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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 

42 

43from . import isrFunctions 

44from . import isrQa 

45from . import linearize 

46from .defects import Defects 

47 

48from .assembleCcdTask import AssembleCcdTask 

49from .crosstalk import CrosstalkTask, CrosstalkCalib 

50from .fringe import FringeTask 

51from .isr import maskNans 

52from .masking import MaskingTask 

53from .overscan import OverscanCorrectionTask 

54from .straylight import StrayLightTask 

55from .vignette import VignetteTask 

56from lsst.daf.butler import DimensionGraph 

57 

58 

59__all__ = ["IsrTask", "IsrTaskConfig", "RunIsrTask", "RunIsrConfig"] 

60 

61 

62def crosstalkSourceLookup(datasetType, registry, quantumDataId, collections): 

63 """Lookup function to identify crosstalkSource entries. 

64 

65 This should return an empty list under most circumstances. Only 

66 when inter-chip crosstalk has been identified should this be 

67 populated. 

68 

69 This will be unused until DM-25348 resolves the quantum graph 

70 generation issue. 

71 

72 Parameters 

73 ---------- 

74 datasetType : `str` 

75 Dataset to lookup. 

76 registry : `lsst.daf.butler.Registry` 

77 Butler registry to query. 

78 quantumDataId : `lsst.daf.butler.ExpandedDataCoordinate` 

79 Data id to transform to identify crosstalkSources. The 

80 ``detector`` entry will be stripped. 

81 collections : `lsst.daf.butler.CollectionSearch` 

82 Collections to search through. 

83 

84 Returns 

85 ------- 

86 results : `list` [`lsst.daf.butler.DatasetRef`] 

87 List of datasets that match the query that will be used as 

88 crosstalkSources. 

89 """ 

90 newDataId = quantumDataId.subset(DimensionGraph(registry.dimensions, names=["instrument", "exposure"])) 

91 results = list(registry.queryDatasets(datasetType, 

92 collections=collections, 

93 dataId=newDataId, 

94 findFirst=True, 

95 ).expanded()) 

96 return results 

97 

98 

99class IsrTaskConnections(pipeBase.PipelineTaskConnections, 

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

101 defaultTemplates={}): 

102 ccdExposure = cT.Input( 

103 name="raw", 

104 doc="Input exposure to process.", 

105 storageClass="Exposure", 

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

107 ) 

108 camera = cT.PrerequisiteInput( 

109 name="camera", 

110 storageClass="Camera", 

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

112 dimensions=["instrument"], 

113 isCalibration=True, 

114 ) 

115 

116 crosstalk = cT.PrerequisiteInput( 

117 name="crosstalk", 

118 doc="Input crosstalk object", 

119 storageClass="CrosstalkCalib", 

120 dimensions=["instrument", "detector"], 

121 isCalibration=True, 

122 ) 

123 # TODO: DM-25348. This does not work yet to correctly load 

124 # possible crosstalk sources. 

125 crosstalkSources = cT.PrerequisiteInput( 

126 name="isrOverscanCorrected", 

127 doc="Overscan corrected input images.", 

128 storageClass="Exposure", 

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

130 deferLoad=True, 

131 multiple=True, 

132 lookupFunction=crosstalkSourceLookup, 

133 ) 

134 bias = cT.PrerequisiteInput( 

135 name="bias", 

136 doc="Input bias calibration.", 

137 storageClass="ExposureF", 

138 dimensions=["instrument", "detector"], 

139 isCalibration=True, 

140 ) 

141 dark = cT.PrerequisiteInput( 

142 name='dark', 

143 doc="Input dark calibration.", 

144 storageClass="ExposureF", 

145 dimensions=["instrument", "detector"], 

146 isCalibration=True, 

147 ) 

148 flat = cT.PrerequisiteInput( 

149 name="flat", 

150 doc="Input flat calibration.", 

151 storageClass="ExposureF", 

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

153 isCalibration=True, 

154 ) 

155 ptc = cT.PrerequisiteInput( 

156 name="ptc", 

157 doc="Input Photon Transfer Curve dataset", 

158 storageClass="PhotonTransferCurveDataset", 

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

160 isCalibration=True, 

161 ) 

162 fringes = cT.PrerequisiteInput( 

163 name="fringe", 

164 doc="Input fringe calibration.", 

165 storageClass="ExposureF", 

166 dimensions=["instrument", "physical_filter", "detector"], 

167 isCalibration=True, 

168 ) 

169 strayLightData = cT.PrerequisiteInput( 

170 name='yBackground', 

171 doc="Input stray light calibration.", 

172 storageClass="StrayLightData", 

173 dimensions=["instrument", "physical_filter", "detector"], 

174 isCalibration=True, 

175 ) 

176 bfKernel = cT.PrerequisiteInput( 

177 name='bfKernel', 

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

179 storageClass="NumpyArray", 

180 dimensions=["instrument"], 

181 isCalibration=True, 

182 ) 

183 newBFKernel = cT.PrerequisiteInput( 

184 name='brighterFatterKernel', 

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

186 storageClass="BrighterFatterKernel", 

187 dimensions=["instrument", "detector"], 

188 isCalibration=True, 

189 ) 

190 defects = cT.PrerequisiteInput( 

191 name='defects', 

192 doc="Input defect tables.", 

193 storageClass="Defects", 

194 dimensions=["instrument", "detector"], 

195 isCalibration=True, 

196 ) 

197 linearizer = cT.PrerequisiteInput( 

198 name='linearizer', 

199 storageClass="Linearizer", 

200 doc="Linearity correction calibration.", 

201 dimensions=["instrument", "detector"], 

202 isCalibration=True, 

203 ) 

204 opticsTransmission = cT.PrerequisiteInput( 

205 name="transmission_optics", 

206 storageClass="TransmissionCurve", 

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

208 dimensions=["instrument"], 

209 isCalibration=True, 

210 ) 

211 filterTransmission = cT.PrerequisiteInput( 

212 name="transmission_filter", 

213 storageClass="TransmissionCurve", 

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

215 dimensions=["instrument", "physical_filter"], 

216 isCalibration=True, 

217 ) 

218 sensorTransmission = cT.PrerequisiteInput( 

219 name="transmission_sensor", 

220 storageClass="TransmissionCurve", 

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

222 dimensions=["instrument", "detector"], 

223 isCalibration=True, 

224 ) 

225 atmosphereTransmission = cT.PrerequisiteInput( 

226 name="transmission_atmosphere", 

227 storageClass="TransmissionCurve", 

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

229 dimensions=["instrument"], 

230 isCalibration=True, 

231 ) 

232 illumMaskedImage = cT.PrerequisiteInput( 

233 name="illum", 

234 doc="Input illumination correction.", 

235 storageClass="MaskedImageF", 

236 dimensions=["instrument", "physical_filter", "detector"], 

237 isCalibration=True, 

238 ) 

239 

240 outputExposure = cT.Output( 

241 name='postISRCCD', 

242 doc="Output ISR processed exposure.", 

243 storageClass="Exposure", 

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

245 ) 

246 preInterpExposure = cT.Output( 

247 name='preInterpISRCCD', 

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

249 storageClass="ExposureF", 

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

251 ) 

252 outputOssThumbnail = cT.Output( 

253 name="OssThumb", 

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

255 storageClass="Thumbnail", 

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

257 ) 

258 outputFlattenedThumbnail = cT.Output( 

259 name="FlattenedThumb", 

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

261 storageClass="Thumbnail", 

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

263 ) 

264 

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

266 super().__init__(config=config) 

267 

268 if config.doBias is not True: 

269 self.prerequisiteInputs.discard("bias") 

270 if config.doLinearize is not True: 

271 self.prerequisiteInputs.discard("linearizer") 

272 if config.doCrosstalk is not True: 

273 self.inputs.discard("crosstalkSources") 

274 self.prerequisiteInputs.discard("crosstalk") 

275 if config.doBrighterFatter is not True: 

276 self.prerequisiteInputs.discard("bfKernel") 

277 self.prerequisiteInputs.discard("newBFKernel") 

278 if config.doDefect is not True: 

279 self.prerequisiteInputs.discard("defects") 

280 if config.doDark is not True: 

281 self.prerequisiteInputs.discard("dark") 

282 if config.doFlat is not True: 

283 self.prerequisiteInputs.discard("flat") 

284 if config.usePtcGains is not True and config.usePtcReadNoise is not True: 

285 self.prerequisiteInputs.discard("ptc") 

286 if config.doAttachTransmissionCurve is not True: 

287 self.prerequisiteInputs.discard("opticsTransmission") 

288 self.prerequisiteInputs.discard("filterTransmission") 

289 self.prerequisiteInputs.discard("sensorTransmission") 

290 self.prerequisiteInputs.discard("atmosphereTransmission") 

291 if config.doUseOpticsTransmission is not True: 

292 self.prerequisiteInputs.discard("opticsTransmission") 

293 if config.doUseFilterTransmission is not True: 

294 self.prerequisiteInputs.discard("filterTransmission") 

295 if config.doUseSensorTransmission is not True: 

296 self.prerequisiteInputs.discard("sensorTransmission") 

297 if config.doUseAtmosphereTransmission is not True: 

298 self.prerequisiteInputs.discard("atmosphereTransmission") 

299 if config.doIlluminationCorrection is not True: 

300 self.prerequisiteInputs.discard("illumMaskedImage") 

301 

302 if config.doWrite is not True: 

303 self.outputs.discard("outputExposure") 

304 self.outputs.discard("preInterpExposure") 

305 self.outputs.discard("outputFlattenedThumbnail") 

306 self.outputs.discard("outputOssThumbnail") 

307 if config.doSaveInterpPixels is not True: 

308 self.outputs.discard("preInterpExposure") 

309 if config.qa.doThumbnailOss is not True: 

310 self.outputs.discard("outputOssThumbnail") 

311 if config.qa.doThumbnailFlattened is not True: 

312 self.outputs.discard("outputFlattenedThumbnail") 

313 

314 

315class IsrTaskConfig(pipeBase.PipelineTaskConfig, 

316 pipelineConnections=IsrTaskConnections): 

317 """Configuration parameters for IsrTask. 

318 

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

320 """ 

321 datasetType = pexConfig.Field( 

322 dtype=str, 

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

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

325 default="raw", 

326 ) 

327 

328 fallbackFilterName = pexConfig.Field( 

329 dtype=str, 

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

331 optional=True 

332 ) 

333 useFallbackDate = pexConfig.Field( 

334 dtype=bool, 

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

336 default=False, 

337 ) 

338 expectWcs = pexConfig.Field( 

339 dtype=bool, 

340 default=True, 

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

342 ) 

343 fwhm = pexConfig.Field( 

344 dtype=float, 

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

346 default=1.0, 

347 ) 

348 qa = pexConfig.ConfigField( 

349 dtype=isrQa.IsrQaConfig, 

350 doc="QA related configuration options.", 

351 ) 

352 

353 # Image conversion configuration 

354 doConvertIntToFloat = pexConfig.Field( 

355 dtype=bool, 

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

357 default=True, 

358 ) 

359 

360 # Saturated pixel handling. 

361 doSaturation = pexConfig.Field( 

362 dtype=bool, 

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

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

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

366 default=True, 

367 ) 

368 saturatedMaskName = pexConfig.Field( 

369 dtype=str, 

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

371 default="SAT", 

372 ) 

373 saturation = pexConfig.Field( 

374 dtype=float, 

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

376 default=float("NaN"), 

377 ) 

378 growSaturationFootprintSize = pexConfig.Field( 

379 dtype=int, 

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

381 default=1, 

382 ) 

383 

384 # Suspect pixel handling. 

385 doSuspect = pexConfig.Field( 

386 dtype=bool, 

387 doc="Mask suspect pixels?", 

388 default=False, 

389 ) 

390 suspectMaskName = pexConfig.Field( 

391 dtype=str, 

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

393 default="SUSPECT", 

394 ) 

395 numEdgeSuspect = pexConfig.Field( 

396 dtype=int, 

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

398 default=0, 

399 ) 

400 edgeMaskLevel = pexConfig.ChoiceField( 

401 dtype=str, 

402 doc="Mask edge pixels in which coordinate frame: DETECTOR or AMP?", 

403 default="DETECTOR", 

404 allowed={ 

405 'DETECTOR': 'Mask only the edges of the full detector.', 

406 'AMP': 'Mask edges of each amplifier.', 

407 }, 

408 ) 

409 

410 # Initial masking options. 

411 doSetBadRegions = pexConfig.Field( 

412 dtype=bool, 

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

414 default=True, 

415 ) 

416 badStatistic = pexConfig.ChoiceField( 

417 dtype=str, 

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

419 default='MEANCLIP', 

420 allowed={ 

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

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

423 }, 

424 ) 

425 

426 # Overscan subtraction configuration. 

427 doOverscan = pexConfig.Field( 

428 dtype=bool, 

429 doc="Do overscan subtraction?", 

430 default=True, 

431 ) 

432 overscan = pexConfig.ConfigurableField( 

433 target=OverscanCorrectionTask, 

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

435 ) 

436 

437 overscanFitType = pexConfig.ChoiceField( 

438 dtype=str, 

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

440 default='MEDIAN', 

441 allowed={ 

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

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

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

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

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

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

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

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

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

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

452 }, 

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

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

455 ) 

456 overscanOrder = pexConfig.Field( 

457 dtype=int, 

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

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

460 default=1, 

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

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

463 ) 

464 overscanNumSigmaClip = pexConfig.Field( 

465 dtype=float, 

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

467 default=3.0, 

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

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

470 ) 

471 overscanIsInt = pexConfig.Field( 

472 dtype=bool, 

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

474 " and overscan.FitType=MEDIAN_PER_ROW.", 

475 default=True, 

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

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

478 ) 

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

480 overscanNumLeadingColumnsToSkip = pexConfig.Field( 

481 dtype=int, 

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

483 default=0, 

484 ) 

485 overscanNumTrailingColumnsToSkip = pexConfig.Field( 

486 dtype=int, 

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

488 default=0, 

489 ) 

490 overscanMaxDev = pexConfig.Field( 490 ↛ exitline 490 didn't jump to the function exit

491 dtype=float, 

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

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

494 ) 

495 overscanBiasJump = pexConfig.Field( 

496 dtype=bool, 

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

498 default=False, 

499 ) 

500 overscanBiasJumpKeyword = pexConfig.Field( 

501 dtype=str, 

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

503 default="NO_SUCH_KEY", 

504 ) 

505 overscanBiasJumpDevices = pexConfig.ListField( 

506 dtype=str, 

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

508 default=(), 

509 ) 

510 overscanBiasJumpLocation = pexConfig.Field( 

511 dtype=int, 

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

513 default=0, 

514 ) 

515 

516 # Amplifier to CCD assembly configuration 

517 doAssembleCcd = pexConfig.Field( 

518 dtype=bool, 

519 default=True, 

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

521 ) 

522 assembleCcd = pexConfig.ConfigurableField( 

523 target=AssembleCcdTask, 

524 doc="CCD assembly task", 

525 ) 

526 

527 # General calibration configuration. 

528 doAssembleIsrExposures = pexConfig.Field( 

529 dtype=bool, 

530 default=False, 

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

532 ) 

533 doTrimToMatchCalib = pexConfig.Field( 

534 dtype=bool, 

535 default=False, 

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

537 ) 

538 

539 # Bias subtraction. 

540 doBias = pexConfig.Field( 

541 dtype=bool, 

542 doc="Apply bias frame correction?", 

543 default=True, 

544 ) 

545 biasDataProductName = pexConfig.Field( 

546 dtype=str, 

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

548 default="bias", 

549 ) 

550 doBiasBeforeOverscan = pexConfig.Field( 

551 dtype=bool, 

552 doc="Reverse order of overscan and bias correction.", 

553 default=False 

554 ) 

555 

556 # Variance construction 

557 doVariance = pexConfig.Field( 

558 dtype=bool, 

559 doc="Calculate variance?", 

560 default=True 

561 ) 

562 gain = pexConfig.Field( 

563 dtype=float, 

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

565 default=float("NaN"), 

566 ) 

567 readNoise = pexConfig.Field( 

568 dtype=float, 

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

570 default=0.0, 

571 ) 

572 doEmpiricalReadNoise = pexConfig.Field( 

573 dtype=bool, 

574 default=False, 

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

576 ) 

577 usePtcReadNoise = pexConfig.Field( 

578 dtype=bool, 

579 default=False, 

580 doc="Use readnoise values from the Photon Transfer Curve?" 

581 ) 

582 # Linearization. 

583 doLinearize = pexConfig.Field( 

584 dtype=bool, 

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

586 default=True, 

587 ) 

588 

589 # Crosstalk. 

590 doCrosstalk = pexConfig.Field( 

591 dtype=bool, 

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

593 default=False, 

594 ) 

595 doCrosstalkBeforeAssemble = pexConfig.Field( 

596 dtype=bool, 

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

598 default=False, 

599 ) 

600 crosstalk = pexConfig.ConfigurableField( 

601 target=CrosstalkTask, 

602 doc="Intra-CCD crosstalk correction", 

603 ) 

604 

605 # Masking options. 

606 doDefect = pexConfig.Field( 

607 dtype=bool, 

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

609 default=True, 

610 ) 

611 doNanMasking = pexConfig.Field( 

612 dtype=bool, 

613 doc="Mask non-finite (NAN, inf) pixels?", 

614 default=True, 

615 ) 

616 doWidenSaturationTrails = pexConfig.Field( 

617 dtype=bool, 

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

619 default=True 

620 ) 

621 

622 # Brighter-Fatter correction. 

623 doBrighterFatter = pexConfig.Field( 

624 dtype=bool, 

625 default=False, 

626 doc="Apply the brighter-fatter correction?" 

627 ) 

628 brighterFatterLevel = pexConfig.ChoiceField( 

629 dtype=str, 

630 default="DETECTOR", 

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

632 allowed={ 

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

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

635 } 

636 ) 

637 brighterFatterMaxIter = pexConfig.Field( 

638 dtype=int, 

639 default=10, 

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

641 ) 

642 brighterFatterThreshold = pexConfig.Field( 

643 dtype=float, 

644 default=1000, 

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

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

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

648 ) 

649 brighterFatterApplyGain = pexConfig.Field( 

650 dtype=bool, 

651 default=True, 

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

653 ) 

654 brighterFatterMaskListToInterpolate = pexConfig.ListField( 

655 dtype=str, 

656 doc="List of mask planes that should be interpolated over when applying the brighter-fatter " 

657 "correction.", 

658 default=["SAT", "BAD", "NO_DATA", "UNMASKEDNAN"], 

659 ) 

660 brighterFatterMaskGrowSize = pexConfig.Field( 

661 dtype=int, 

662 default=0, 

663 doc="Number of pixels to grow the masks listed in config.brighterFatterMaskListToInterpolate " 

664 "when brighter-fatter correction is applied." 

665 ) 

666 

667 # Dark subtraction. 

668 doDark = pexConfig.Field( 

669 dtype=bool, 

670 doc="Apply dark frame correction?", 

671 default=True, 

672 ) 

673 darkDataProductName = pexConfig.Field( 

674 dtype=str, 

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

676 default="dark", 

677 ) 

678 

679 # Camera-specific stray light removal. 

680 doStrayLight = pexConfig.Field( 

681 dtype=bool, 

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

683 default=False, 

684 ) 

685 strayLight = pexConfig.ConfigurableField( 

686 target=StrayLightTask, 

687 doc="y-band stray light correction" 

688 ) 

689 

690 # Flat correction. 

691 doFlat = pexConfig.Field( 

692 dtype=bool, 

693 doc="Apply flat field correction?", 

694 default=True, 

695 ) 

696 flatDataProductName = pexConfig.Field( 

697 dtype=str, 

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

699 default="flat", 

700 ) 

701 flatScalingType = pexConfig.ChoiceField( 

702 dtype=str, 

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

704 default='USER', 

705 allowed={ 

706 "USER": "Scale by flatUserScale", 

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

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

709 }, 

710 ) 

711 flatUserScale = pexConfig.Field( 

712 dtype=float, 

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

714 default=1.0, 

715 ) 

716 doTweakFlat = pexConfig.Field( 

717 dtype=bool, 

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

719 default=False 

720 ) 

721 

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

723 doApplyGains = pexConfig.Field( 

724 dtype=bool, 

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

726 default=False, 

727 ) 

728 usePtcGains = pexConfig.Field( 

729 dtype=bool, 

730 doc="Use the gain values from the Photon Transfer Curve?", 

731 default=False, 

732 ) 

733 normalizeGains = pexConfig.Field( 

734 dtype=bool, 

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

736 default=False, 

737 ) 

738 

739 # Fringe correction. 

740 doFringe = pexConfig.Field( 

741 dtype=bool, 

742 doc="Apply fringe correction?", 

743 default=True, 

744 ) 

745 fringe = pexConfig.ConfigurableField( 

746 target=FringeTask, 

747 doc="Fringe subtraction task", 

748 ) 

749 fringeAfterFlat = pexConfig.Field( 

750 dtype=bool, 

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

752 default=True, 

753 ) 

754 

755 # Initial CCD-level background statistics options. 

756 doMeasureBackground = pexConfig.Field( 

757 dtype=bool, 

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

759 default=False, 

760 ) 

761 

762 # Camera-specific masking configuration. 

763 doCameraSpecificMasking = pexConfig.Field( 

764 dtype=bool, 

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

766 default=False, 

767 ) 

768 masking = pexConfig.ConfigurableField( 

769 target=MaskingTask, 

770 doc="Masking task." 

771 ) 

772 

773 # Interpolation options. 

774 

775 doInterpolate = pexConfig.Field( 

776 dtype=bool, 

777 doc="Interpolate masked pixels?", 

778 default=True, 

779 ) 

780 doSaturationInterpolation = pexConfig.Field( 

781 dtype=bool, 

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

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

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

785 default=True, 

786 ) 

787 doNanInterpolation = pexConfig.Field( 

788 dtype=bool, 

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

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

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

792 default=True, 

793 ) 

794 doNanInterpAfterFlat = pexConfig.Field( 

795 dtype=bool, 

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

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

798 default=False, 

799 ) 

800 maskListToInterpolate = pexConfig.ListField( 

801 dtype=str, 

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

803 default=['SAT', 'BAD'], 

804 ) 

805 doSaveInterpPixels = pexConfig.Field( 

806 dtype=bool, 

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

808 default=False, 

809 ) 

810 

811 # Default photometric calibration options. 

812 fluxMag0T1 = pexConfig.DictField( 

813 keytype=str, 

814 itemtype=float, 

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

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

817 )) 

818 ) 

819 defaultFluxMag0T1 = pexConfig.Field( 

820 dtype=float, 

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

822 default=pow(10.0, 0.4*28.0) 

823 ) 

824 

825 # Vignette correction configuration. 

826 doVignette = pexConfig.Field( 

827 dtype=bool, 

828 doc="Apply vignetting parameters?", 

829 default=False, 

830 ) 

831 vignette = pexConfig.ConfigurableField( 

832 target=VignetteTask, 

833 doc="Vignetting task.", 

834 ) 

835 

836 # Transmission curve configuration. 

837 doAttachTransmissionCurve = pexConfig.Field( 

838 dtype=bool, 

839 default=False, 

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

841 ) 

842 doUseOpticsTransmission = pexConfig.Field( 

843 dtype=bool, 

844 default=True, 

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

846 ) 

847 doUseFilterTransmission = pexConfig.Field( 

848 dtype=bool, 

849 default=True, 

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

851 ) 

852 doUseSensorTransmission = pexConfig.Field( 

853 dtype=bool, 

854 default=True, 

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

856 ) 

857 doUseAtmosphereTransmission = pexConfig.Field( 

858 dtype=bool, 

859 default=True, 

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

861 ) 

862 

863 # Illumination correction. 

864 doIlluminationCorrection = pexConfig.Field( 

865 dtype=bool, 

866 default=False, 

867 doc="Perform illumination correction?" 

868 ) 

869 illuminationCorrectionDataProductName = pexConfig.Field( 

870 dtype=str, 

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

872 default="illumcor", 

873 ) 

874 illumScale = pexConfig.Field( 

875 dtype=float, 

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

877 default=1.0, 

878 ) 

879 illumFilters = pexConfig.ListField( 

880 dtype=str, 

881 default=[], 

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

883 ) 

884 

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

886 doWrite = pexConfig.Field( 

887 dtype=bool, 

888 doc="Persist postISRCCD?", 

889 default=True, 

890 ) 

891 

892 def validate(self): 

893 super().validate() 

894 if self.doFlat and self.doApplyGains: 

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

896 if self.doBiasBeforeOverscan and self.doTrimToMatchCalib: 

897 raise ValueError("You may not specify both doBiasBeforeOverscan and doTrimToMatchCalib") 

898 if self.doSaturationInterpolation and self.saturatedMaskName not in self.maskListToInterpolate: 

899 self.maskListToInterpolate.append(self.saturatedMaskName) 

900 if not self.doSaturationInterpolation and self.saturatedMaskName in self.maskListToInterpolate: 

901 self.maskListToInterpolate.remove(self.saturatedMaskName) 

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

903 self.maskListToInterpolate.append("UNMASKEDNAN") 

904 

905 

906class IsrTask(pipeBase.PipelineTask, pipeBase.CmdLineTask): 

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

908 

909 The process for correcting imaging data is very similar from 

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

911 doing these corrections, including the ability to turn certain 

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

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

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

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

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

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

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

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

920 subclassed for different camera, although the most camera specific 

921 methods have been split into subtasks that can be redirected 

922 appropriately. 

923 

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

925 the defaults from `lsst.ip.isr`. 

926 

927 Parameters 

928 ---------- 

929 args : `list` 

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

931 kwargs : `dict`, optional 

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

933 """ 

934 ConfigClass = IsrTaskConfig 

935 _DefaultName = "isr" 

936 

937 def __init__(self, **kwargs): 

938 super().__init__(**kwargs) 

939 self.makeSubtask("assembleCcd") 

940 self.makeSubtask("crosstalk") 

941 self.makeSubtask("strayLight") 

942 self.makeSubtask("fringe") 

943 self.makeSubtask("masking") 

944 self.makeSubtask("overscan") 

945 self.makeSubtask("vignette") 

946 

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

948 inputs = butlerQC.get(inputRefs) 

949 

950 try: 

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

952 except Exception as e: 

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

954 (inputRefs, e)) 

955 

956 inputs['isGen3'] = True 

957 

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

959 

960 if self.config.doCrosstalk is True: 

961 # Crosstalk sources need to be defined by the pipeline 

962 # yaml if they exist. 

963 if 'crosstalk' in inputs and inputs['crosstalk'] is not None: 

964 if not isinstance(inputs['crosstalk'], CrosstalkCalib): 

965 inputs['crosstalk'] = CrosstalkCalib.fromTable(inputs['crosstalk']) 

966 else: 

967 coeffVector = (self.config.crosstalk.crosstalkValues 

968 if self.config.crosstalk.useConfigCoefficients else None) 

969 crosstalkCalib = CrosstalkCalib().fromDetector(detector, coeffVector=coeffVector) 

970 inputs['crosstalk'] = crosstalkCalib 

971 if inputs['crosstalk'].interChip and len(inputs['crosstalk'].interChip) > 0: 

972 if 'crosstalkSources' not in inputs: 

973 self.log.warn("No crosstalkSources found for chip with interChip terms!") 

974 

975 if self.doLinearize(detector) is True: 

976 if 'linearizer' in inputs: 

977 if isinstance(inputs['linearizer'], dict): 

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

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

980 self.log.warn("Dictionary linearizers will be deprecated in DM-28741.") 

981 elif isinstance(inputs['linearizer'], numpy.ndarray): 

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

983 detector=detector, 

984 log=self.log) 

985 self.log.warn("Bare lookup table linearizers will be deprecated in DM-28741.") 

986 else: 

987 linearizer = inputs['linearizer'] 

988 linearizer.log = self.log 

989 inputs['linearizer'] = linearizer 

990 else: 

991 inputs['linearizer'] = linearize.Linearizer(detector=detector, log=self.log) 

992 self.log.warn("Constructing linearizer from cameraGeom information.") 

993 

994 if self.config.doDefect is True: 

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

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

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

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

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

1000 

1001 # Load the correct style of brighter-fatter kernel, and repack 

1002 # the information as a numpy array. 

1003 if self.config.doBrighterFatter: 

1004 brighterFatterKernel = inputs.pop('newBFKernel', None) 

1005 if brighterFatterKernel is None: 

1006 brighterFatterKernel = inputs.get('bfKernel', None) 

1007 

1008 if brighterFatterKernel is not None and not isinstance(brighterFatterKernel, numpy.ndarray): 

1009 # This is a ISR calib kernel 

1010 detName = detector.getName() 

1011 level = brighterFatterKernel.level 

1012 

1013 # This is expected to be a dictionary of amp-wise gains. 

1014 inputs['bfGains'] = brighterFatterKernel.gain 

1015 if self.config.brighterFatterLevel == 'DETECTOR': 

1016 if level == 'DETECTOR': 

1017 if detName in brighterFatterKernel.detKernels: 

1018 inputs['bfKernel'] = brighterFatterKernel.detKernels[detName] 

1019 else: 

1020 raise RuntimeError("Failed to extract kernel from new-style BF kernel.") 

1021 elif level == 'AMP': 

1022 self.log.warn("Making DETECTOR level kernel from AMP based brighter fatter kernels.") 

1023 brighterFatterKernel.makeDetectorKernelFromAmpwiseKernels(detName) 

1024 inputs['bfKernel'] = brighterFatterKernel.detKernels[detName] 

1025 elif self.config.brighterFatterLevel == 'AMP': 

1026 raise NotImplementedError("Per-amplifier brighter-fatter correction not implemented") 

1027 

1028 if self.config.doFringe is True and self.fringe.checkFilter(inputs['ccdExposure']): 

1029 expId = inputs['ccdExposure'].getInfo().getVisitInfo().getExposureId() 

1030 inputs['fringes'] = self.fringe.loadFringes(inputs['fringes'], 

1031 expId=expId, 

1032 assembler=self.assembleCcd 

1033 if self.config.doAssembleIsrExposures else None) 

1034 else: 

1035 inputs['fringes'] = pipeBase.Struct(fringes=None) 

1036 

1037 if self.config.doStrayLight is True and self.strayLight.checkFilter(inputs['ccdExposure']): 

1038 if 'strayLightData' not in inputs: 

1039 inputs['strayLightData'] = None 

1040 

1041 outputs = self.run(**inputs) 

1042 butlerQC.put(outputs, outputRefs) 

1043 

1044 def readIsrData(self, dataRef, rawExposure): 

1045 """Retrieve necessary frames for instrument signature removal. 

1046 

1047 Pre-fetching all required ISR data products limits the IO 

1048 required by the ISR. Any conflict between the calibration data 

1049 available and that needed for ISR is also detected prior to 

1050 doing processing, allowing it to fail quickly. 

1051 

1052 Parameters 

1053 ---------- 

1054 dataRef : `daf.persistence.butlerSubset.ButlerDataRef` 

1055 Butler reference of the detector data to be processed 

1056 rawExposure : `afw.image.Exposure` 

1057 The raw exposure that will later be corrected with the 

1058 retrieved calibration data; should not be modified in this 

1059 method. 

1060 

1061 Returns 

1062 ------- 

1063 result : `lsst.pipe.base.Struct` 

1064 Result struct with components (which may be `None`): 

1065 - ``bias``: bias calibration frame (`afw.image.Exposure`) 

1066 - ``linearizer``: functor for linearization (`ip.isr.linearize.LinearizeBase`) 

1067 - ``crosstalkSources``: list of possible crosstalk sources (`list`) 

1068 - ``dark``: dark calibration frame (`afw.image.Exposure`) 

1069 - ``flat``: flat calibration frame (`afw.image.Exposure`) 

1070 - ``bfKernel``: Brighter-Fatter kernel (`numpy.ndarray`) 

1071 - ``defects``: list of defects (`lsst.ip.isr.Defects`) 

1072 - ``fringes``: `lsst.pipe.base.Struct` with components: 

1073 - ``fringes``: fringe calibration frame (`afw.image.Exposure`) 

1074 - ``seed``: random seed derived from the ccdExposureId for random 

1075 number generator (`uint32`). 

1076 - ``opticsTransmission``: `lsst.afw.image.TransmissionCurve` 

1077 A ``TransmissionCurve`` that represents the throughput of the optics, 

1078 to be evaluated in focal-plane coordinates. 

1079 - ``filterTransmission`` : `lsst.afw.image.TransmissionCurve` 

1080 A ``TransmissionCurve`` that represents the throughput of the filter 

1081 itself, to be evaluated in focal-plane coordinates. 

1082 - ``sensorTransmission`` : `lsst.afw.image.TransmissionCurve` 

1083 A ``TransmissionCurve`` that represents the throughput of the sensor 

1084 itself, to be evaluated in post-assembly trimmed detector coordinates. 

1085 - ``atmosphereTransmission`` : `lsst.afw.image.TransmissionCurve` 

1086 A ``TransmissionCurve`` that represents the throughput of the 

1087 atmosphere, assumed to be spatially constant. 

1088 - ``strayLightData`` : `object` 

1089 An opaque object containing calibration information for 

1090 stray-light correction. If `None`, no correction will be 

1091 performed. 

1092 - ``illumMaskedImage`` : illumination correction image (`lsst.afw.image.MaskedImage`) 

1093 

1094 Raises 

1095 ------ 

1096 NotImplementedError : 

1097 Raised if a per-amplifier brighter-fatter kernel is requested by the configuration. 

1098 """ 

1099 try: 

1100 dateObs = rawExposure.getInfo().getVisitInfo().getDate() 

1101 dateObs = dateObs.toPython().isoformat() 

1102 except RuntimeError: 

1103 self.log.warn("Unable to identify dateObs for rawExposure.") 

1104 dateObs = None 

1105 

1106 ccd = rawExposure.getDetector() 

1107 filterLabel = rawExposure.getFilterLabel() 

1108 rawExposure.mask.addMaskPlane("UNMASKEDNAN") # needed to match pre DM-15862 processing. 

1109 biasExposure = (self.getIsrExposure(dataRef, self.config.biasDataProductName) 

1110 if self.config.doBias else None) 

1111 # immediate=True required for functors and linearizers are functors; see ticket DM-6515 

1112 linearizer = (dataRef.get("linearizer", immediate=True) 

1113 if self.doLinearize(ccd) else None) 

1114 if linearizer is not None and not isinstance(linearizer, numpy.ndarray): 

1115 linearizer.log = self.log 

1116 if isinstance(linearizer, numpy.ndarray): 

1117 linearizer = linearize.Linearizer(table=linearizer, detector=ccd) 

1118 

1119 crosstalkCalib = None 

1120 if self.config.doCrosstalk: 

1121 try: 

1122 crosstalkCalib = dataRef.get("crosstalk", immediate=True) 

1123 except NoResults: 

1124 coeffVector = (self.config.crosstalk.crosstalkValues 

1125 if self.config.crosstalk.useConfigCoefficients else None) 

1126 crosstalkCalib = CrosstalkCalib().fromDetector(ccd, coeffVector=coeffVector) 

1127 crosstalkSources = (self.crosstalk.prepCrosstalk(dataRef, crosstalkCalib) 

1128 if self.config.doCrosstalk else None) 

1129 

1130 darkExposure = (self.getIsrExposure(dataRef, self.config.darkDataProductName) 

1131 if self.config.doDark else None) 

1132 flatExposure = (self.getIsrExposure(dataRef, self.config.flatDataProductName, 

1133 dateObs=dateObs) 

1134 if self.config.doFlat else None) 

1135 

1136 brighterFatterKernel = None 

1137 brighterFatterGains = None 

1138 if self.config.doBrighterFatter is True: 

1139 try: 

1140 # Use the new-style cp_pipe version of the kernel if it exists 

1141 # If using a new-style kernel, always use the self-consistent 

1142 # gains, i.e. the ones inside the kernel object itself 

1143 brighterFatterKernel = dataRef.get("brighterFatterKernel") 

1144 brighterFatterGains = brighterFatterKernel.gain 

1145 self.log.info("New style brighter-fatter kernel (brighterFatterKernel) loaded") 

1146 except NoResults: 

1147 try: # Fall back to the old-style numpy-ndarray style kernel if necessary. 

1148 brighterFatterKernel = dataRef.get("bfKernel") 

1149 self.log.info("Old style brighter-fatter kernel (np.array) loaded") 

1150 except NoResults: 

1151 brighterFatterKernel = None 

1152 if brighterFatterKernel is not None and not isinstance(brighterFatterKernel, numpy.ndarray): 

1153 # If the kernel is not an ndarray, it's the cp_pipe version 

1154 # so extract the kernel for this detector, or raise an error 

1155 if self.config.brighterFatterLevel == 'DETECTOR': 

1156 if brighterFatterKernel.detectorKernel: 

1157 brighterFatterKernel = brighterFatterKernel.detectorKernel[ccd.getId()] 

1158 else: 

1159 raise RuntimeError("Failed to extract kernel from new-style BF kernel.") 

1160 else: 

1161 # TODO DM-15631 for implementing this 

1162 raise NotImplementedError("Per-amplifier brighter-fatter correction not implemented") 

1163 

1164 defectList = (dataRef.get("defects") 

1165 if self.config.doDefect else None) 

1166 expId = rawExposure.getInfo().getVisitInfo().getExposureId() 

1167 fringeStruct = (self.fringe.readFringes(dataRef, expId=expId, assembler=self.assembleCcd 

1168 if self.config.doAssembleIsrExposures else None) 

1169 if self.config.doFringe and self.fringe.checkFilter(rawExposure) 

1170 else pipeBase.Struct(fringes=None)) 

1171 

1172 if self.config.doAttachTransmissionCurve: 

1173 opticsTransmission = (dataRef.get("transmission_optics") 

1174 if self.config.doUseOpticsTransmission else None) 

1175 filterTransmission = (dataRef.get("transmission_filter") 

1176 if self.config.doUseFilterTransmission else None) 

1177 sensorTransmission = (dataRef.get("transmission_sensor") 

1178 if self.config.doUseSensorTransmission else None) 

1179 atmosphereTransmission = (dataRef.get("transmission_atmosphere") 

1180 if self.config.doUseAtmosphereTransmission else None) 

1181 else: 

1182 opticsTransmission = None 

1183 filterTransmission = None 

1184 sensorTransmission = None 

1185 atmosphereTransmission = None 

1186 

1187 if self.config.doStrayLight: 

1188 strayLightData = self.strayLight.readIsrData(dataRef, rawExposure) 

1189 else: 

1190 strayLightData = None 

1191 

1192 illumMaskedImage = (self.getIsrExposure(dataRef, 

1193 self.config.illuminationCorrectionDataProductName).getMaskedImage() 

1194 if (self.config.doIlluminationCorrection 

1195 and filterLabel in self.config.illumFilters) 

1196 else None) 

1197 

1198 # Struct should include only kwargs to run() 

1199 return pipeBase.Struct(bias=biasExposure, 

1200 linearizer=linearizer, 

1201 crosstalk=crosstalkCalib, 

1202 crosstalkSources=crosstalkSources, 

1203 dark=darkExposure, 

1204 flat=flatExposure, 

1205 bfKernel=brighterFatterKernel, 

1206 bfGains=brighterFatterGains, 

1207 defects=defectList, 

1208 fringes=fringeStruct, 

1209 opticsTransmission=opticsTransmission, 

1210 filterTransmission=filterTransmission, 

1211 sensorTransmission=sensorTransmission, 

1212 atmosphereTransmission=atmosphereTransmission, 

1213 strayLightData=strayLightData, 

1214 illumMaskedImage=illumMaskedImage 

1215 ) 

1216 

1217 @pipeBase.timeMethod 

1218 def run(self, ccdExposure, *, camera=None, bias=None, linearizer=None, 

1219 crosstalk=None, crosstalkSources=None, 

1220 dark=None, flat=None, ptc=None, bfKernel=None, bfGains=None, defects=None, 

1221 fringes=pipeBase.Struct(fringes=None), opticsTransmission=None, filterTransmission=None, 

1222 sensorTransmission=None, atmosphereTransmission=None, 

1223 detectorNum=None, strayLightData=None, illumMaskedImage=None, 

1224 isGen3=False, 

1225 ): 

1226 """Perform instrument signature removal on an exposure. 

1227 

1228 Steps included in the ISR processing, in order performed, are: 

1229 - saturation and suspect pixel masking 

1230 - overscan subtraction 

1231 - CCD assembly of individual amplifiers 

1232 - bias subtraction 

1233 - variance image construction 

1234 - linearization of non-linear response 

1235 - crosstalk masking 

1236 - brighter-fatter correction 

1237 - dark subtraction 

1238 - fringe correction 

1239 - stray light subtraction 

1240 - flat correction 

1241 - masking of known defects and camera specific features 

1242 - vignette calculation 

1243 - appending transmission curve and distortion model 

1244 

1245 Parameters 

1246 ---------- 

1247 ccdExposure : `lsst.afw.image.Exposure` 

1248 The raw exposure that is to be run through ISR. The 

1249 exposure is modified by this method. 

1250 camera : `lsst.afw.cameraGeom.Camera`, optional 

1251 The camera geometry for this exposure. Required if ``isGen3`` is 

1252 `True` and one or more of ``ccdExposure``, ``bias``, ``dark``, or 

1253 ``flat`` does not have an associated detector. 

1254 bias : `lsst.afw.image.Exposure`, optional 

1255 Bias calibration frame. 

1256 linearizer : `lsst.ip.isr.linearize.LinearizeBase`, optional 

1257 Functor for linearization. 

1258 crosstalk : `lsst.ip.isr.crosstalk.CrosstalkCalib`, optional 

1259 Calibration for crosstalk. 

1260 crosstalkSources : `list`, optional 

1261 List of possible crosstalk sources. 

1262 dark : `lsst.afw.image.Exposure`, optional 

1263 Dark calibration frame. 

1264 flat : `lsst.afw.image.Exposure`, optional 

1265 Flat calibration frame. 

1266 ptc : `lsst.ip.isr.PhotonTransferCurveDataset`, optional 

1267 Photon transfer curve dataset, with, e.g., gains 

1268 and read noise. 

1269 bfKernel : `numpy.ndarray`, optional 

1270 Brighter-fatter kernel. 

1271 bfGains : `dict` of `float`, optional 

1272 Gains used to override the detector's nominal gains for the 

1273 brighter-fatter correction. A dict keyed by amplifier name for 

1274 the detector in question. 

1275 defects : `lsst.ip.isr.Defects`, optional 

1276 List of defects. 

1277 fringes : `lsst.pipe.base.Struct`, optional 

1278 Struct containing the fringe correction data, with 

1279 elements: 

1280 - ``fringes``: fringe calibration frame (`afw.image.Exposure`) 

1281 - ``seed``: random seed derived from the ccdExposureId for random 

1282 number generator (`uint32`) 

1283 opticsTransmission: `lsst.afw.image.TransmissionCurve`, optional 

1284 A ``TransmissionCurve`` that represents the throughput of the optics, 

1285 to be evaluated in focal-plane coordinates. 

1286 filterTransmission : `lsst.afw.image.TransmissionCurve` 

1287 A ``TransmissionCurve`` that represents the throughput of the filter 

1288 itself, to be evaluated in focal-plane coordinates. 

1289 sensorTransmission : `lsst.afw.image.TransmissionCurve` 

1290 A ``TransmissionCurve`` that represents the throughput of the sensor 

1291 itself, to be evaluated in post-assembly trimmed detector coordinates. 

1292 atmosphereTransmission : `lsst.afw.image.TransmissionCurve` 

1293 A ``TransmissionCurve`` that represents the throughput of the 

1294 atmosphere, assumed to be spatially constant. 

1295 detectorNum : `int`, optional 

1296 The integer number for the detector to process. 

1297 isGen3 : bool, optional 

1298 Flag this call to run() as using the Gen3 butler environment. 

1299 strayLightData : `object`, optional 

1300 Opaque object containing calibration information for stray-light 

1301 correction. If `None`, no correction will be performed. 

1302 illumMaskedImage : `lsst.afw.image.MaskedImage`, optional 

1303 Illumination correction image. 

1304 

1305 Returns 

1306 ------- 

1307 result : `lsst.pipe.base.Struct` 

1308 Result struct with component: 

1309 - ``exposure`` : `afw.image.Exposure` 

1310 The fully ISR corrected exposure. 

1311 - ``outputExposure`` : `afw.image.Exposure` 

1312 An alias for `exposure` 

1313 - ``ossThumb`` : `numpy.ndarray` 

1314 Thumbnail image of the exposure after overscan subtraction. 

1315 - ``flattenedThumb`` : `numpy.ndarray` 

1316 Thumbnail image of the exposure after flat-field correction. 

1317 

1318 Raises 

1319 ------ 

1320 RuntimeError 

1321 Raised if a configuration option is set to True, but the 

1322 required calibration data has not been specified. 

1323 

1324 Notes 

1325 ----- 

1326 The current processed exposure can be viewed by setting the 

1327 appropriate lsstDebug entries in the `debug.display` 

1328 dictionary. The names of these entries correspond to some of 

1329 the IsrTaskConfig Boolean options, with the value denoting the 

1330 frame to use. The exposure is shown inside the matching 

1331 option check and after the processing of that step has 

1332 finished. The steps with debug points are: 

1333 

1334 doAssembleCcd 

1335 doBias 

1336 doCrosstalk 

1337 doBrighterFatter 

1338 doDark 

1339 doFringe 

1340 doStrayLight 

1341 doFlat 

1342 

1343 In addition, setting the "postISRCCD" entry displays the 

1344 exposure after all ISR processing has finished. 

1345 

1346 """ 

1347 

1348 if isGen3 is True: 

1349 # Gen3 currently cannot automatically do configuration overrides. 

1350 # DM-15257 looks to discuss this issue. 

1351 # Configure input exposures; 

1352 if detectorNum is None: 

1353 raise RuntimeError("Must supply the detectorNum if running as Gen3.") 

1354 

1355 ccdExposure = self.ensureExposure(ccdExposure, camera, detectorNum) 

1356 bias = self.ensureExposure(bias, camera, detectorNum) 

1357 dark = self.ensureExposure(dark, camera, detectorNum) 

1358 flat = self.ensureExposure(flat, camera, detectorNum) 

1359 else: 

1360 if isinstance(ccdExposure, ButlerDataRef): 

1361 return self.runDataRef(ccdExposure) 

1362 

1363 ccd = ccdExposure.getDetector() 

1364 filterLabel = ccdExposure.getFilterLabel() 

1365 

1366 if not ccd: 

1367 assert not self.config.doAssembleCcd, "You need a Detector to run assembleCcd." 

1368 ccd = [FakeAmp(ccdExposure, self.config)] 

1369 

1370 # Validate Input 

1371 if self.config.doBias and bias is None: 

1372 raise RuntimeError("Must supply a bias exposure if config.doBias=True.") 

1373 if self.doLinearize(ccd) and linearizer is None: 

1374 raise RuntimeError("Must supply a linearizer if config.doLinearize=True for this detector.") 

1375 if self.config.doBrighterFatter and bfKernel is None: 

1376 raise RuntimeError("Must supply a kernel if config.doBrighterFatter=True.") 

1377 if self.config.doDark and dark is None: 

1378 raise RuntimeError("Must supply a dark exposure if config.doDark=True.") 

1379 if self.config.doFlat and flat is None: 

1380 raise RuntimeError("Must supply a flat exposure if config.doFlat=True.") 

1381 if self.config.doDefect and defects is None: 

1382 raise RuntimeError("Must supply defects if config.doDefect=True.") 

1383 if (self.config.doFringe and filterLabel in self.fringe.config.filters 

1384 and fringes.fringes is None): 

1385 # The `fringes` object needs to be a pipeBase.Struct, as 

1386 # we use it as a `dict` for the parameters of 

1387 # `FringeTask.run()`. The `fringes.fringes` `list` may 

1388 # not be `None` if `doFringe=True`. Otherwise, raise. 

1389 raise RuntimeError("Must supply fringe exposure as a pipeBase.Struct.") 

1390 if (self.config.doIlluminationCorrection and filterLabel in self.config.illumFilters 

1391 and illumMaskedImage is None): 

1392 raise RuntimeError("Must supply an illumcor if config.doIlluminationCorrection=True.") 

1393 

1394 # Begin ISR processing. 

1395 if self.config.doConvertIntToFloat: 

1396 self.log.info("Converting exposure to floating point values.") 

1397 ccdExposure = self.convertIntToFloat(ccdExposure) 

1398 

1399 if self.config.doBias and self.config.doBiasBeforeOverscan: 

1400 self.log.info("Applying bias correction.") 

1401 isrFunctions.biasCorrection(ccdExposure.getMaskedImage(), bias.getMaskedImage(), 

1402 trimToFit=self.config.doTrimToMatchCalib) 

1403 self.debugView(ccdExposure, "doBias") 

1404 

1405 # Amplifier level processing. 

1406 overscans = [] 

1407 for amp in ccd: 

1408 # if ccdExposure is one amp, check for coverage to prevent performing ops multiple times 

1409 if ccdExposure.getBBox().contains(amp.getBBox()): 

1410 # Check for fully masked bad amplifiers, and generate masks for SUSPECT and SATURATED values. 

1411 badAmp = self.maskAmplifier(ccdExposure, amp, defects) 

1412 

1413 if self.config.doOverscan and not badAmp: 

1414 # Overscan correction on amp-by-amp basis. 

1415 overscanResults = self.overscanCorrection(ccdExposure, amp) 

1416 self.log.debug("Corrected overscan for amplifier %s.", amp.getName()) 

1417 if overscanResults is not None and \ 

1418 self.config.qa is not None and self.config.qa.saveStats is True: 

1419 if isinstance(overscanResults.overscanFit, float): 

1420 qaMedian = overscanResults.overscanFit 

1421 qaStdev = float("NaN") 

1422 else: 

1423 qaStats = afwMath.makeStatistics(overscanResults.overscanFit, 

1424 afwMath.MEDIAN | afwMath.STDEVCLIP) 

1425 qaMedian = qaStats.getValue(afwMath.MEDIAN) 

1426 qaStdev = qaStats.getValue(afwMath.STDEVCLIP) 

1427 

1428 self.metadata.set(f"FIT MEDIAN {amp.getName()}", qaMedian) 

1429 self.metadata.set(f"FIT STDEV {amp.getName()}", qaStdev) 

1430 self.log.debug(" Overscan stats for amplifer %s: %f +/- %f", 

1431 amp.getName(), qaMedian, qaStdev) 

1432 

1433 # Residuals after overscan correction 

1434 qaStatsAfter = afwMath.makeStatistics(overscanResults.overscanImage, 

1435 afwMath.MEDIAN | afwMath.STDEVCLIP) 

1436 qaMedianAfter = qaStatsAfter.getValue(afwMath.MEDIAN) 

1437 qaStdevAfter = qaStatsAfter.getValue(afwMath.STDEVCLIP) 

1438 

1439 self.metadata.set(f"RESIDUAL MEDIAN {amp.getName()}", qaMedianAfter) 

1440 self.metadata.set(f"RESIDUAL STDEV {amp.getName()}", qaStdevAfter) 

1441 self.log.debug(" Overscan stats for amplifer %s after correction: %f +/- %f", 

1442 amp.getName(), qaMedianAfter, qaStdevAfter) 

1443 

1444 ccdExposure.getMetadata().set('OVERSCAN', "Overscan corrected") 

1445 else: 

1446 if badAmp: 

1447 self.log.warn("Amplifier %s is bad.", amp.getName()) 

1448 overscanResults = None 

1449 

1450 overscans.append(overscanResults if overscanResults is not None else None) 

1451 else: 

1452 self.log.info("Skipped OSCAN for %s.", amp.getName()) 

1453 

1454 if self.config.doCrosstalk and self.config.doCrosstalkBeforeAssemble: 

1455 self.log.info("Applying crosstalk correction.") 

1456 self.crosstalk.run(ccdExposure, crosstalk=crosstalk, 

1457 crosstalkSources=crosstalkSources, camera=camera) 

1458 self.debugView(ccdExposure, "doCrosstalk") 

1459 

1460 if self.config.doAssembleCcd: 

1461 self.log.info("Assembling CCD from amplifiers.") 

1462 ccdExposure = self.assembleCcd.assembleCcd(ccdExposure) 

1463 

1464 if self.config.expectWcs and not ccdExposure.getWcs(): 

1465 self.log.warn("No WCS found in input exposure.") 

1466 self.debugView(ccdExposure, "doAssembleCcd") 

1467 

1468 ossThumb = None 

1469 if self.config.qa.doThumbnailOss: 

1470 ossThumb = isrQa.makeThumbnail(ccdExposure, isrQaConfig=self.config.qa) 

1471 

1472 if self.config.doBias and not self.config.doBiasBeforeOverscan: 

1473 self.log.info("Applying bias correction.") 

1474 isrFunctions.biasCorrection(ccdExposure.getMaskedImage(), bias.getMaskedImage(), 

1475 trimToFit=self.config.doTrimToMatchCalib) 

1476 self.debugView(ccdExposure, "doBias") 

1477 

1478 if self.config.doVariance: 

1479 for amp, overscanResults in zip(ccd, overscans): 

1480 if ccdExposure.getBBox().contains(amp.getBBox()): 

1481 self.log.debug("Constructing variance map for amplifer %s.", amp.getName()) 

1482 ampExposure = ccdExposure.Factory(ccdExposure, amp.getBBox()) 

1483 if overscanResults is not None: 

1484 self.updateVariance(ampExposure, amp, 

1485 overscanImage=overscanResults.overscanImage, 

1486 ptcDataset=ptc) 

1487 else: 

1488 self.updateVariance(ampExposure, amp, 

1489 overscanImage=None, 

1490 ptcDataset=ptc) 

1491 if self.config.qa is not None and self.config.qa.saveStats is True: 

1492 qaStats = afwMath.makeStatistics(ampExposure.getVariance(), 

1493 afwMath.MEDIAN | afwMath.STDEVCLIP) 

1494 self.metadata.set(f"ISR VARIANCE {amp.getName()} MEDIAN", 

1495 qaStats.getValue(afwMath.MEDIAN)) 

1496 self.metadata.set(f"ISR VARIANCE {amp.getName()} STDEV", 

1497 qaStats.getValue(afwMath.STDEVCLIP)) 

1498 self.log.debug(" Variance stats for amplifer %s: %f +/- %f.", 

1499 amp.getName(), qaStats.getValue(afwMath.MEDIAN), 

1500 qaStats.getValue(afwMath.STDEVCLIP)) 

1501 

1502 if self.doLinearize(ccd): 

1503 self.log.info("Applying linearizer.") 

1504 linearizer.applyLinearity(image=ccdExposure.getMaskedImage().getImage(), 

1505 detector=ccd, log=self.log) 

1506 

1507 if self.config.doCrosstalk and not self.config.doCrosstalkBeforeAssemble: 

1508 self.log.info("Applying crosstalk correction.") 

1509 self.crosstalk.run(ccdExposure, crosstalk=crosstalk, 

1510 crosstalkSources=crosstalkSources, isTrimmed=True) 

1511 self.debugView(ccdExposure, "doCrosstalk") 

1512 

1513 # Masking block. Optionally mask known defects, NAN/inf pixels, widen trails, and do 

1514 # anything else the camera needs. Saturated and suspect pixels have already been masked. 

1515 if self.config.doDefect: 

1516 self.log.info("Masking defects.") 

1517 self.maskDefect(ccdExposure, defects) 

1518 

1519 if self.config.numEdgeSuspect > 0: 

1520 self.log.info("Masking edges as SUSPECT.") 

1521 self.maskEdges(ccdExposure, numEdgePixels=self.config.numEdgeSuspect, 

1522 maskPlane="SUSPECT", level=self.config.edgeMaskLevel) 

1523 

1524 if self.config.doNanMasking: 

1525 self.log.info("Masking non-finite (NAN, inf) value pixels.") 

1526 self.maskNan(ccdExposure) 

1527 

1528 if self.config.doWidenSaturationTrails: 

1529 self.log.info("Widening saturation trails.") 

1530 isrFunctions.widenSaturationTrails(ccdExposure.getMaskedImage().getMask()) 

1531 

1532 if self.config.doCameraSpecificMasking: 

1533 self.log.info("Masking regions for camera specific reasons.") 

1534 self.masking.run(ccdExposure) 

1535 

1536 if self.config.doBrighterFatter: 

1537 # We need to apply flats and darks before we can interpolate, and we 

1538 # need to interpolate before we do B-F, but we do B-F without the 

1539 # flats and darks applied so we can work in units of electrons or holes. 

1540 # This context manager applies and then removes the darks and flats. 

1541 # 

1542 # We also do not want to interpolate values here, so operate on temporary 

1543 # images so we can apply only the BF-correction and roll back the 

1544 # interpolation. 

1545 interpExp = ccdExposure.clone() 

1546 with self.flatContext(interpExp, flat, dark): 

1547 isrFunctions.interpolateFromMask( 

1548 maskedImage=interpExp.getMaskedImage(), 

1549 fwhm=self.config.fwhm, 

1550 growSaturatedFootprints=self.config.growSaturationFootprintSize, 

1551 maskNameList=list(self.config.brighterFatterMaskListToInterpolate) 

1552 ) 

1553 bfExp = interpExp.clone() 

1554 

1555 self.log.info("Applying brighter-fatter correction using kernel type %s / gains %s.", 

1556 type(bfKernel), type(bfGains)) 

1557 bfResults = isrFunctions.brighterFatterCorrection(bfExp, bfKernel, 

1558 self.config.brighterFatterMaxIter, 

1559 self.config.brighterFatterThreshold, 

1560 self.config.brighterFatterApplyGain, 

1561 bfGains) 

1562 if bfResults[1] == self.config.brighterFatterMaxIter: 

1563 self.log.warn("Brighter-fatter correction did not converge, final difference %f.", 

1564 bfResults[0]) 

1565 else: 

1566 self.log.info("Finished brighter-fatter correction in %d iterations.", 

1567 bfResults[1]) 

1568 image = ccdExposure.getMaskedImage().getImage() 

1569 bfCorr = bfExp.getMaskedImage().getImage() 

1570 bfCorr -= interpExp.getMaskedImage().getImage() 

1571 image += bfCorr 

1572 

1573 # Applying the brighter-fatter correction applies a 

1574 # convolution to the science image. At the edges this 

1575 # convolution may not have sufficient valid pixels to 

1576 # produce a valid correction. Mark pixels within the size 

1577 # of the brighter-fatter kernel as EDGE to warn of this 

1578 # fact. 

1579 self.log.info("Ensuring image edges are masked as EDGE to the brighter-fatter kernel size.") 

1580 self.maskEdges(ccdExposure, numEdgePixels=numpy.max(bfKernel.shape) // 2, 

1581 maskPlane="EDGE") 

1582 

1583 if self.config.brighterFatterMaskGrowSize > 0: 

1584 self.log.info("Growing masks to account for brighter-fatter kernel convolution.") 

1585 for maskPlane in self.config.brighterFatterMaskListToInterpolate: 

1586 isrFunctions.growMasks(ccdExposure.getMask(), 

1587 radius=self.config.brighterFatterMaskGrowSize, 

1588 maskNameList=maskPlane, 

1589 maskValue=maskPlane) 

1590 

1591 self.debugView(ccdExposure, "doBrighterFatter") 

1592 

1593 if self.config.doDark: 

1594 self.log.info("Applying dark correction.") 

1595 self.darkCorrection(ccdExposure, dark) 

1596 self.debugView(ccdExposure, "doDark") 

1597 

1598 if self.config.doFringe and not self.config.fringeAfterFlat: 

1599 self.log.info("Applying fringe correction before flat.") 

1600 self.fringe.run(ccdExposure, **fringes.getDict()) 

1601 self.debugView(ccdExposure, "doFringe") 

1602 

1603 if self.config.doStrayLight and self.strayLight.check(ccdExposure): 

1604 self.log.info("Checking strayLight correction.") 

1605 self.strayLight.run(ccdExposure, strayLightData) 

1606 self.debugView(ccdExposure, "doStrayLight") 

1607 

1608 if self.config.doFlat: 

1609 self.log.info("Applying flat correction.") 

1610 self.flatCorrection(ccdExposure, flat) 

1611 self.debugView(ccdExposure, "doFlat") 

1612 

1613 if self.config.doApplyGains: 

1614 self.log.info("Applying gain correction instead of flat.") 

1615 if self.config.usePtcGains: 

1616 self.log.info("Using gains from the Photon Transfer Curve.") 

1617 isrFunctions.applyGains(ccdExposure, self.config.normalizeGains, 

1618 ptcGains=ptc.gain) 

1619 else: 

1620 isrFunctions.applyGains(ccdExposure, self.config.normalizeGains) 

1621 

1622 if self.config.doFringe and self.config.fringeAfterFlat: 

1623 self.log.info("Applying fringe correction after flat.") 

1624 self.fringe.run(ccdExposure, **fringes.getDict()) 

1625 

1626 if self.config.doVignette: 

1627 self.log.info("Constructing Vignette polygon.") 

1628 self.vignettePolygon = self.vignette.run(ccdExposure) 

1629 

1630 if self.config.vignette.doWriteVignettePolygon: 

1631 self.setValidPolygonIntersect(ccdExposure, self.vignettePolygon) 

1632 

1633 if self.config.doAttachTransmissionCurve: 

1634 self.log.info("Adding transmission curves.") 

1635 isrFunctions.attachTransmissionCurve(ccdExposure, opticsTransmission=opticsTransmission, 

1636 filterTransmission=filterTransmission, 

1637 sensorTransmission=sensorTransmission, 

1638 atmosphereTransmission=atmosphereTransmission) 

1639 

1640 flattenedThumb = None 

1641 if self.config.qa.doThumbnailFlattened: 

1642 flattenedThumb = isrQa.makeThumbnail(ccdExposure, isrQaConfig=self.config.qa) 

1643 

1644 if self.config.doIlluminationCorrection and filterLabel in self.config.illumFilters: 

1645 self.log.info("Performing illumination correction.") 

1646 isrFunctions.illuminationCorrection(ccdExposure.getMaskedImage(), 

1647 illumMaskedImage, illumScale=self.config.illumScale, 

1648 trimToFit=self.config.doTrimToMatchCalib) 

1649 

1650 preInterpExp = None 

1651 if self.config.doSaveInterpPixels: 

1652 preInterpExp = ccdExposure.clone() 

1653 

1654 # Reset and interpolate bad pixels. 

1655 # 

1656 # Large contiguous bad regions (which should have the BAD mask 

1657 # bit set) should have their values set to the image median. 

1658 # This group should include defects and bad amplifiers. As the 

1659 # area covered by these defects are large, there's little 

1660 # reason to expect that interpolation would provide a more 

1661 # useful value. 

1662 # 

1663 # Smaller defects can be safely interpolated after the larger 

1664 # regions have had their pixel values reset. This ensures 

1665 # that the remaining defects adjacent to bad amplifiers (as an 

1666 # example) do not attempt to interpolate extreme values. 

1667 if self.config.doSetBadRegions: 

1668 badPixelCount, badPixelValue = isrFunctions.setBadRegions(ccdExposure) 

1669 if badPixelCount > 0: 

1670 self.log.info("Set %d BAD pixels to %f.", badPixelCount, badPixelValue) 

1671 

1672 if self.config.doInterpolate: 

1673 self.log.info("Interpolating masked pixels.") 

1674 isrFunctions.interpolateFromMask( 

1675 maskedImage=ccdExposure.getMaskedImage(), 

1676 fwhm=self.config.fwhm, 

1677 growSaturatedFootprints=self.config.growSaturationFootprintSize, 

1678 maskNameList=list(self.config.maskListToInterpolate) 

1679 ) 

1680 

1681 self.roughZeroPoint(ccdExposure) 

1682 

1683 if self.config.doMeasureBackground: 

1684 self.log.info("Measuring background level.") 

1685 self.measureBackground(ccdExposure, self.config.qa) 

1686 

1687 if self.config.qa is not None and self.config.qa.saveStats is True: 

1688 for amp in ccd: 

1689 ampExposure = ccdExposure.Factory(ccdExposure, amp.getBBox()) 

1690 qaStats = afwMath.makeStatistics(ampExposure.getImage(), 

1691 afwMath.MEDIAN | afwMath.STDEVCLIP) 

1692 self.metadata.set("ISR BACKGROUND {} MEDIAN".format(amp.getName()), 

1693 qaStats.getValue(afwMath.MEDIAN)) 

1694 self.metadata.set("ISR BACKGROUND {} STDEV".format(amp.getName()), 

1695 qaStats.getValue(afwMath.STDEVCLIP)) 

1696 self.log.debug(" Background stats for amplifer %s: %f +/- %f", 

1697 amp.getName(), qaStats.getValue(afwMath.MEDIAN), 

1698 qaStats.getValue(afwMath.STDEVCLIP)) 

1699 

1700 self.debugView(ccdExposure, "postISRCCD") 

1701 

1702 return pipeBase.Struct( 

1703 exposure=ccdExposure, 

1704 ossThumb=ossThumb, 

1705 flattenedThumb=flattenedThumb, 

1706 

1707 preInterpolatedExposure=preInterpExp, 

1708 outputExposure=ccdExposure, 

1709 outputOssThumbnail=ossThumb, 

1710 outputFlattenedThumbnail=flattenedThumb, 

1711 ) 

1712 

1713 @pipeBase.timeMethod 

1714 def runDataRef(self, sensorRef): 

1715 """Perform instrument signature removal on a ButlerDataRef of a Sensor. 

1716 

1717 This method contains the `CmdLineTask` interface to the ISR 

1718 processing. All IO is handled here, freeing the `run()` method 

1719 to manage only pixel-level calculations. The steps performed 

1720 are: 

1721 - Read in necessary detrending/isr/calibration data. 

1722 - Process raw exposure in `run()`. 

1723 - Persist the ISR-corrected exposure as "postISRCCD" if 

1724 config.doWrite=True. 

1725 

1726 Parameters 

1727 ---------- 

1728 sensorRef : `daf.persistence.butlerSubset.ButlerDataRef` 

1729 DataRef of the detector data to be processed 

1730 

1731 Returns 

1732 ------- 

1733 result : `lsst.pipe.base.Struct` 

1734 Result struct with component: 

1735 - ``exposure`` : `afw.image.Exposure` 

1736 The fully ISR corrected exposure. 

1737 

1738 Raises 

1739 ------ 

1740 RuntimeError 

1741 Raised if a configuration option is set to True, but the 

1742 required calibration data does not exist. 

1743 

1744 """ 

1745 self.log.info("Performing ISR on sensor %s.", sensorRef.dataId) 

1746 

1747 ccdExposure = sensorRef.get(self.config.datasetType) 

1748 

1749 camera = sensorRef.get("camera") 

1750 isrData = self.readIsrData(sensorRef, ccdExposure) 

1751 

1752 result = self.run(ccdExposure, camera=camera, **isrData.getDict()) 

1753 

1754 if self.config.doWrite: 

1755 sensorRef.put(result.exposure, "postISRCCD") 

1756 if result.preInterpolatedExposure is not None: 

1757 sensorRef.put(result.preInterpolatedExposure, "postISRCCD_uninterpolated") 

1758 if result.ossThumb is not None: 

1759 isrQa.writeThumbnail(sensorRef, result.ossThumb, "ossThumb") 

1760 if result.flattenedThumb is not None: 

1761 isrQa.writeThumbnail(sensorRef, result.flattenedThumb, "flattenedThumb") 

1762 

1763 return result 

1764 

1765 def getIsrExposure(self, dataRef, datasetType, dateObs=None, immediate=True): 

1766 """Retrieve a calibration dataset for removing instrument signature. 

1767 

1768 Parameters 

1769 ---------- 

1770 

1771 dataRef : `daf.persistence.butlerSubset.ButlerDataRef` 

1772 DataRef of the detector data to find calibration datasets 

1773 for. 

1774 datasetType : `str` 

1775 Type of dataset to retrieve (e.g. 'bias', 'flat', etc). 

1776 dateObs : `str`, optional 

1777 Date of the observation. Used to correct butler failures 

1778 when using fallback filters. 

1779 immediate : `Bool` 

1780 If True, disable butler proxies to enable error handling 

1781 within this routine. 

1782 

1783 Returns 

1784 ------- 

1785 exposure : `lsst.afw.image.Exposure` 

1786 Requested calibration frame. 

1787 

1788 Raises 

1789 ------ 

1790 RuntimeError 

1791 Raised if no matching calibration frame can be found. 

1792 """ 

1793 try: 

1794 exp = dataRef.get(datasetType, immediate=immediate) 

1795 except Exception as exc1: 

1796 if not self.config.fallbackFilterName: 

1797 raise RuntimeError("Unable to retrieve %s for %s: %s." % (datasetType, dataRef.dataId, exc1)) 

1798 try: 

1799 if self.config.useFallbackDate and dateObs: 

1800 exp = dataRef.get(datasetType, filter=self.config.fallbackFilterName, 

1801 dateObs=dateObs, immediate=immediate) 

1802 else: 

1803 exp = dataRef.get(datasetType, filter=self.config.fallbackFilterName, immediate=immediate) 

1804 except Exception as exc2: 

1805 raise RuntimeError("Unable to retrieve %s for %s, even with fallback filter %s: %s AND %s." % 

1806 (datasetType, dataRef.dataId, self.config.fallbackFilterName, exc1, exc2)) 

1807 self.log.warn("Using fallback calibration from filter %s.", self.config.fallbackFilterName) 

1808 

1809 if self.config.doAssembleIsrExposures: 

1810 exp = self.assembleCcd.assembleCcd(exp) 

1811 return exp 

1812 

1813 def ensureExposure(self, inputExp, camera, detectorNum): 

1814 """Ensure that the data returned by Butler is a fully constructed exposure. 

1815 

1816 ISR requires exposure-level image data for historical reasons, so if we did 

1817 not recieve that from Butler, construct it from what we have, modifying the 

1818 input in place. 

1819 

1820 Parameters 

1821 ---------- 

1822 inputExp : `lsst.afw.image.Exposure`, `lsst.afw.image.DecoratedImageU`, or 

1823 `lsst.afw.image.ImageF` 

1824 The input data structure obtained from Butler. 

1825 camera : `lsst.afw.cameraGeom.camera` 

1826 The camera associated with the image. Used to find the appropriate 

1827 detector. 

1828 detectorNum : `int` 

1829 The detector this exposure should match. 

1830 

1831 Returns 

1832 ------- 

1833 inputExp : `lsst.afw.image.Exposure` 

1834 The re-constructed exposure, with appropriate detector parameters. 

1835 

1836 Raises 

1837 ------ 

1838 TypeError 

1839 Raised if the input data cannot be used to construct an exposure. 

1840 """ 

1841 if isinstance(inputExp, afwImage.DecoratedImageU): 

1842 inputExp = afwImage.makeExposure(afwImage.makeMaskedImage(inputExp)) 

1843 elif isinstance(inputExp, afwImage.ImageF): 

1844 inputExp = afwImage.makeExposure(afwImage.makeMaskedImage(inputExp)) 

1845 elif isinstance(inputExp, afwImage.MaskedImageF): 

1846 inputExp = afwImage.makeExposure(inputExp) 

1847 elif isinstance(inputExp, afwImage.Exposure): 

1848 pass 

1849 elif inputExp is None: 

1850 # Assume this will be caught by the setup if it is a problem. 

1851 return inputExp 

1852 else: 

1853 raise TypeError("Input Exposure is not known type in isrTask.ensureExposure: %s." % 

1854 (type(inputExp), )) 

1855 

1856 if inputExp.getDetector() is None: 

1857 inputExp.setDetector(camera[detectorNum]) 

1858 

1859 return inputExp 

1860 

1861 def convertIntToFloat(self, exposure): 

1862 """Convert exposure image from uint16 to float. 

1863 

1864 If the exposure does not need to be converted, the input is 

1865 immediately returned. For exposures that are converted to use 

1866 floating point pixels, the variance is set to unity and the 

1867 mask to zero. 

1868 

1869 Parameters 

1870 ---------- 

1871 exposure : `lsst.afw.image.Exposure` 

1872 The raw exposure to be converted. 

1873 

1874 Returns 

1875 ------- 

1876 newexposure : `lsst.afw.image.Exposure` 

1877 The input ``exposure``, converted to floating point pixels. 

1878 

1879 Raises 

1880 ------ 

1881 RuntimeError 

1882 Raised if the exposure type cannot be converted to float. 

1883 

1884 """ 

1885 if isinstance(exposure, afwImage.ExposureF): 

1886 # Nothing to be done 

1887 self.log.debug("Exposure already of type float.") 

1888 return exposure 

1889 if not hasattr(exposure, "convertF"): 

1890 raise RuntimeError("Unable to convert exposure (%s) to float." % type(exposure)) 

1891 

1892 newexposure = exposure.convertF() 

1893 newexposure.variance[:] = 1 

1894 newexposure.mask[:] = 0x0 

1895 

1896 return newexposure 

1897 

1898 def maskAmplifier(self, ccdExposure, amp, defects): 

1899 """Identify bad amplifiers, saturated and suspect pixels. 

1900 

1901 Parameters 

1902 ---------- 

1903 ccdExposure : `lsst.afw.image.Exposure` 

1904 Input exposure to be masked. 

1905 amp : `lsst.afw.table.AmpInfoCatalog` 

1906 Catalog of parameters defining the amplifier on this 

1907 exposure to mask. 

1908 defects : `lsst.ip.isr.Defects` 

1909 List of defects. Used to determine if the entire 

1910 amplifier is bad. 

1911 

1912 Returns 

1913 ------- 

1914 badAmp : `Bool` 

1915 If this is true, the entire amplifier area is covered by 

1916 defects and unusable. 

1917 

1918 """ 

1919 maskedImage = ccdExposure.getMaskedImage() 

1920 

1921 badAmp = False 

1922 

1923 # Check if entire amp region is defined as a defect (need to use amp.getBBox() for correct 

1924 # comparison with current defects definition. 

1925 if defects is not None: 

1926 badAmp = bool(sum([v.getBBox().contains(amp.getBBox()) for v in defects])) 

1927 

1928 # In the case of a bad amp, we will set mask to "BAD" (here use amp.getRawBBox() for correct 

1929 # association with pixels in current ccdExposure). 

1930 if badAmp: 

1931 dataView = afwImage.MaskedImageF(maskedImage, amp.getRawBBox(), 

1932 afwImage.PARENT) 

1933 maskView = dataView.getMask() 

1934 maskView |= maskView.getPlaneBitMask("BAD") 

1935 del maskView 

1936 return badAmp 

1937 

1938 # Mask remaining defects after assembleCcd() to allow for defects that cross amplifier boundaries. 

1939 # Saturation and suspect pixels can be masked now, though. 

1940 limits = dict() 

1941 if self.config.doSaturation and not badAmp: 

1942 limits.update({self.config.saturatedMaskName: amp.getSaturation()}) 

1943 if self.config.doSuspect and not badAmp: 

1944 limits.update({self.config.suspectMaskName: amp.getSuspectLevel()}) 

1945 if math.isfinite(self.config.saturation): 

1946 limits.update({self.config.saturatedMaskName: self.config.saturation}) 

1947 

1948 for maskName, maskThreshold in limits.items(): 

1949 if not math.isnan(maskThreshold): 

1950 dataView = maskedImage.Factory(maskedImage, amp.getRawBBox()) 

1951 isrFunctions.makeThresholdMask( 

1952 maskedImage=dataView, 

1953 threshold=maskThreshold, 

1954 growFootprints=0, 

1955 maskName=maskName 

1956 ) 

1957 

1958 # Determine if we've fully masked this amplifier with SUSPECT and SAT pixels. 

1959 maskView = afwImage.Mask(maskedImage.getMask(), amp.getRawDataBBox(), 

1960 afwImage.PARENT) 

1961 maskVal = maskView.getPlaneBitMask([self.config.saturatedMaskName, 

1962 self.config.suspectMaskName]) 

1963 if numpy.all(maskView.getArray() & maskVal > 0): 

1964 badAmp = True 

1965 maskView |= maskView.getPlaneBitMask("BAD") 

1966 

1967 return badAmp 

1968 

1969 def overscanCorrection(self, ccdExposure, amp): 

1970 """Apply overscan correction in place. 

1971 

1972 This method does initial pixel rejection of the overscan 

1973 region. The overscan can also be optionally segmented to 

1974 allow for discontinuous overscan responses to be fit 

1975 separately. The actual overscan subtraction is performed by 

1976 the `lsst.ip.isr.isrFunctions.overscanCorrection` function, 

1977 which is called here after the amplifier is preprocessed. 

1978 

1979 Parameters 

1980 ---------- 

1981 ccdExposure : `lsst.afw.image.Exposure` 

1982 Exposure to have overscan correction performed. 

1983 amp : `lsst.afw.cameraGeom.Amplifer` 

1984 The amplifier to consider while correcting the overscan. 

1985 

1986 Returns 

1987 ------- 

1988 overscanResults : `lsst.pipe.base.Struct` 

1989 Result struct with components: 

1990 - ``imageFit`` : scalar or `lsst.afw.image.Image` 

1991 Value or fit subtracted from the amplifier image data. 

1992 - ``overscanFit`` : scalar or `lsst.afw.image.Image` 

1993 Value or fit subtracted from the overscan image data. 

1994 - ``overscanImage`` : `lsst.afw.image.Image` 

1995 Image of the overscan region with the overscan 

1996 correction applied. This quantity is used to estimate 

1997 the amplifier read noise empirically. 

1998 

1999 Raises 

2000 ------ 

2001 RuntimeError 

2002 Raised if the ``amp`` does not contain raw pixel information. 

2003 

2004 See Also 

2005 -------- 

2006 lsst.ip.isr.isrFunctions.overscanCorrection 

2007 """ 

2008 if amp.getRawHorizontalOverscanBBox().isEmpty(): 

2009 self.log.info("ISR_OSCAN: No overscan region. Not performing overscan correction.") 

2010 return None 

2011 

2012 statControl = afwMath.StatisticsControl() 

2013 statControl.setAndMask(ccdExposure.mask.getPlaneBitMask("SAT")) 

2014 

2015 # Determine the bounding boxes 

2016 dataBBox = amp.getRawDataBBox() 

2017 oscanBBox = amp.getRawHorizontalOverscanBBox() 

2018 dx0 = 0 

2019 dx1 = 0 

2020 

2021 prescanBBox = amp.getRawPrescanBBox() 

2022 if (oscanBBox.getBeginX() > prescanBBox.getBeginX()): # amp is at the right 

2023 dx0 += self.config.overscanNumLeadingColumnsToSkip 

2024 dx1 -= self.config.overscanNumTrailingColumnsToSkip 

2025 else: 

2026 dx0 += self.config.overscanNumTrailingColumnsToSkip 

2027 dx1 -= self.config.overscanNumLeadingColumnsToSkip 

2028 

2029 # Determine if we need to work on subregions of the amplifier and overscan. 

2030 imageBBoxes = [] 

2031 overscanBBoxes = [] 

2032 

2033 if ((self.config.overscanBiasJump 

2034 and self.config.overscanBiasJumpLocation) 

2035 and (ccdExposure.getMetadata().exists(self.config.overscanBiasJumpKeyword) 

2036 and ccdExposure.getMetadata().getScalar(self.config.overscanBiasJumpKeyword) in 

2037 self.config.overscanBiasJumpDevices)): 

2038 if amp.getReadoutCorner() in (ReadoutCorner.LL, ReadoutCorner.LR): 

2039 yLower = self.config.overscanBiasJumpLocation 

2040 yUpper = dataBBox.getHeight() - yLower 

2041 else: 

2042 yUpper = self.config.overscanBiasJumpLocation 

2043 yLower = dataBBox.getHeight() - yUpper 

2044 

2045 imageBBoxes.append(lsst.geom.Box2I(dataBBox.getBegin(), 

2046 lsst.geom.Extent2I(dataBBox.getWidth(), yLower))) 

2047 overscanBBoxes.append(lsst.geom.Box2I(oscanBBox.getBegin() + lsst.geom.Extent2I(dx0, 0), 

2048 lsst.geom.Extent2I(oscanBBox.getWidth() - dx0 + dx1, 

2049 yLower))) 

2050 

2051 imageBBoxes.append(lsst.geom.Box2I(dataBBox.getBegin() + lsst.geom.Extent2I(0, yLower), 

2052 lsst.geom.Extent2I(dataBBox.getWidth(), yUpper))) 

2053 overscanBBoxes.append(lsst.geom.Box2I(oscanBBox.getBegin() + lsst.geom.Extent2I(dx0, yLower), 

2054 lsst.geom.Extent2I(oscanBBox.getWidth() - dx0 + dx1, 

2055 yUpper))) 

2056 else: 

2057 imageBBoxes.append(lsst.geom.Box2I(dataBBox.getBegin(), 

2058 lsst.geom.Extent2I(dataBBox.getWidth(), dataBBox.getHeight()))) 

2059 overscanBBoxes.append(lsst.geom.Box2I(oscanBBox.getBegin() + lsst.geom.Extent2I(dx0, 0), 

2060 lsst.geom.Extent2I(oscanBBox.getWidth() - dx0 + dx1, 

2061 oscanBBox.getHeight()))) 

2062 

2063 # Perform overscan correction on subregions, ensuring saturated pixels are masked. 

2064 for imageBBox, overscanBBox in zip(imageBBoxes, overscanBBoxes): 

2065 ampImage = ccdExposure.maskedImage[imageBBox] 

2066 overscanImage = ccdExposure.maskedImage[overscanBBox] 

2067 

2068 overscanArray = overscanImage.image.array 

2069 median = numpy.ma.median(numpy.ma.masked_where(overscanImage.mask.array, overscanArray)) 

2070 bad = numpy.where(numpy.abs(overscanArray - median) > self.config.overscanMaxDev) 

2071 overscanImage.mask.array[bad] = overscanImage.mask.getPlaneBitMask("SAT") 

2072 

2073 statControl = afwMath.StatisticsControl() 

2074 statControl.setAndMask(ccdExposure.mask.getPlaneBitMask("SAT")) 

2075 

2076 overscanResults = self.overscan.run(ampImage.getImage(), overscanImage, amp) 

2077 

2078 # Measure average overscan levels and record them in the metadata. 

2079 levelStat = afwMath.MEDIAN 

2080 sigmaStat = afwMath.STDEVCLIP 

2081 

2082 sctrl = afwMath.StatisticsControl(self.config.qa.flatness.clipSigma, 

2083 self.config.qa.flatness.nIter) 

2084 metadata = ccdExposure.getMetadata() 

2085 ampNum = amp.getName() 

2086 # if self.config.overscanFitType in ("MEDIAN", "MEAN", "MEANCLIP"): 

2087 if isinstance(overscanResults.overscanFit, float): 

2088 metadata.set("ISR_OSCAN_LEVEL%s" % ampNum, overscanResults.overscanFit) 

2089 metadata.set("ISR_OSCAN_SIGMA%s" % ampNum, 0.0) 

2090 else: 

2091 stats = afwMath.makeStatistics(overscanResults.overscanFit, levelStat | sigmaStat, sctrl) 

2092 metadata.set("ISR_OSCAN_LEVEL%s" % ampNum, stats.getValue(levelStat)) 

2093 metadata.set("ISR_OSCAN_SIGMA%s" % ampNum, stats.getValue(sigmaStat)) 

2094 

2095 return overscanResults 

2096 

2097 def updateVariance(self, ampExposure, amp, overscanImage=None, ptcDataset=None): 

2098 """Set the variance plane using the gain and read noise 

2099 

2100 The read noise is calculated from the ``overscanImage`` if the 

2101 ``doEmpiricalReadNoise`` option is set in the configuration; otherwise 

2102 the value from the amplifier data is used. 

2103 

2104 Parameters 

2105 ---------- 

2106 ampExposure : `lsst.afw.image.Exposure` 

2107 Exposure to process. 

2108 amp : `lsst.afw.table.AmpInfoRecord` or `FakeAmp` 

2109 Amplifier detector data. 

2110 overscanImage : `lsst.afw.image.MaskedImage`, optional. 

2111 Image of overscan, required only for empirical read noise. 

2112 ptcDataset : `lsst.ip.isr.PhotonTransferCurveDataset`, optional 

2113 PTC dataset containing the gains and read noise. 

2114 

2115 

2116 Raises 

2117 ------ 

2118 RuntimeError 

2119 Raised if either ``usePtcGains`` of ``usePtcReadNoise`` 

2120 are ``True``, but ptcDataset is not provided. 

2121 

2122 Raised if ```doEmpiricalReadNoise`` is ``True`` but 

2123 ``overscanImage`` is ``None``. 

2124 

2125 See also 

2126 -------- 

2127 lsst.ip.isr.isrFunctions.updateVariance 

2128 """ 

2129 maskPlanes = [self.config.saturatedMaskName, self.config.suspectMaskName] 

2130 if self.config.usePtcGains: 

2131 if ptcDataset is None: 

2132 raise RuntimeError("No ptcDataset provided to use PTC gains.") 

2133 else: 

2134 gain = ptcDataset.gain[amp.getName()] 

2135 self.log.info("Using gain from Photon Transfer Curve.") 

2136 else: 

2137 gain = amp.getGain() 

2138 

2139 if math.isnan(gain): 

2140 gain = 1.0 

2141 self.log.warn("Gain set to NAN! Updating to 1.0 to generate Poisson variance.") 

2142 elif gain <= 0: 

2143 patchedGain = 1.0 

2144 self.log.warn("Gain for amp %s == %g <= 0; setting to %f.", 

2145 amp.getName(), gain, patchedGain) 

2146 gain = patchedGain 

2147 

2148 if self.config.doEmpiricalReadNoise and overscanImage is None: 

2149 raise RuntimeError("Overscan is none for EmpiricalReadNoise.") 

2150 

2151 if self.config.doEmpiricalReadNoise and overscanImage is not None: 

2152 stats = afwMath.StatisticsControl() 

2153 stats.setAndMask(overscanImage.mask.getPlaneBitMask(maskPlanes)) 

2154 readNoise = afwMath.makeStatistics(overscanImage, afwMath.STDEVCLIP, stats).getValue() 

2155 self.log.info("Calculated empirical read noise for amp %s: %f.", 

2156 amp.getName(), readNoise) 

2157 elif self.config.usePtcReadNoise: 

2158 if ptcDataset is None: 

2159 raise RuntimeError("No ptcDataset provided to use PTC readnoise.") 

2160 else: 

2161 readNoise = ptcDataset.noise[amp.getName()] 

2162 self.log.info("Using read noise from Photon Transfer Curve.") 

2163 else: 

2164 readNoise = amp.getReadNoise() 

2165 

2166 isrFunctions.updateVariance( 

2167 maskedImage=ampExposure.getMaskedImage(), 

2168 gain=gain, 

2169 readNoise=readNoise, 

2170 ) 

2171 

2172 def darkCorrection(self, exposure, darkExposure, invert=False): 

2173 """Apply dark correction in place. 

2174 

2175 Parameters 

2176 ---------- 

2177 exposure : `lsst.afw.image.Exposure` 

2178 Exposure to process. 

2179 darkExposure : `lsst.afw.image.Exposure` 

2180 Dark exposure of the same size as ``exposure``. 

2181 invert : `Bool`, optional 

2182 If True, re-add the dark to an already corrected image. 

2183 

2184 Raises 

2185 ------ 

2186 RuntimeError 

2187 Raised if either ``exposure`` or ``darkExposure`` do not 

2188 have their dark time defined. 

2189 

2190 See Also 

2191 -------- 

2192 lsst.ip.isr.isrFunctions.darkCorrection 

2193 """ 

2194 expScale = exposure.getInfo().getVisitInfo().getDarkTime() 

2195 if math.isnan(expScale): 

2196 raise RuntimeError("Exposure darktime is NAN.") 

2197 if darkExposure.getInfo().getVisitInfo() is not None \ 

2198 and not math.isnan(darkExposure.getInfo().getVisitInfo().getDarkTime()): 

2199 darkScale = darkExposure.getInfo().getVisitInfo().getDarkTime() 

2200 else: 

2201 # DM-17444: darkExposure.getInfo.getVisitInfo() is None 

2202 # so getDarkTime() does not exist. 

2203 self.log.warn("darkExposure.getInfo().getVisitInfo() does not exist. Using darkScale = 1.0.") 

2204 darkScale = 1.0 

2205 

2206 isrFunctions.darkCorrection( 

2207 maskedImage=exposure.getMaskedImage(), 

2208 darkMaskedImage=darkExposure.getMaskedImage(), 

2209 expScale=expScale, 

2210 darkScale=darkScale, 

2211 invert=invert, 

2212 trimToFit=self.config.doTrimToMatchCalib 

2213 ) 

2214 

2215 def doLinearize(self, detector): 

2216 """Check if linearization is needed for the detector cameraGeom. 

2217 

2218 Checks config.doLinearize and the linearity type of the first 

2219 amplifier. 

2220 

2221 Parameters 

2222 ---------- 

2223 detector : `lsst.afw.cameraGeom.Detector` 

2224 Detector to get linearity type from. 

2225 

2226 Returns 

2227 ------- 

2228 doLinearize : `Bool` 

2229 If True, linearization should be performed. 

2230 """ 

2231 return self.config.doLinearize and \ 

2232 detector.getAmplifiers()[0].getLinearityType() != NullLinearityType 

2233 

2234 def flatCorrection(self, exposure, flatExposure, invert=False): 

2235 """Apply flat correction in place. 

2236 

2237 Parameters 

2238 ---------- 

2239 exposure : `lsst.afw.image.Exposure` 

2240 Exposure to process. 

2241 flatExposure : `lsst.afw.image.Exposure` 

2242 Flat exposure of the same size as ``exposure``. 

2243 invert : `Bool`, optional 

2244 If True, unflatten an already flattened image. 

2245 

2246 See Also 

2247 -------- 

2248 lsst.ip.isr.isrFunctions.flatCorrection 

2249 """ 

2250 isrFunctions.flatCorrection( 

2251 maskedImage=exposure.getMaskedImage(), 

2252 flatMaskedImage=flatExposure.getMaskedImage(), 

2253 scalingType=self.config.flatScalingType, 

2254 userScale=self.config.flatUserScale, 

2255 invert=invert, 

2256 trimToFit=self.config.doTrimToMatchCalib 

2257 ) 

2258 

2259 def saturationDetection(self, exposure, amp): 

2260 """Detect saturated pixels and mask them using mask plane config.saturatedMaskName, in place. 

2261 

2262 Parameters 

2263 ---------- 

2264 exposure : `lsst.afw.image.Exposure` 

2265 Exposure to process. Only the amplifier DataSec is processed. 

2266 amp : `lsst.afw.table.AmpInfoCatalog` 

2267 Amplifier detector data. 

2268 

2269 See Also 

2270 -------- 

2271 lsst.ip.isr.isrFunctions.makeThresholdMask 

2272 """ 

2273 if not math.isnan(amp.getSaturation()): 

2274 maskedImage = exposure.getMaskedImage() 

2275 dataView = maskedImage.Factory(maskedImage, amp.getRawBBox()) 

2276 isrFunctions.makeThresholdMask( 

2277 maskedImage=dataView, 

2278 threshold=amp.getSaturation(), 

2279 growFootprints=0, 

2280 maskName=self.config.saturatedMaskName, 

2281 ) 

2282 

2283 def saturationInterpolation(self, exposure): 

2284 """Interpolate over saturated pixels, in place. 

2285 

2286 This method should be called after `saturationDetection`, to 

2287 ensure that the saturated pixels have been identified in the 

2288 SAT mask. It should also be called after `assembleCcd`, since 

2289 saturated regions may cross amplifier boundaries. 

2290 

2291 Parameters 

2292 ---------- 

2293 exposure : `lsst.afw.image.Exposure` 

2294 Exposure to process. 

2295 

2296 See Also 

2297 -------- 

2298 lsst.ip.isr.isrTask.saturationDetection 

2299 lsst.ip.isr.isrFunctions.interpolateFromMask 

2300 """ 

2301 isrFunctions.interpolateFromMask( 

2302 maskedImage=exposure.getMaskedImage(), 

2303 fwhm=self.config.fwhm, 

2304 growSaturatedFootprints=self.config.growSaturationFootprintSize, 

2305 maskNameList=list(self.config.saturatedMaskName), 

2306 ) 

2307 

2308 def suspectDetection(self, exposure, amp): 

2309 """Detect suspect pixels and mask them using mask plane config.suspectMaskName, in place. 

2310 

2311 Parameters 

2312 ---------- 

2313 exposure : `lsst.afw.image.Exposure` 

2314 Exposure to process. Only the amplifier DataSec is processed. 

2315 amp : `lsst.afw.table.AmpInfoCatalog` 

2316 Amplifier detector data. 

2317 

2318 See Also 

2319 -------- 

2320 lsst.ip.isr.isrFunctions.makeThresholdMask 

2321 

2322 Notes 

2323 ----- 

2324 Suspect pixels are pixels whose value is greater than amp.getSuspectLevel(). 

2325 This is intended to indicate pixels that may be affected by unknown systematics; 

2326 for example if non-linearity corrections above a certain level are unstable 

2327 then that would be a useful value for suspectLevel. A value of `nan` indicates 

2328 that no such level exists and no pixels are to be masked as suspicious. 

2329 """ 

2330 suspectLevel = amp.getSuspectLevel() 

2331 if math.isnan(suspectLevel): 

2332 return 

2333 

2334 maskedImage = exposure.getMaskedImage() 

2335 dataView = maskedImage.Factory(maskedImage, amp.getRawBBox()) 

2336 isrFunctions.makeThresholdMask( 

2337 maskedImage=dataView, 

2338 threshold=suspectLevel, 

2339 growFootprints=0, 

2340 maskName=self.config.suspectMaskName, 

2341 ) 

2342 

2343 def maskDefect(self, exposure, defectBaseList): 

2344 """Mask defects using mask plane "BAD", in place. 

2345 

2346 Parameters 

2347 ---------- 

2348 exposure : `lsst.afw.image.Exposure` 

2349 Exposure to process. 

2350 defectBaseList : `lsst.ip.isr.Defects` or `list` of 

2351 `lsst.afw.image.DefectBase`. 

2352 List of defects to mask. 

2353 

2354 Notes 

2355 ----- 

2356 Call this after CCD assembly, since defects may cross amplifier boundaries. 

2357 """ 

2358 maskedImage = exposure.getMaskedImage() 

2359 if not isinstance(defectBaseList, Defects): 

2360 # Promotes DefectBase to Defect 

2361 defectList = Defects(defectBaseList) 

2362 else: 

2363 defectList = defectBaseList 

2364 defectList.maskPixels(maskedImage, maskName="BAD") 

2365 

2366 def maskEdges(self, exposure, numEdgePixels=0, maskPlane="SUSPECT", level='DETECTOR'): 

2367 """Mask edge pixels with applicable mask plane. 

2368 

2369 Parameters 

2370 ---------- 

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

2372 Exposure to process. 

2373 numEdgePixels : `int`, optional 

2374 Number of edge pixels to mask. 

2375 maskPlane : `str`, optional 

2376 Mask plane name to use. 

2377 level : `str`, optional 

2378 Level at which to mask edges. 

2379 """ 

2380 maskedImage = exposure.getMaskedImage() 

2381 maskBitMask = maskedImage.getMask().getPlaneBitMask(maskPlane) 

2382 

2383 if numEdgePixels > 0: 

2384 if level == 'DETECTOR': 

2385 boxes = [maskedImage.getBBox()] 

2386 elif level == 'AMP': 

2387 boxes = [amp.getBBox() for amp in exposure.getDetector()] 

2388 

2389 for box in boxes: 

2390 # This makes a bbox numEdgeSuspect pixels smaller than the image on each side 

2391 subImage = maskedImage[box] 

2392 box.grow(-numEdgePixels) 

2393 # Mask pixels outside box 

2394 SourceDetectionTask.setEdgeBits( 

2395 subImage, 

2396 box, 

2397 maskBitMask) 

2398 

2399 def maskAndInterpolateDefects(self, exposure, defectBaseList): 

2400 """Mask and interpolate defects using mask plane "BAD", in place. 

2401 

2402 Parameters 

2403 ---------- 

2404 exposure : `lsst.afw.image.Exposure` 

2405 Exposure to process. 

2406 defectBaseList : `lsst.ip.isr.Defects` or `list` of 

2407 `lsst.afw.image.DefectBase`. 

2408 List of defects to mask and interpolate. 

2409 

2410 See Also 

2411 -------- 

2412 lsst.ip.isr.isrTask.maskDefect 

2413 """ 

2414 self.maskDefect(exposure, defectBaseList) 

2415 self.maskEdges(exposure, numEdgePixels=self.config.numEdgeSuspect, 

2416 maskPlane="SUSPECT", level=self.config.edgeMaskLevel) 

2417 isrFunctions.interpolateFromMask( 

2418 maskedImage=exposure.getMaskedImage(), 

2419 fwhm=self.config.fwhm, 

2420 growSaturatedFootprints=0, 

2421 maskNameList=["BAD"], 

2422 ) 

2423 

2424 def maskNan(self, exposure): 

2425 """Mask NaNs using mask plane "UNMASKEDNAN", in place. 

2426 

2427 Parameters 

2428 ---------- 

2429 exposure : `lsst.afw.image.Exposure` 

2430 Exposure to process. 

2431 

2432 Notes 

2433 ----- 

2434 We mask over all non-finite values (NaN, inf), including those 

2435 that are masked with other bits (because those may or may not be 

2436 interpolated over later, and we want to remove all NaN/infs). 

2437 Despite this behaviour, the "UNMASKEDNAN" mask plane is used to 

2438 preserve the historical name. 

2439 """ 

2440 maskedImage = exposure.getMaskedImage() 

2441 

2442 # Find and mask NaNs 

2443 maskedImage.getMask().addMaskPlane("UNMASKEDNAN") 

2444 maskVal = maskedImage.getMask().getPlaneBitMask("UNMASKEDNAN") 

2445 numNans = maskNans(maskedImage, maskVal) 

2446 self.metadata.set("NUMNANS", numNans) 

2447 if numNans > 0: 

2448 self.log.warn("There were %d unmasked NaNs.", numNans) 

2449 

2450 def maskAndInterpolateNan(self, exposure): 

2451 """"Mask and interpolate NaN/infs using mask plane "UNMASKEDNAN", 

2452 in place. 

2453 

2454 Parameters 

2455 ---------- 

2456 exposure : `lsst.afw.image.Exposure` 

2457 Exposure to process. 

2458 

2459 See Also 

2460 -------- 

2461 lsst.ip.isr.isrTask.maskNan 

2462 """ 

2463 self.maskNan(exposure) 

2464 isrFunctions.interpolateFromMask( 

2465 maskedImage=exposure.getMaskedImage(), 

2466 fwhm=self.config.fwhm, 

2467 growSaturatedFootprints=0, 

2468 maskNameList=["UNMASKEDNAN"], 

2469 ) 

2470 

2471 def measureBackground(self, exposure, IsrQaConfig=None): 

2472 """Measure the image background in subgrids, for quality control purposes. 

2473 

2474 Parameters 

2475 ---------- 

2476 exposure : `lsst.afw.image.Exposure` 

2477 Exposure to process. 

2478 IsrQaConfig : `lsst.ip.isr.isrQa.IsrQaConfig` 

2479 Configuration object containing parameters on which background 

2480 statistics and subgrids to use. 

2481 """ 

2482 if IsrQaConfig is not None: 

2483 statsControl = afwMath.StatisticsControl(IsrQaConfig.flatness.clipSigma, 

2484 IsrQaConfig.flatness.nIter) 

2485 maskVal = exposure.getMaskedImage().getMask().getPlaneBitMask(["BAD", "SAT", "DETECTED"]) 

2486 statsControl.setAndMask(maskVal) 

2487 maskedImage = exposure.getMaskedImage() 

2488 stats = afwMath.makeStatistics(maskedImage, afwMath.MEDIAN | afwMath.STDEVCLIP, statsControl) 

2489 skyLevel = stats.getValue(afwMath.MEDIAN) 

2490 skySigma = stats.getValue(afwMath.STDEVCLIP) 

2491 self.log.info("Flattened sky level: %f +/- %f.", skyLevel, skySigma) 

2492 metadata = exposure.getMetadata() 

2493 metadata.set('SKYLEVEL', skyLevel) 

2494 metadata.set('SKYSIGMA', skySigma) 

2495 

2496 # calcluating flatlevel over the subgrids 

2497 stat = afwMath.MEANCLIP if IsrQaConfig.flatness.doClip else afwMath.MEAN 

2498 meshXHalf = int(IsrQaConfig.flatness.meshX/2.) 

2499 meshYHalf = int(IsrQaConfig.flatness.meshY/2.) 

2500 nX = int((exposure.getWidth() + meshXHalf) / IsrQaConfig.flatness.meshX) 

2501 nY = int((exposure.getHeight() + meshYHalf) / IsrQaConfig.flatness.meshY) 

2502 skyLevels = numpy.zeros((nX, nY)) 

2503 

2504 for j in range(nY): 

2505 yc = meshYHalf + j * IsrQaConfig.flatness.meshY 

2506 for i in range(nX): 

2507 xc = meshXHalf + i * IsrQaConfig.flatness.meshX 

2508 

2509 xLLC = xc - meshXHalf 

2510 yLLC = yc - meshYHalf 

2511 xURC = xc + meshXHalf - 1 

2512 yURC = yc + meshYHalf - 1 

2513 

2514 bbox = lsst.geom.Box2I(lsst.geom.Point2I(xLLC, yLLC), lsst.geom.Point2I(xURC, yURC)) 

2515 miMesh = maskedImage.Factory(exposure.getMaskedImage(), bbox, afwImage.LOCAL) 

2516 

2517 skyLevels[i, j] = afwMath.makeStatistics(miMesh, stat, statsControl).getValue() 

2518 

2519 good = numpy.where(numpy.isfinite(skyLevels)) 

2520 skyMedian = numpy.median(skyLevels[good]) 

2521 flatness = (skyLevels[good] - skyMedian) / skyMedian 

2522 flatness_rms = numpy.std(flatness) 

2523 flatness_pp = flatness.max() - flatness.min() if len(flatness) > 0 else numpy.nan 

2524 

2525 self.log.info("Measuring sky levels in %dx%d grids: %f.", nX, nY, skyMedian) 

2526 self.log.info("Sky flatness in %dx%d grids - pp: %f rms: %f.", 

2527 nX, nY, flatness_pp, flatness_rms) 

2528 

2529 metadata.set('FLATNESS_PP', float(flatness_pp)) 

2530 metadata.set('FLATNESS_RMS', float(flatness_rms)) 

2531 metadata.set('FLATNESS_NGRIDS', '%dx%d' % (nX, nY)) 

2532 metadata.set('FLATNESS_MESHX', IsrQaConfig.flatness.meshX) 

2533 metadata.set('FLATNESS_MESHY', IsrQaConfig.flatness.meshY) 

2534 

2535 def roughZeroPoint(self, exposure): 

2536 """Set an approximate magnitude zero point for the exposure. 

2537 

2538 Parameters 

2539 ---------- 

2540 exposure : `lsst.afw.image.Exposure` 

2541 Exposure to process. 

2542 """ 

2543 filterLabel = exposure.getFilterLabel() 

2544 if filterLabel in self.config.fluxMag0T1: 

2545 fluxMag0 = self.config.fluxMag0T1[filterLabel] 

2546 else: 

2547 self.log.warn("No rough magnitude zero point set for filter %s.", filterLabel) 

2548 fluxMag0 = self.config.defaultFluxMag0T1 

2549 

2550 expTime = exposure.getInfo().getVisitInfo().getExposureTime() 

2551 if not expTime > 0: # handle NaN as well as <= 0 

2552 self.log.warn("Non-positive exposure time; skipping rough zero point.") 

2553 return 

2554 

2555 self.log.info("Setting rough magnitude zero point: %f", 2.5*math.log10(fluxMag0*expTime)) 

2556 exposure.setPhotoCalib(afwImage.makePhotoCalibFromCalibZeroPoint(fluxMag0*expTime, 0.0)) 

2557 

2558 def setValidPolygonIntersect(self, ccdExposure, fpPolygon): 

2559 """Set the valid polygon as the intersection of fpPolygon and the ccd corners. 

2560 

2561 Parameters 

2562 ---------- 

2563 ccdExposure : `lsst.afw.image.Exposure` 

2564 Exposure to process. 

2565 fpPolygon : `lsst.afw.geom.Polygon` 

2566 Polygon in focal plane coordinates. 

2567 """ 

2568 # Get ccd corners in focal plane coordinates 

2569 ccd = ccdExposure.getDetector() 

2570 fpCorners = ccd.getCorners(FOCAL_PLANE) 

2571 ccdPolygon = Polygon(fpCorners) 

2572 

2573 # Get intersection of ccd corners with fpPolygon 

2574 intersect = ccdPolygon.intersectionSingle(fpPolygon) 

2575 

2576 # Transform back to pixel positions and build new polygon 

2577 ccdPoints = ccd.transform(intersect, FOCAL_PLANE, PIXELS) 

2578 validPolygon = Polygon(ccdPoints) 

2579 ccdExposure.getInfo().setValidPolygon(validPolygon) 

2580 

2581 @contextmanager 

2582 def flatContext(self, exp, flat, dark=None): 

2583 """Context manager that applies and removes flats and darks, 

2584 if the task is configured to apply them. 

2585 

2586 Parameters 

2587 ---------- 

2588 exp : `lsst.afw.image.Exposure` 

2589 Exposure to process. 

2590 flat : `lsst.afw.image.Exposure` 

2591 Flat exposure the same size as ``exp``. 

2592 dark : `lsst.afw.image.Exposure`, optional 

2593 Dark exposure the same size as ``exp``. 

2594 

2595 Yields 

2596 ------ 

2597 exp : `lsst.afw.image.Exposure` 

2598 The flat and dark corrected exposure. 

2599 """ 

2600 if self.config.doDark and dark is not None: 

2601 self.darkCorrection(exp, dark) 

2602 if self.config.doFlat: 

2603 self.flatCorrection(exp, flat) 

2604 try: 

2605 yield exp 

2606 finally: 

2607 if self.config.doFlat: 

2608 self.flatCorrection(exp, flat, invert=True) 

2609 if self.config.doDark and dark is not None: 

2610 self.darkCorrection(exp, dark, invert=True) 

2611 

2612 def debugView(self, exposure, stepname): 

2613 """Utility function to examine ISR exposure at different stages. 

2614 

2615 Parameters 

2616 ---------- 

2617 exposure : `lsst.afw.image.Exposure` 

2618 Exposure to view. 

2619 stepname : `str` 

2620 State of processing to view. 

2621 """ 

2622 frame = getDebugFrame(self._display, stepname) 

2623 if frame: 

2624 display = getDisplay(frame) 

2625 display.scale('asinh', 'zscale') 

2626 display.mtv(exposure) 

2627 prompt = "Press Enter to continue [c]... " 

2628 while True: 

2629 ans = input(prompt).lower() 

2630 if ans in ("", "c",): 

2631 break 

2632 

2633 

2634class FakeAmp(object): 

2635 """A Detector-like object that supports returning gain and saturation level 

2636 

2637 This is used when the input exposure does not have a detector. 

2638 

2639 Parameters 

2640 ---------- 

2641 exposure : `lsst.afw.image.Exposure` 

2642 Exposure to generate a fake amplifier for. 

2643 config : `lsst.ip.isr.isrTaskConfig` 

2644 Configuration to apply to the fake amplifier. 

2645 """ 

2646 

2647 def __init__(self, exposure, config): 

2648 self._bbox = exposure.getBBox(afwImage.LOCAL) 

2649 self._RawHorizontalOverscanBBox = lsst.geom.Box2I() 

2650 self._gain = config.gain 

2651 self._readNoise = config.readNoise 

2652 self._saturation = config.saturation 

2653 

2654 def getBBox(self): 

2655 return self._bbox 

2656 

2657 def getRawBBox(self): 

2658 return self._bbox 

2659 

2660 def getRawHorizontalOverscanBBox(self): 

2661 return self._RawHorizontalOverscanBBox 

2662 

2663 def getGain(self): 

2664 return self._gain 

2665 

2666 def getReadNoise(self): 

2667 return self._readNoise 

2668 

2669 def getSaturation(self): 

2670 return self._saturation 

2671 

2672 def getSuspectLevel(self): 

2673 return float("NaN") 

2674 

2675 

2676class RunIsrConfig(pexConfig.Config): 

2677 isr = pexConfig.ConfigurableField(target=IsrTask, doc="Instrument signature removal") 

2678 

2679 

2680class RunIsrTask(pipeBase.CmdLineTask): 

2681 """Task to wrap the default IsrTask to allow it to be retargeted. 

2682 

2683 The standard IsrTask can be called directly from a command line 

2684 program, but doing so removes the ability of the task to be 

2685 retargeted. As most cameras override some set of the IsrTask 

2686 methods, this would remove those data-specific methods in the 

2687 output post-ISR images. This wrapping class fixes the issue, 

2688 allowing identical post-ISR images to be generated by both the 

2689 processCcd and isrTask code. 

2690 """ 

2691 ConfigClass = RunIsrConfig 

2692 _DefaultName = "runIsr" 

2693 

2694 def __init__(self, *args, **kwargs): 

2695 super().__init__(*args, **kwargs) 

2696 self.makeSubtask("isr") 

2697 

2698 def runDataRef(self, dataRef): 

2699 """ 

2700 Parameters 

2701 ---------- 

2702 dataRef : `lsst.daf.persistence.ButlerDataRef` 

2703 data reference of the detector data to be processed 

2704 

2705 Returns 

2706 ------- 

2707 result : `pipeBase.Struct` 

2708 Result struct with component: 

2709 

2710 - exposure : `lsst.afw.image.Exposure` 

2711 Post-ISR processed exposure. 

2712 """ 

2713 return self.isr.runDataRef(dataRef)