expScale = exposure.getInfo().getVisitInfo().getDarkTime() 

1984 if math.isnan(expScale): 

1985 raise RuntimeError("Exposure darktime is NAN.") 

1986 if darkExposure.getInfo().getVisitInfo() is not None \ 

1987 and not math.isnan(darkExposure.getInfo().getVisitInfo().getDarkTime()): 

1988 darkScale = darkExposure.getInfo().getVisitInfo().getDarkTime() 

1989 else: 

1990 # DM-17444: darkExposure.getInfo.getVisitInfo() is None 

1991 # so getDarkTime() does not exist. 

1992 self.log.warn("darkExposure.getInfo().getVisitInfo() does not exist. Using darkScale = 1.0.") 

1993 darkScale = 1.0 

1994 

1995 isrFunctions.darkCorrection( 

1996 maskedImage=exposure.getMaskedImage(), 

1997 darkMaskedImage=darkExposure.getMaskedImage(), 

1998 expScale=expScale, 

1999 darkScale=darkScale, 

2000 invert=invert, 

2001 trimToFit=self.config.doTrimToMatchCalib 

2002 ) 

2003 

2004 def doLinearize(self, detector): 

2005 """!Check if linearization is needed for the detector cameraGeom. 

2006 

2007 Checks config.doLinearize and the linearity type of the first 

2008 amplifier. 

2009 

2010 Parameters 

2011 ---------- 

2012 detector : `lsst.afw.cameraGeom.Detector` 

2013 Detector to get linearity type from. 

2014 

2015 Returns 

2016 ------- 

2017 doLinearize : `Bool` 

2018 If True, linearization should be performed. 

2019 """ 

2020 return self.config.doLinearize and \ 

2021 detector.getAmplifiers()[0].getLinearityType() != NullLinearityType 

2022 

2023 def flatCorrection(self, exposure, flatExposure, invert=False): 

2024 """!Apply flat correction in place. 

2025 

2026 Parameters 

2027 ---------- 

2028 exposure : `lsst.afw.image.Exposure` 

2029 Exposure to process. 

2030 flatExposure : `lsst.afw.image.Exposure` 

2031 Flat exposure of the same size as ``exposure``. 

2032 invert : `Bool`, optional 

2033 If True, unflatten an already flattened image. 

2034 

2035 See Also 

2036 -------- 

2037 lsst.ip.isr.isrFunctions.flatCorrection 

2038 """ 

2039 isrFunctions.flatCorrection( 

2040 maskedImage=exposure.getMaskedImage(), 

2041 flatMaskedImage=flatExposure.getMaskedImage(), 

2042 scalingType=self.config.flatScalingType, 

2043 userScale=self.config.flatUserScale, 

2044 invert=invert, 

2045 trimToFit=self.config.doTrimToMatchCalib 

2046 ) 

2047 

2048 def saturationDetection(self, exposure, amp): 

2049 """!Detect saturated pixels and mask them using mask plane config.saturatedMaskName, in place. 

2050 

2051 Parameters 

2052 ---------- 

2053 exposure : `lsst.afw.image.Exposure` 

2054 Exposure to process. Only the amplifier DataSec is processed. 

2055 amp : `lsst.afw.table.AmpInfoCatalog` 

2056 Amplifier detector data. 

2057 

2058 See Also 

2059 -------- 

2060 lsst.ip.isr.isrFunctions.makeThresholdMask 

2061 """ 

2062 if not math.isnan(amp.getSaturation()): 

2063 maskedImage = exposure.getMaskedImage() 

2064 dataView = maskedImage.Factory(maskedImage, amp.getRawBBox()) 

2065 isrFunctions.makeThresholdMask( 

2066 maskedImage=dataView, 

2067 threshold=amp.getSaturation(), 

2068 growFootprints=0, 

2069 maskName=self.config.saturatedMaskName, 

2070 ) 

2071 

2072 def saturationInterpolation(self, exposure): 

2073 """!Interpolate over saturated pixels, in place. 

2074 

2075 This method should be called after `saturationDetection`, to 

2076 ensure that the saturated pixels have been identified in the 

2077 SAT mask. It should also be called after `assembleCcd`, since 

2078 saturated regions may cross amplifier boundaries. 

2079 

2080 Parameters 

2081 ---------- 

2082 exposure : `lsst.afw.image.Exposure` 

2083 Exposure to process. 

2084 

2085 See Also 

2086 -------- 

2087 lsst.ip.isr.isrTask.saturationDetection 

2088 lsst.ip.isr.isrFunctions.interpolateFromMask 

2089 """ 

2090 isrFunctions.interpolateFromMask( 

2091 maskedImage=exposure.getMaskedImage(), 

2092 fwhm=self.config.fwhm, 

2093 growSaturatedFootprints=self.config.growSaturationFootprintSize, 

2094 maskNameList=list(self.config.saturatedMaskName), 

2095 ) 

2096 

2097 def suspectDetection(self, exposure, amp): 

2098 """!Detect suspect pixels and mask them using mask plane config.suspectMaskName, in place. 

2099 

2100 Parameters 

2101 ---------- 

2102 exposure : `lsst.afw.image.Exposure` 

2103 Exposure to process. Only the amplifier DataSec is processed. 

2104 amp : `lsst.afw.table.AmpInfoCatalog` 

2105 Amplifier detector data. 

2106 

2107 See Also 

2108 -------- 

2109 lsst.ip.isr.isrFunctions.makeThresholdMask 

2110 

2111 Notes 

2112 ----- 

2113 Suspect pixels are pixels whose value is greater than amp.getSuspectLevel(). 

2114 This is intended to indicate pixels that may be affected by unknown systematics; 

2115 for example if non-linearity corrections above a certain level are unstable 

2116 then that would be a useful value for suspectLevel. A value of `nan` indicates 

2117 that no such level exists and no pixels are to be masked as suspicious. 

2118 """ 

2119 suspectLevel = amp.getSuspectLevel() 

2120 if math.isnan(suspectLevel): 

2121 return 

2122 

2123 maskedImage = exposure.getMaskedImage() 

2124 dataView = maskedImage.Factory(maskedImage, amp.getRawBBox()) 

2125 isrFunctions.makeThresholdMask( 

2126 maskedImage=dataView, 

2127 threshold=suspectLevel, 

2128 growFootprints=0, 

2129 maskName=self.config.suspectMaskName, 

2130 ) 

2131 

2132 def maskDefect(self, exposure, defectBaseList): 

2133 """!Mask defects using mask plane "BAD", in place. 

2134 

2135 Parameters 

2136 ---------- 

2137 exposure : `lsst.afw.image.Exposure` 

2138 Exposure to process. 

2139 defectBaseList : `lsst.meas.algorithms.Defects` or `list` of 

2140 `lsst.afw.image.DefectBase`. 

2141 List of defects to mask. 

2142 

2143 Notes 

2144 ----- 

2145 Call this after CCD assembly, since defects may cross amplifier boundaries. 

2146 """ 

2147 maskedImage = exposure.getMaskedImage() 

2148 if not isinstance(defectBaseList, Defects): 

2149 # Promotes DefectBase to Defect 

2150 defectList = Defects(defectBaseList) 

2151 else: 

2152 defectList = defectBaseList 

2153 defectList.maskPixels(maskedImage, maskName="BAD") 

2154 

2155 def maskEdges(self, exposure, numEdgePixels=0, maskPlane="SUSPECT"): 

2156 """!Mask edge pixels with applicable mask plane. 

2157 

2158 Parameters 

2159 ---------- 

2160 exposure : `lsst.afw.image.Exposure` 

2161 Exposure to process. 

2162 numEdgePixels : `int`, optional 

2163 Number of edge pixels to mask. 

2164 maskPlane : `str`, optional 

2165 Mask plane name to use. 

2166 """ 

2167 maskedImage = exposure.getMaskedImage() 

2168 maskBitMask = maskedImage.getMask().getPlaneBitMask(maskPlane) 

2169 

2170 if numEdgePixels > 0: 

2171 goodBBox = maskedImage.getBBox() 

2172 # This makes a bbox numEdgeSuspect pixels smaller than the image on each side 

2173 goodBBox.grow(-numEdgePixels) 

2174 # Mask pixels outside goodBBox 

2175 SourceDetectionTask.setEdgeBits( 

2176 maskedImage, 

2177 goodBBox, 

2178 maskBitMask 

2179 ) 

2180 

2181 def maskAndInterpolateDefects(self, exposure, defectBaseList): 

2182 """Mask and interpolate defects using mask plane "BAD", in place. 

2183 

2184 Parameters 

2185 ---------- 

2186 exposure : `lsst.afw.image.Exposure` 

2187 Exposure to process. 

2188 defectBaseList : `lsst.meas.algorithms.Defects` or `list` of 

2189 `lsst.afw.image.DefectBase`. 

2190 List of defects to mask and interpolate. 

2191 

2192 See Also 

2193 -------- 

2194 lsst.ip.isr.isrTask.maskDefect() 

2195 """ 

2196 self.maskDefect(exposure, defectBaseList) 

2197 self.maskEdges(exposure, numEdgePixels=self.config.numEdgeSuspect, 

2198 maskPlane="SUSPECT") 

2199 isrFunctions.interpolateFromMask( 

2200 maskedImage=exposure.getMaskedImage(), 

2201 fwhm=self.config.fwhm, 

2202 growSaturatedFootprints=0, 

2203 maskNameList=["BAD"], 

2204 ) 

2205 

2206 def maskNan(self, exposure): 

2207 """Mask NaNs using mask plane "UNMASKEDNAN", in place. 

2208 

2209 Parameters 

2210 ---------- 

2211 exposure : `lsst.afw.image.Exposure` 

2212 Exposure to process. 

2213 

2214 Notes 

2215 ----- 

2216 We mask over all NaNs, including those that are masked with 

2217 other bits (because those may or may not be interpolated over 

2218 later, and we want to remove all NaNs). Despite this 

2219 behaviour, the "UNMASKEDNAN" mask plane is used to preserve 

2220 the historical name. 

2221 """ 

2222 maskedImage = exposure.getMaskedImage() 

2223 

2224 # Find and mask NaNs 

2225 maskedImage.getMask().addMaskPlane("UNMASKEDNAN") 

2226 maskVal = maskedImage.getMask().getPlaneBitMask("UNMASKEDNAN") 

2227 numNans = maskNans(maskedImage, maskVal) 

2228 self.metadata.set("NUMNANS", numNans) 

2229 if numNans > 0: 

2230 self.log.warn("There were %d unmasked NaNs.", numNans) 

2231 

2232 def maskAndInterpolateNan(self, exposure): 

2233 """"Mask and interpolate NaNs using mask plane "UNMASKEDNAN", in place. 

2234 

2235 Parameters 

2236 ---------- 

2237 exposure : `lsst.afw.image.Exposure` 

2238 Exposure to process. 

2239 

2240 See Also 

2241 -------- 

2242 lsst.ip.isr.isrTask.maskNan() 

2243 """ 

2244 self.maskNan(exposure) 

2245 isrFunctions.interpolateFromMask( 

2246 maskedImage=exposure.getMaskedImage(), 

2247 fwhm=self.config.fwhm, 

2248 growSaturatedFootprints=0, 

2249 maskNameList=["UNMASKEDNAN"], 

2250 ) 

2251 

2252 def measureBackground(self, exposure, IsrQaConfig=None): 

2253 """Measure the image background in subgrids, for quality control purposes. 

2254 

2255 Parameters 

2256 ---------- 

2257 exposure : `lsst.afw.image.Exposure` 

2258 Exposure to process. 

2259 IsrQaConfig : `lsst.ip.isr.isrQa.IsrQaConfig` 

2260 Configuration object containing parameters on which background 

2261 statistics and subgrids to use. 

2262 """ 

2263 if IsrQaConfig is not None: 

2264 statsControl = afwMath.StatisticsControl(IsrQaConfig.flatness.clipSigma, 

2265 IsrQaConfig.flatness.nIter) 

2266 maskVal = exposure.getMaskedImage().getMask().getPlaneBitMask(["BAD", "SAT", "DETECTED"]) 

2267 statsControl.setAndMask(maskVal) 

2268 maskedImage = exposure.getMaskedImage() 

2269 stats = afwMath.makeStatistics(maskedImage, afwMath.MEDIAN | afwMath.STDEVCLIP, statsControl) 

2270 skyLevel = stats.getValue(afwMath.MEDIAN) 

2271 skySigma = stats.getValue(afwMath.STDEVCLIP) 

2272 self.log.info("Flattened sky level: %f +/- %f.", skyLevel, skySigma) 

2273 metadata = exposure.getMetadata() 

2274 metadata.set('SKYLEVEL', skyLevel) 

2275 metadata.set('SKYSIGMA', skySigma) 

2276 

2277 # calcluating flatlevel over the subgrids 

2278 stat = afwMath.MEANCLIP if IsrQaConfig.flatness.doClip else afwMath.MEAN 

2279 meshXHalf = int(IsrQaConfig.flatness.meshX/2.) 

2280 meshYHalf = int(IsrQaConfig.flatness.meshY/2.) 

2281 nX = int((exposure.getWidth() + meshXHalf) / IsrQaConfig.flatness.meshX) 

2282 nY = int((exposure.getHeight() + meshYHalf) / IsrQaConfig.flatness.meshY) 

2283 skyLevels = numpy.zeros((nX, nY)) 

2284 

2285 for j in range(nY): 

2286 yc = meshYHalf + j * IsrQaConfig.flatness.meshY 

2287 for i in range(nX): 

2288 xc = meshXHalf + i * IsrQaConfig.flatness.meshX 

2289 

2290 xLLC = xc - meshXHalf 

2291 yLLC = yc - meshYHalf 

2292 xURC = xc + meshXHalf - 1 

2293 yURC = yc + meshYHalf - 1 

2294 

2295 bbox = lsst.geom.Box2I(lsst.geom.Point2I(xLLC, yLLC), lsst.geom.Point2I(xURC, yURC)) 

2296 miMesh = maskedImage.Factory(exposure.getMaskedImage(), bbox, afwImage.LOCAL) 

2297 

2298 skyLevels[i, j] = afwMath.makeStatistics(miMesh, stat, statsControl).getValue() 

2299 

2300 good = numpy.where(numpy.isfinite(skyLevels)) 

2301 skyMedian = numpy.median(skyLevels[good]) 

2302 flatness = (skyLevels[good] - skyMedian) / skyMedian 

2303 flatness_rms = numpy.std(flatness) 

2304 flatness_pp = flatness.max() - flatness.min() if len(flatness) > 0 else numpy.nan 

2305 

2306 self.log.info("Measuring sky levels in %dx%d grids: %f.", nX, nY, skyMedian) 

2307 self.log.info("Sky flatness in %dx%d grids - pp: %f rms: %f.", 

2308 nX, nY, flatness_pp, flatness_rms) 

2309 

2310 metadata.set('FLATNESS_PP', float(flatness_pp)) 

2311 metadata.set('FLATNESS_RMS', float(flatness_rms)) 

2312 metadata.set('FLATNESS_NGRIDS', '%dx%d' % (nX, nY)) 

2313 metadata.set('FLATNESS_MESHX', IsrQaConfig.flatness.meshX) 

2314 metadata.set('FLATNESS_MESHY', IsrQaConfig.flatness.meshY) 

2315 

2316 def roughZeroPoint(self, exposure): 

2317 """Set an approximate magnitude zero point for the exposure. 

2318 

2319 Parameters 

2320 ---------- 

2321 exposure : `lsst.afw.image.Exposure` 

2322 Exposure to process. 

2323 """ 

2324 filterName = afwImage.Filter(exposure.getFilter().getId()).getName() # Canonical name for filter 

2325 if filterName in self.config.fluxMag0T1: 

2326 fluxMag0 = self.config.fluxMag0T1[filterName] 

2327 else: 

2328 self.log.warn("No rough magnitude zero point set for filter %s.", filterName) 

2329 fluxMag0 = self.config.defaultFluxMag0T1 

2330 

2331 expTime = exposure.getInfo().getVisitInfo().getExposureTime() 

2332 if not expTime > 0: # handle NaN as well as <= 0 

2333 self.log.warn("Non-positive exposure time; skipping rough zero point.") 

2334 return 

2335 

2336 self.log.info("Setting rough magnitude zero point: %f", 2.5*math.log10(fluxMag0*expTime)) 

2337 exposure.setPhotoCalib(afwImage.makePhotoCalibFromCalibZeroPoint(fluxMag0*expTime, 0.0)) 

2338 

2339 def setValidPolygonIntersect(self, ccdExposure, fpPolygon): 

2340 """!Set the valid polygon as the intersection of fpPolygon and the ccd corners. 

2341 

2342 Parameters 

2343 ---------- 

2344 ccdExposure : `lsst.afw.image.Exposure` 

2345 Exposure to process. 

2346 fpPolygon : `lsst.afw.geom.Polygon` 

2347 Polygon in focal plane coordinates. 

2348 """ 

2349 # Get ccd corners in focal plane coordinates 

2350 ccd = ccdExposure.getDetector() 

2351 fpCorners = ccd.getCorners(FOCAL_PLANE) 

2352 ccdPolygon = Polygon(fpCorners) 

2353 

2354 # Get intersection of ccd corners with fpPolygon 

2355 intersect = ccdPolygon.intersectionSingle(fpPolygon) 

2356 

2357 # Transform back to pixel positions and build new polygon 

2358 ccdPoints = ccd.transform(intersect, FOCAL_PLANE, PIXELS) 

2359 validPolygon = Polygon(ccdPoints) 

2360 ccdExposure.getInfo().setValidPolygon(validPolygon) 

2361 

2362 @contextmanager 

2363 def flatContext(self, exp, flat, dark=None): 

2364 """Context manager that applies and removes flats and darks, 

2365 if the task is configured to apply them. 

2366 

2367 Parameters 

2368 ---------- 

2369 exp : `lsst.afw.image.Exposure` 

2370 Exposure to process. 

2371 flat : `lsst.afw.image.Exposure` 

2372 Flat exposure the same size as ``exp``. 

2373 dark : `lsst.afw.image.Exposure`, optional 

2374 Dark exposure the same size as ``exp``. 

2375 

2376 Yields 

2377 ------ 

2378 exp : `lsst.afw.image.Exposure` 

2379 The flat and dark corrected exposure. 

2380 """ 

2381 if self.config.doDark and dark is not None: 

2382 self.darkCorrection(exp, dark) 

2383 if self.config.doFlat: 

2384 self.flatCorrection(exp, flat) 

2385 try: 

2386 yield exp 

2387 finally: 

2388 if self.config.doFlat: 

2389 self.flatCorrection(exp, flat, invert=True) 

2390 if self.config.doDark and dark is not None: 

2391 self.darkCorrection(exp, dark, invert=True) 

2392 

2393 def debugView(self, exposure, stepname): 

2394 """Utility function to examine ISR exposure at different stages. 

2395 

2396 Parameters 

2397 ---------- 

2398 exposure : `lsst.afw.image.Exposure` 

2399 Exposure to view. 

2400 stepname : `str` 

2401 State of processing to view. 

2402 """ 

2403 frame = getDebugFrame(self._display, stepname) 

2404 if frame: 

2405 display = getDisplay(frame) 

2406 display.scale('asinh', 'zscale') 

2407 display.mtv(exposure) 

2408 prompt = "Press Enter to continue [c]... " 

2409 while True: 

2410 ans = input(prompt).lower() 

2411 if ans in ("", "c",): 

2412 break 

2413 

2414 

2415class FakeAmp(object): 

2416 """A Detector-like object that supports returning gain and saturation level 

2417 

2418 This is used when the input exposure does not have a detector. 

2419 

2420 Parameters 

2421 ---------- 

2422 exposure : `lsst.afw.image.Exposure` 

2423 Exposure to generate a fake amplifier for. 

2424 config : `lsst.ip.isr.isrTaskConfig` 

2425 Configuration to apply to the fake amplifier. 

2426 """ 

2427 

2428 def __init__(self, exposure, config): 

2429 self._bbox = exposure.getBBox(afwImage.LOCAL) 

2430 self._RawHorizontalOverscanBBox = lsst.geom.Box2I() 

2431 self._gain = config.gain 

2432 self._readNoise = config.readNoise 

2433 self._saturation = config.saturation 

2434 

2435 def getBBox(self): 

2436 return self._bbox 

2437 

2438 def getRawBBox(self): 

2439 return self._bbox 

2440 

2441 def getRawHorizontalOverscanBBox(self): 

2442 return self._RawHorizontalOverscanBBox 

2443 

2444 def getGain(self): 

2445 return self._gain 

2446 

2447 def getReadNoise(self): 

2448 return self._readNoise 

2449 

2450 def getSaturation(self): 

2451 return self._saturation 

2452 

2453 def getSuspectLevel(self): 

2454 return float("NaN") 

2455 

2456 

2457class RunIsrConfig(pexConfig.Config): 

2458 isr = pexConfig.ConfigurableField(target=IsrTask, doc="Instrument signature removal") 

2459 

2460 

2461class RunIsrTask(pipeBase.CmdLineTask): 

2462 """Task to wrap the default IsrTask to allow it to be retargeted. 

2463 

2464 The standard IsrTask can be called directly from a command line 

2465 program, but doing so removes the ability of the task to be 

2466 retargeted. As most cameras override some set of the IsrTask 

2467 methods, this would remove those data-specific methods in the 

2468 output post-ISR images. This wrapping class fixes the issue, 

2469 allowing identical post-ISR images to be generated by both the 

2470 processCcd and isrTask code. 

2471 """ 

2472 ConfigClass = RunIsrConfig 

2473 _DefaultName = "runIsr" 

2474 

2475 def __init__(self, *args, **kwargs): 

2476 super().__init__(*args, **kwargs) 

2477 self.makeSubtask("isr") 

2478 

2479 def runDataRef(self, dataRef): 

2480 """ 

2481 Parameters 

2482 ---------- 

2483 dataRef : `lsst.daf.persistence.ButlerDataRef` 

2484 data reference of the detector data to be processed 

2485 

2486 Returns 

2487 ------- 

2488 result : `pipeBase.Struct` 

2489 Result struct with component: 

2490 

2491 - exposure : `lsst.afw.image.Exposure` 

2492 Post-ISR processed exposure. 

2493 """ 

2494 return self.isr.runDataRef(dataRef)