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1# This file is part of ip_isr. 

2# 

3# Developed for the LSST Data Management System. 

4# This product includes software developed by the LSST Project 

5# (https://www.lsst.org). 

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

7# for details of code ownership. 

8# 

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

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

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

12# (at your option) any later version. 

13# 

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

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

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

17# GNU General Public License for more details. 

18# 

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

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

21 

22import math 

23import numpy 

24 

25import lsst.geom 

26import lsst.afw.image as afwImage 

27import lsst.afw.math as afwMath 

28import lsst.pex.config as pexConfig 

29import lsst.pipe.base as pipeBase 

30import lsst.pipe.base.connectionTypes as cT 

31 

32from contextlib import contextmanager 

33from lsstDebug import getDebugFrame 

34 

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

36 ReadoutCorner) 

37from lsst.afw.display import getDisplay 

38from lsst.afw.geom import Polygon 

39from lsst.daf.persistence import ButlerDataRef 

40from lsst.daf.persistence.butler import NoResults 

41from lsst.meas.algorithms.detection import SourceDetectionTask 

42from lsst.utils.timer import timeMethod 

43 

44from . import isrFunctions 

45from . import isrQa 

46from . import linearize 

47from .defects import Defects 

48 

49from .assembleCcdTask import AssembleCcdTask 

50from .crosstalk import CrosstalkTask, CrosstalkCalib 

51from .fringe import FringeTask 

52from .isr import maskNans 

53from .masking import MaskingTask 

54from .overscan import OverscanCorrectionTask 

55from .straylight import StrayLightTask 

56from .vignette import VignetteTask 

57from .ampOffset import AmpOffsetTask 

58from lsst.daf.butler import DimensionGraph 

59 

60 

61__all__ = ["IsrTask", "IsrTaskConfig", "RunIsrTask", "RunIsrConfig"] 

62 

63 

64def crosstalkSourceLookup(datasetType, registry, quantumDataId, collections): 

65 """Lookup function to identify crosstalkSource entries. 

66 

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

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

69 populated. 

70 

71 Parameters 

72 ---------- 

73 datasetType : `str` 

74 Dataset to lookup. 

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

76 Butler registry to query. 

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

78 Data id to transform to identify crosstalkSources. The 

79 ``detector`` entry will be stripped. 

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

81 Collections to search through. 

82 

83 Returns 

84 ------- 

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

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

87 crosstalkSources. 

88 """ 

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

90 results = set(registry.queryDatasets(datasetType, collections=collections, dataId=newDataId, 

91 findFirst=True)) 

92 # In some contexts, calling `.expanded()` to expand all data IDs in the 

93 # query results can be a lot faster because it vectorizes lookups. But in 

94 # this case, expandDataId shouldn't need to hit the database at all in the 

95 # steady state, because only the detector record is unknown and those are 

96 # cached in the registry. 

97 return [ref.expanded(registry.expandDataId(ref.dataId, records=newDataId.records)) for ref in results] 

98 

99 

100class IsrTaskConnections(pipeBase.PipelineTaskConnections, 

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

102 defaultTemplates={}): 

103 ccdExposure = cT.Input( 

104 name="raw", 

105 doc="Input exposure to process.", 

106 storageClass="Exposure", 

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

108 ) 

109 camera = cT.PrerequisiteInput( 

110 name="camera", 

111 storageClass="Camera", 

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

113 dimensions=["instrument"], 

114 isCalibration=True, 

115 ) 

116 

117 crosstalk = cT.PrerequisiteInput( 

118 name="crosstalk", 

119 doc="Input crosstalk object", 

120 storageClass="CrosstalkCalib", 

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

122 isCalibration=True, 

123 minimum=0, # can fall back to cameraGeom 

124 ) 

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 minimum=0, # not needed for all instruments, no config to control this 

134 ) 

135 bias = cT.PrerequisiteInput( 

136 name="bias", 

137 doc="Input bias calibration.", 

138 storageClass="ExposureF", 

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

140 isCalibration=True, 

141 ) 

142 dark = cT.PrerequisiteInput( 

143 name='dark', 

144 doc="Input dark calibration.", 

145 storageClass="ExposureF", 

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

147 isCalibration=True, 

148 ) 

149 flat = cT.PrerequisiteInput( 

150 name="flat", 

151 doc="Input flat calibration.", 

152 storageClass="ExposureF", 

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

154 isCalibration=True, 

155 ) 

156 ptc = cT.PrerequisiteInput( 

157 name="ptc", 

158 doc="Input Photon Transfer Curve dataset", 

159 storageClass="PhotonTransferCurveDataset", 

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

161 isCalibration=True, 

162 ) 

163 fringes = cT.PrerequisiteInput( 

164 name="fringe", 

165 doc="Input fringe calibration.", 

166 storageClass="ExposureF", 

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

168 isCalibration=True, 

169 minimum=0, # only needed for some bands, even when enabled 

170 ) 

171 strayLightData = cT.PrerequisiteInput( 

172 name='yBackground', 

173 doc="Input stray light calibration.", 

174 storageClass="StrayLightData", 

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

176 deferLoad=True, 

177 isCalibration=True, 

178 minimum=0, # only needed for some bands, even when enabled 

179 ) 

180 bfKernel = cT.PrerequisiteInput( 

181 name='bfKernel', 

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

183 storageClass="NumpyArray", 

184 dimensions=["instrument"], 

185 isCalibration=True, 

186 minimum=0, # can use either bfKernel or newBFKernel 

187 ) 

188 newBFKernel = cT.PrerequisiteInput( 

189 name='brighterFatterKernel', 

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

191 storageClass="BrighterFatterKernel", 

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

193 isCalibration=True, 

194 minimum=0, # can use either bfKernel or newBFKernel 

195 ) 

196 defects = cT.PrerequisiteInput( 

197 name='defects', 

198 doc="Input defect tables.", 

199 storageClass="Defects", 

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

201 isCalibration=True, 

202 ) 

203 linearizer = cT.PrerequisiteInput( 

204 name='linearizer', 

205 storageClass="Linearizer", 

206 doc="Linearity correction calibration.", 

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

208 isCalibration=True, 

209 minimum=0, # can fall back to cameraGeom 

210 ) 

211 opticsTransmission = cT.PrerequisiteInput( 

212 name="transmission_optics", 

213 storageClass="TransmissionCurve", 

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

215 dimensions=["instrument"], 

216 isCalibration=True, 

217 ) 

218 filterTransmission = cT.PrerequisiteInput( 

219 name="transmission_filter", 

220 storageClass="TransmissionCurve", 

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

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

223 isCalibration=True, 

224 ) 

225 sensorTransmission = cT.PrerequisiteInput( 

226 name="transmission_sensor", 

227 storageClass="TransmissionCurve", 

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

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

230 isCalibration=True, 

231 ) 

232 atmosphereTransmission = cT.PrerequisiteInput( 

233 name="transmission_atmosphere", 

234 storageClass="TransmissionCurve", 

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

236 dimensions=["instrument"], 

237 isCalibration=True, 

238 ) 

239 illumMaskedImage = cT.PrerequisiteInput( 

240 name="illum", 

241 doc="Input illumination correction.", 

242 storageClass="MaskedImageF", 

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

244 isCalibration=True, 

245 ) 

246 

247 outputExposure = cT.Output( 

248 name='postISRCCD', 

249 doc="Output ISR processed exposure.", 

250 storageClass="Exposure", 

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

252 ) 

253 preInterpExposure = cT.Output( 

254 name='preInterpISRCCD', 

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

256 storageClass="ExposureF", 

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

258 ) 

259 outputOssThumbnail = cT.Output( 

260 name="OssThumb", 

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

262 storageClass="Thumbnail", 

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

264 ) 

265 outputFlattenedThumbnail = cT.Output( 

266 name="FlattenedThumb", 

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

268 storageClass="Thumbnail", 

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

270 ) 

271 

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

273 super().__init__(config=config) 

274 

275 if config.doBias is not True: 

276 self.prerequisiteInputs.discard("bias") 

277 if config.doLinearize is not True: 

278 self.prerequisiteInputs.discard("linearizer") 

279 if config.doCrosstalk is not True: 

280 self.prerequisiteInputs.discard("crosstalkSources") 

281 self.prerequisiteInputs.discard("crosstalk") 

282 if config.doBrighterFatter is not True: 

283 self.prerequisiteInputs.discard("bfKernel") 

284 self.prerequisiteInputs.discard("newBFKernel") 

285 if config.doDefect is not True: 

286 self.prerequisiteInputs.discard("defects") 

287 if config.doDark is not True: 

288 self.prerequisiteInputs.discard("dark") 

289 if config.doFlat is not True: 

290 self.prerequisiteInputs.discard("flat") 

291 if config.doFringe is not True: 

292 self.prerequisiteInputs.discard("fringe") 

293 if config.doStrayLight is not True: 

294 self.prerequisiteInputs.discard("strayLightData") 

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

296 self.prerequisiteInputs.discard("ptc") 

297 if config.doAttachTransmissionCurve is not True: 

298 self.prerequisiteInputs.discard("opticsTransmission") 

299 self.prerequisiteInputs.discard("filterTransmission") 

300 self.prerequisiteInputs.discard("sensorTransmission") 

301 self.prerequisiteInputs.discard("atmosphereTransmission") 

302 if config.doUseOpticsTransmission is not True: 

303 self.prerequisiteInputs.discard("opticsTransmission") 

304 if config.doUseFilterTransmission is not True: 

305 self.prerequisiteInputs.discard("filterTransmission") 

306 if config.doUseSensorTransmission is not True: 

307 self.prerequisiteInputs.discard("sensorTransmission") 

308 if config.doUseAtmosphereTransmission is not True: 

309 self.prerequisiteInputs.discard("atmosphereTransmission") 

310 if config.doIlluminationCorrection is not True: 

311 self.prerequisiteInputs.discard("illumMaskedImage") 

312 

313 if config.doWrite is not True: 

314 self.outputs.discard("outputExposure") 

315 self.outputs.discard("preInterpExposure") 

316 self.outputs.discard("outputFlattenedThumbnail") 

317 self.outputs.discard("outputOssThumbnail") 

318 if config.doSaveInterpPixels is not True: 

319 self.outputs.discard("preInterpExposure") 

320 if config.qa.doThumbnailOss is not True: 

321 self.outputs.discard("outputOssThumbnail") 

322 if config.qa.doThumbnailFlattened is not True: 

323 self.outputs.discard("outputFlattenedThumbnail") 

324 

325 

326class IsrTaskConfig(pipeBase.PipelineTaskConfig, 

327 pipelineConnections=IsrTaskConnections): 

328 """Configuration parameters for IsrTask. 

329 

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

331 """ 

332 datasetType = pexConfig.Field( 

333 dtype=str, 

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

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

336 default="raw", 

337 ) 

338 

339 fallbackFilterName = pexConfig.Field( 

340 dtype=str, 

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

342 optional=True 

343 ) 

344 useFallbackDate = pexConfig.Field( 

345 dtype=bool, 

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

347 default=False, 

348 ) 

349 expectWcs = pexConfig.Field( 

350 dtype=bool, 

351 default=True, 

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

353 ) 

354 fwhm = pexConfig.Field( 

355 dtype=float, 

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

357 default=1.0, 

358 ) 

359 qa = pexConfig.ConfigField( 

360 dtype=isrQa.IsrQaConfig, 

361 doc="QA related configuration options.", 

362 ) 

363 

364 # Image conversion configuration 

365 doConvertIntToFloat = pexConfig.Field( 

366 dtype=bool, 

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

368 default=True, 

369 ) 

370 

371 # Saturated pixel handling. 

372 doSaturation = pexConfig.Field( 

373 dtype=bool, 

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

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

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

377 default=True, 

378 ) 

379 saturatedMaskName = pexConfig.Field( 

380 dtype=str, 

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

382 default="SAT", 

383 ) 

384 saturation = pexConfig.Field( 

385 dtype=float, 

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

387 default=float("NaN"), 

388 ) 

389 growSaturationFootprintSize = pexConfig.Field( 

390 dtype=int, 

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

392 default=1, 

393 ) 

394 

395 # Suspect pixel handling. 

396 doSuspect = pexConfig.Field( 

397 dtype=bool, 

398 doc="Mask suspect pixels?", 

399 default=False, 

400 ) 

401 suspectMaskName = pexConfig.Field( 

402 dtype=str, 

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

404 default="SUSPECT", 

405 ) 

406 numEdgeSuspect = pexConfig.Field( 

407 dtype=int, 

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

409 default=0, 

410 ) 

411 edgeMaskLevel = pexConfig.ChoiceField( 

412 dtype=str, 

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

414 default="DETECTOR", 

415 allowed={ 

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

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

418 }, 

419 ) 

420 

421 # Initial masking options. 

422 doSetBadRegions = pexConfig.Field( 

423 dtype=bool, 

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

425 default=True, 

426 ) 

427 badStatistic = pexConfig.ChoiceField( 

428 dtype=str, 

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

430 default='MEANCLIP', 

431 allowed={ 

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

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

434 }, 

435 ) 

436 

437 # Overscan subtraction configuration. 

438 doOverscan = pexConfig.Field( 

439 dtype=bool, 

440 doc="Do overscan subtraction?", 

441 default=True, 

442 ) 

443 overscan = pexConfig.ConfigurableField( 

444 target=OverscanCorrectionTask, 

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

446 ) 

447 overscanFitType = pexConfig.ChoiceField( 

448 dtype=str, 

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

450 default='MEDIAN', 

451 allowed={ 

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

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

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

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

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

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

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

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

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

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

462 }, 

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

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

465 ) 

466 overscanOrder = pexConfig.Field( 

467 dtype=int, 

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

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

470 default=1, 

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

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

473 ) 

474 overscanNumSigmaClip = pexConfig.Field( 

475 dtype=float, 

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

477 default=3.0, 

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

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

480 ) 

481 overscanIsInt = pexConfig.Field( 

482 dtype=bool, 

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

484 " and overscan.FitType=MEDIAN_PER_ROW.", 

485 default=True, 

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

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

488 ) 

489 # These options do not get deprecated, as they define how we slice up the 

490 # image data. 

491 overscanNumLeadingColumnsToSkip = pexConfig.Field( 

492 dtype=int, 

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

494 default=0, 

495 ) 

496 overscanNumTrailingColumnsToSkip = pexConfig.Field( 

497 dtype=int, 

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

499 default=0, 

500 ) 

501 overscanMaxDev = pexConfig.Field( 501 ↛ exitline 501 didn't jump to the function exit

502 dtype=float, 

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

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

505 ) 

506 overscanBiasJump = pexConfig.Field( 

507 dtype=bool, 

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

509 default=False, 

510 ) 

511 overscanBiasJumpKeyword = pexConfig.Field( 

512 dtype=str, 

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

514 default="NO_SUCH_KEY", 

515 ) 

516 overscanBiasJumpDevices = pexConfig.ListField( 

517 dtype=str, 

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

519 default=(), 

520 ) 

521 overscanBiasJumpLocation = pexConfig.Field( 

522 dtype=int, 

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

524 default=0, 

525 ) 

526 

527 # Amplifier to CCD assembly configuration 

528 doAssembleCcd = pexConfig.Field( 

529 dtype=bool, 

530 default=True, 

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

532 ) 

533 assembleCcd = pexConfig.ConfigurableField( 

534 target=AssembleCcdTask, 

535 doc="CCD assembly task", 

536 ) 

537 

538 # General calibration configuration. 

539 doAssembleIsrExposures = pexConfig.Field( 

540 dtype=bool, 

541 default=False, 

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

543 ) 

544 doTrimToMatchCalib = pexConfig.Field( 

545 dtype=bool, 

546 default=False, 

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

548 ) 

549 

550 # Bias subtraction. 

551 doBias = pexConfig.Field( 

552 dtype=bool, 

553 doc="Apply bias frame correction?", 

554 default=True, 

555 ) 

556 biasDataProductName = pexConfig.Field( 

557 dtype=str, 

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

559 default="bias", 

560 ) 

561 doBiasBeforeOverscan = pexConfig.Field( 

562 dtype=bool, 

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

564 default=False 

565 ) 

566 

567 # Variance construction 

568 doVariance = pexConfig.Field( 

569 dtype=bool, 

570 doc="Calculate variance?", 

571 default=True 

572 ) 

573 gain = pexConfig.Field( 

574 dtype=float, 

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

576 default=float("NaN"), 

577 ) 

578 readNoise = pexConfig.Field( 

579 dtype=float, 

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

581 default=0.0, 

582 ) 

583 doEmpiricalReadNoise = pexConfig.Field( 

584 dtype=bool, 

585 default=False, 

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

587 ) 

588 usePtcReadNoise = pexConfig.Field( 

589 dtype=bool, 

590 default=False, 

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

592 ) 

593 maskNegativeVariance = pexConfig.Field( 

594 dtype=bool, 

595 default=True, 

596 doc="Mask pixels that claim a negative variance? This likely indicates a failure " 

597 "in the measurement of the overscan at an edge due to the data falling off faster " 

598 "than the overscan model can account for it." 

599 ) 

600 negativeVarianceMaskName = pexConfig.Field( 

601 dtype=str, 

602 default="BAD", 

603 doc="Mask plane to use to mark pixels with negative variance, if `maskNegativeVariance` is True.", 

604 ) 

605 # Linearization. 

606 doLinearize = pexConfig.Field( 

607 dtype=bool, 

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

609 default=True, 

610 ) 

611 

612 # Crosstalk. 

613 doCrosstalk = pexConfig.Field( 

614 dtype=bool, 

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

616 default=False, 

617 ) 

618 doCrosstalkBeforeAssemble = pexConfig.Field( 

619 dtype=bool, 

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

621 default=False, 

622 ) 

623 crosstalk = pexConfig.ConfigurableField( 

624 target=CrosstalkTask, 

625 doc="Intra-CCD crosstalk correction", 

626 ) 

627 

628 # Masking options. 

629 doDefect = pexConfig.Field( 

630 dtype=bool, 

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

632 default=True, 

633 ) 

634 doNanMasking = pexConfig.Field( 

635 dtype=bool, 

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

637 default=True, 

638 ) 

639 doWidenSaturationTrails = pexConfig.Field( 

640 dtype=bool, 

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

642 default=True 

643 ) 

644 

645 # Brighter-Fatter correction. 

646 doBrighterFatter = pexConfig.Field( 

647 dtype=bool, 

648 default=False, 

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

650 ) 

651 brighterFatterLevel = pexConfig.ChoiceField( 

652 dtype=str, 

653 default="DETECTOR", 

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

655 allowed={ 

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

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

658 } 

659 ) 

660 brighterFatterMaxIter = pexConfig.Field( 

661 dtype=int, 

662 default=10, 

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

664 ) 

665 brighterFatterThreshold = pexConfig.Field( 

666 dtype=float, 

667 default=1000, 

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

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

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

671 ) 

672 brighterFatterApplyGain = pexConfig.Field( 

673 dtype=bool, 

674 default=True, 

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

676 ) 

677 brighterFatterMaskListToInterpolate = pexConfig.ListField( 

678 dtype=str, 

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

680 "correction.", 

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

682 ) 

683 brighterFatterMaskGrowSize = pexConfig.Field( 

684 dtype=int, 

685 default=0, 

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

687 "when brighter-fatter correction is applied." 

688 ) 

689 

690 # Dark subtraction. 

691 doDark = pexConfig.Field( 

692 dtype=bool, 

693 doc="Apply dark frame correction?", 

694 default=True, 

695 ) 

696 darkDataProductName = pexConfig.Field( 

697 dtype=str, 

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

699 default="dark", 

700 ) 

701 

702 # Camera-specific stray light removal. 

703 doStrayLight = pexConfig.Field( 

704 dtype=bool, 

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

706 default=False, 

707 ) 

708 strayLight = pexConfig.ConfigurableField( 

709 target=StrayLightTask, 

710 doc="y-band stray light correction" 

711 ) 

712 

713 # Flat correction. 

714 doFlat = pexConfig.Field( 

715 dtype=bool, 

716 doc="Apply flat field correction?", 

717 default=True, 

718 ) 

719 flatDataProductName = pexConfig.Field( 

720 dtype=str, 

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

722 default="flat", 

723 ) 

724 flatScalingType = pexConfig.ChoiceField( 

725 dtype=str, 

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

727 default='USER', 

728 allowed={ 

729 "USER": "Scale by flatUserScale", 

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

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

732 }, 

733 ) 

734 flatUserScale = pexConfig.Field( 

735 dtype=float, 

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

737 default=1.0, 

738 ) 

739 doTweakFlat = pexConfig.Field( 

740 dtype=bool, 

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

742 default=False 

743 ) 

744 

745 # Amplifier normalization based on gains instead of using flats 

746 # configuration. 

747 doApplyGains = pexConfig.Field( 

748 dtype=bool, 

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

750 default=False, 

751 ) 

752 usePtcGains = pexConfig.Field( 

753 dtype=bool, 

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

755 default=False, 

756 ) 

757 normalizeGains = pexConfig.Field( 

758 dtype=bool, 

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

760 default=False, 

761 ) 

762 

763 # Fringe correction. 

764 doFringe = pexConfig.Field( 

765 dtype=bool, 

766 doc="Apply fringe correction?", 

767 default=True, 

768 ) 

769 fringe = pexConfig.ConfigurableField( 

770 target=FringeTask, 

771 doc="Fringe subtraction task", 

772 ) 

773 fringeAfterFlat = pexConfig.Field( 

774 dtype=bool, 

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

776 default=True, 

777 ) 

778 

779 # Amp offset correction. 

780 doAmpOffset = pexConfig.Field( 

781 doc="Calculate and apply amp offset corrections?", 

782 dtype=bool, 

783 default=False, 

784 ) 

785 ampOffset = pexConfig.ConfigurableField( 

786 doc="Amp offset correction task.", 

787 target=AmpOffsetTask, 

788 ) 

789 

790 # Initial CCD-level background statistics options. 

791 doMeasureBackground = pexConfig.Field( 

792 dtype=bool, 

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

794 default=False, 

795 ) 

796 

797 # Camera-specific masking configuration. 

798 doCameraSpecificMasking = pexConfig.Field( 

799 dtype=bool, 

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

801 default=False, 

802 ) 

803 masking = pexConfig.ConfigurableField( 

804 target=MaskingTask, 

805 doc="Masking task." 

806 ) 

807 

808 # Interpolation options. 

809 doInterpolate = pexConfig.Field( 

810 dtype=bool, 

811 doc="Interpolate masked pixels?", 

812 default=True, 

813 ) 

814 doSaturationInterpolation = pexConfig.Field( 

815 dtype=bool, 

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

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

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

819 default=True, 

820 ) 

821 doNanInterpolation = pexConfig.Field( 

822 dtype=bool, 

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

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

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

826 default=True, 

827 ) 

828 doNanInterpAfterFlat = pexConfig.Field( 

829 dtype=bool, 

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

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

832 default=False, 

833 ) 

834 maskListToInterpolate = pexConfig.ListField( 

835 dtype=str, 

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

837 default=['SAT', 'BAD'], 

838 ) 

839 doSaveInterpPixels = pexConfig.Field( 

840 dtype=bool, 

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

842 default=False, 

843 ) 

844 

845 # Default photometric calibration options. 

846 fluxMag0T1 = pexConfig.DictField( 

847 keytype=str, 

848 itemtype=float, 

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

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

851 )) 

852 ) 

853 defaultFluxMag0T1 = pexConfig.Field( 

854 dtype=float, 

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

856 default=pow(10.0, 0.4*28.0) 

857 ) 

858 

859 # Vignette correction configuration. 

860 doVignette = pexConfig.Field( 

861 dtype=bool, 

862 doc="Apply vignetting parameters?", 

863 default=False, 

864 ) 

865 vignette = pexConfig.ConfigurableField( 

866 target=VignetteTask, 

867 doc="Vignetting task.", 

868 ) 

869 

870 # Transmission curve configuration. 

871 doAttachTransmissionCurve = pexConfig.Field( 

872 dtype=bool, 

873 default=False, 

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

875 ) 

876 doUseOpticsTransmission = pexConfig.Field( 

877 dtype=bool, 

878 default=True, 

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

880 ) 

881 doUseFilterTransmission = pexConfig.Field( 

882 dtype=bool, 

883 default=True, 

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

885 ) 

886 doUseSensorTransmission = pexConfig.Field( 

887 dtype=bool, 

888 default=True, 

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

890 ) 

891 doUseAtmosphereTransmission = pexConfig.Field( 

892 dtype=bool, 

893 default=True, 

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

895 ) 

896 

897 # Illumination correction. 

898 doIlluminationCorrection = pexConfig.Field( 

899 dtype=bool, 

900 default=False, 

901 doc="Perform illumination correction?" 

902 ) 

903 illuminationCorrectionDataProductName = pexConfig.Field( 

904 dtype=str, 

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

906 default="illumcor", 

907 ) 

908 illumScale = pexConfig.Field( 

909 dtype=float, 

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

911 default=1.0, 

912 ) 

913 illumFilters = pexConfig.ListField( 

914 dtype=str, 

915 default=[], 

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

917 ) 

918 

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

920 # be needed. 

921 doWrite = pexConfig.Field( 

922 dtype=bool, 

923 doc="Persist postISRCCD?", 

924 default=True, 

925 ) 

926 

927 def validate(self): 

928 super().validate() 

929 if self.doFlat and self.doApplyGains: 

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

931 if self.doBiasBeforeOverscan and self.doTrimToMatchCalib: 

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

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

934 self.maskListToInterpolate.append(self.saturatedMaskName) 

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

936 self.maskListToInterpolate.remove(self.saturatedMaskName) 

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

938 self.maskListToInterpolate.append("UNMASKEDNAN") 

939 

940 

941class IsrTask(pipeBase.PipelineTask, pipeBase.CmdLineTask): 

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

943 

944 The process for correcting imaging data is very similar from 

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

946 doing these corrections, including the ability to turn certain 

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

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

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

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

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

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

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

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

955 subclassed for different camera, although the most camera specific 

956 methods have been split into subtasks that can be redirected 

957 appropriately. 

958 

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

960 the defaults from `lsst.ip.isr`. 

961 

962 Parameters 

963 ---------- 

964 args : `list` 

965 Positional arguments passed to the Task constructor. 

966 None used at this time. 

967 kwargs : `dict`, optional 

968 Keyword arguments passed on to the Task constructor. 

969 None used at this time. 

970 """ 

971 ConfigClass = IsrTaskConfig 

972 _DefaultName = "isr" 

973 

974 def __init__(self, **kwargs): 

975 super().__init__(**kwargs) 

976 self.makeSubtask("assembleCcd") 

977 self.makeSubtask("crosstalk") 

978 self.makeSubtask("strayLight") 

979 self.makeSubtask("fringe") 

980 self.makeSubtask("masking") 

981 self.makeSubtask("overscan") 

982 self.makeSubtask("vignette") 

983 self.makeSubtask("ampOffset") 

984 

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

986 inputs = butlerQC.get(inputRefs) 

987 

988 try: 

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

990 except Exception as e: 

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

992 (inputRefs, e)) 

993 

994 inputs['isGen3'] = True 

995 

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

997 

998 if self.config.doCrosstalk is True: 

999 # Crosstalk sources need to be defined by the pipeline 

1000 # yaml if they exist. 

1001 if 'crosstalk' in inputs and inputs['crosstalk'] is not None: 

1002 if not isinstance(inputs['crosstalk'], CrosstalkCalib): 

1003 inputs['crosstalk'] = CrosstalkCalib.fromTable(inputs['crosstalk']) 

1004 else: 

1005 coeffVector = (self.config.crosstalk.crosstalkValues 

1006 if self.config.crosstalk.useConfigCoefficients else None) 

1007 crosstalkCalib = CrosstalkCalib().fromDetector(detector, coeffVector=coeffVector) 

1008 inputs['crosstalk'] = crosstalkCalib 

1009 if inputs['crosstalk'].interChip and len(inputs['crosstalk'].interChip) > 0: 

1010 if 'crosstalkSources' not in inputs: 

1011 self.log.warning("No crosstalkSources found for chip with interChip terms!") 

1012 

1013 if self.doLinearize(detector) is True: 

1014 if 'linearizer' in inputs: 

1015 if isinstance(inputs['linearizer'], dict): 

1016 linearizer = linearize.Linearizer(detector=detector, log=self.log) 

1017 linearizer.fromYaml(inputs['linearizer']) 

1018 self.log.warning("Dictionary linearizers will be deprecated in DM-28741.") 

1019 elif isinstance(inputs['linearizer'], numpy.ndarray): 

1020 linearizer = linearize.Linearizer(table=inputs.get('linearizer', None), 

1021 detector=detector, 

1022 log=self.log) 

1023 self.log.warning("Bare lookup table linearizers will be deprecated in DM-28741.") 

1024 else: 

1025 linearizer = inputs['linearizer'] 

1026 linearizer.log = self.log 

1027 inputs['linearizer'] = linearizer 

1028 else: 

1029 inputs['linearizer'] = linearize.Linearizer(detector=detector, log=self.log) 

1030 self.log.warning("Constructing linearizer from cameraGeom information.") 

1031 

1032 if self.config.doDefect is True: 

1033 if "defects" in inputs and inputs['defects'] is not None: 

1034 # defects is loaded as a BaseCatalog with columns 

1035 # x0, y0, width, height. Masking expects a list of defects 

1036 # defined by their bounding box 

1037 if not isinstance(inputs["defects"], Defects): 

1038 inputs["defects"] = Defects.fromTable(inputs["defects"]) 

1039 

1040 # Load the correct style of brighter-fatter kernel, and repack 

1041 # the information as a numpy array. 

1042 if self.config.doBrighterFatter: 

1043 brighterFatterKernel = inputs.pop('newBFKernel', None) 

1044 if brighterFatterKernel is None: 

1045 brighterFatterKernel = inputs.get('bfKernel', None) 

1046 

1047 if brighterFatterKernel is not None and not isinstance(brighterFatterKernel, numpy.ndarray): 

1048 # This is a ISR calib kernel 

1049 detName = detector.getName() 

1050 level = brighterFatterKernel.level 

1051 

1052 # This is expected to be a dictionary of amp-wise gains. 

1053 inputs['bfGains'] = brighterFatterKernel.gain 

1054 if self.config.brighterFatterLevel == 'DETECTOR': 

1055 if level == 'DETECTOR': 

1056 if detName in brighterFatterKernel.detKernels: 

1057 inputs['bfKernel'] = brighterFatterKernel.detKernels[detName] 

1058 else: 

1059 raise RuntimeError("Failed to extract kernel from new-style BF kernel.") 

1060 elif level == 'AMP': 

1061 self.log.warning("Making DETECTOR level kernel from AMP based brighter " 

1062 "fatter kernels.") 

1063 brighterFatterKernel.makeDetectorKernelFromAmpwiseKernels(detName) 

1064 inputs['bfKernel'] = brighterFatterKernel.detKernels[detName] 

1065 elif self.config.brighterFatterLevel == 'AMP': 

1066 raise NotImplementedError("Per-amplifier brighter-fatter correction not implemented") 

1067 

1068 if self.config.doFringe is True and self.fringe.checkFilter(inputs['ccdExposure']): 

1069 expId = inputs['ccdExposure'].info.id 

1070 inputs['fringes'] = self.fringe.loadFringes(inputs['fringes'], 

1071 expId=expId, 

1072 assembler=self.assembleCcd 

1073 if self.config.doAssembleIsrExposures else None) 

1074 else: 

1075 inputs['fringes'] = pipeBase.Struct(fringes=None) 

1076 

1077 if self.config.doStrayLight is True and self.strayLight.checkFilter(inputs['ccdExposure']): 

1078 if 'strayLightData' not in inputs: 

1079 inputs['strayLightData'] = None 

1080 

1081 outputs = self.run(**inputs) 

1082 butlerQC.put(outputs, outputRefs) 

1083 

1084 def readIsrData(self, dataRef, rawExposure): 

1085 """Retrieve necessary frames for instrument signature removal. 

1086 

1087 Pre-fetching all required ISR data products limits the IO 

1088 required by the ISR. Any conflict between the calibration data 

1089 available and that needed for ISR is also detected prior to 

1090 doing processing, allowing it to fail quickly. 

1091 

1092 Parameters 

1093 ---------- 

1094 dataRef : `daf.persistence.butlerSubset.ButlerDataRef` 

1095 Butler reference of the detector data to be processed 

1096 rawExposure : `afw.image.Exposure` 

1097 The raw exposure that will later be corrected with the 

1098 retrieved calibration data; should not be modified in this 

1099 method. 

1100 

1101 Returns 

1102 ------- 

1103 result : `lsst.pipe.base.Struct` 

1104 Result struct with components (which may be `None`): 

1105 - ``bias``: bias calibration frame (`afw.image.Exposure`) 

1106 - ``linearizer``: functor for linearization 

1107 (`ip.isr.linearize.LinearizeBase`) 

1108 - ``crosstalkSources``: list of possible crosstalk sources (`list`) 

1109 - ``dark``: dark calibration frame (`afw.image.Exposure`) 

1110 - ``flat``: flat calibration frame (`afw.image.Exposure`) 

1111 - ``bfKernel``: Brighter-Fatter kernel (`numpy.ndarray`) 

1112 - ``defects``: list of defects (`lsst.ip.isr.Defects`) 

1113 - ``fringes``: `lsst.pipe.base.Struct` with components: 

1114 - ``fringes``: fringe calibration frame (`afw.image.Exposure`) 

1115 - ``seed``: random seed derived from the ccdExposureId for random 

1116 number generator (`uint32`). 

1117 - ``opticsTransmission``: `lsst.afw.image.TransmissionCurve` 

1118 A ``TransmissionCurve`` that represents the throughput of the 

1119 optics, to be evaluated in focal-plane coordinates. 

1120 - ``filterTransmission`` : `lsst.afw.image.TransmissionCurve` 

1121 A ``TransmissionCurve`` that represents the throughput of the 

1122 filter itself, to be evaluated in focal-plane coordinates. 

1123 - ``sensorTransmission`` : `lsst.afw.image.TransmissionCurve` 

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

1125 sensor itself, to be evaluated in post-assembly trimmed 

1126 detector coordinates. 

1127 - ``atmosphereTransmission`` : `lsst.afw.image.TransmissionCurve` 

1128 A ``TransmissionCurve`` that represents the throughput of the 

1129 atmosphere, assumed to be spatially constant. 

1130 - ``strayLightData`` : `object` 

1131 An opaque object containing calibration information for 

1132 stray-light correction. If `None`, no correction will be 

1133 performed. 

1134 - ``illumMaskedImage`` : illumination correction image 

1135 (`lsst.afw.image.MaskedImage`) 

1136 

1137 Raises 

1138 ------ 

1139 NotImplementedError : 

1140 Raised if a per-amplifier brighter-fatter kernel is requested by 

1141 the configuration. 

1142 """ 

1143 try: 

1144 dateObs = rawExposure.getInfo().getVisitInfo().getDate() 

1145 dateObs = dateObs.toPython().isoformat() 

1146 except RuntimeError: 

1147 self.log.warning("Unable to identify dateObs for rawExposure.") 

1148 dateObs = None 

1149 

1150 ccd = rawExposure.getDetector() 

1151 filterLabel = rawExposure.getFilterLabel() 

1152 physicalFilter = isrFunctions.getPhysicalFilter(filterLabel, self.log) 

1153 rawExposure.mask.addMaskPlane("UNMASKEDNAN") # needed to match pre DM-15862 processing. 

1154 biasExposure = (self.getIsrExposure(dataRef, self.config.biasDataProductName) 

1155 if self.config.doBias else None) 

1156 # immediate=True required for functors and linearizers are functors 

1157 # see ticket DM-6515 

1158 linearizer = (dataRef.get("linearizer", immediate=True) 

1159 if self.doLinearize(ccd) else None) 

1160 if linearizer is not None and not isinstance(linearizer, numpy.ndarray): 

1161 linearizer.log = self.log 

1162 if isinstance(linearizer, numpy.ndarray): 

1163 linearizer = linearize.Linearizer(table=linearizer, detector=ccd) 

1164 

1165 crosstalkCalib = None 

1166 if self.config.doCrosstalk: 

1167 try: 

1168 crosstalkCalib = dataRef.get("crosstalk", immediate=True) 

1169 except NoResults: 

1170 coeffVector = (self.config.crosstalk.crosstalkValues 

1171 if self.config.crosstalk.useConfigCoefficients else None) 

1172 crosstalkCalib = CrosstalkCalib().fromDetector(ccd, coeffVector=coeffVector) 

1173 crosstalkSources = (self.crosstalk.prepCrosstalk(dataRef, crosstalkCalib) 

1174 if self.config.doCrosstalk else None) 

1175 

1176 darkExposure = (self.getIsrExposure(dataRef, self.config.darkDataProductName) 

1177 if self.config.doDark else None) 

1178 flatExposure = (self.getIsrExposure(dataRef, self.config.flatDataProductName, 

1179 dateObs=dateObs) 

1180 if self.config.doFlat else None) 

1181 

1182 brighterFatterKernel = None 

1183 brighterFatterGains = None 

1184 if self.config.doBrighterFatter is True: 

1185 try: 

1186 # Use the new-style cp_pipe version of the kernel if it exists 

1187 # If using a new-style kernel, always use the self-consistent 

1188 # gains, i.e. the ones inside the kernel object itself 

1189 brighterFatterKernel = dataRef.get("brighterFatterKernel") 

1190 brighterFatterGains = brighterFatterKernel.gain 

1191 self.log.info("New style brighter-fatter kernel (brighterFatterKernel) loaded") 

1192 except NoResults: 

1193 try: # Fall back to the old-style numpy-ndarray style kernel if necessary. 

1194 brighterFatterKernel = dataRef.get("bfKernel") 

1195 self.log.info("Old style brighter-fatter kernel (bfKernel) loaded") 

1196 except NoResults: 

1197 brighterFatterKernel = None 

1198 if brighterFatterKernel is not None and not isinstance(brighterFatterKernel, numpy.ndarray): 

1199 # If the kernel is not an ndarray, it's the cp_pipe version 

1200 # so extract the kernel for this detector, or raise an error 

1201 if self.config.brighterFatterLevel == 'DETECTOR': 

1202 if brighterFatterKernel.detKernels: 

1203 brighterFatterKernel = brighterFatterKernel.detKernels[ccd.getName()] 

1204 else: 

1205 raise RuntimeError("Failed to extract kernel from new-style BF kernel.") 

1206 else: 

1207 # TODO DM-15631 for implementing this 

1208 raise NotImplementedError("Per-amplifier brighter-fatter correction not implemented") 

1209 

1210 defectList = (dataRef.get("defects") 

1211 if self.config.doDefect else None) 

1212 expId = rawExposure.info.id 

1213 fringeStruct = (self.fringe.readFringes(dataRef, expId=expId, assembler=self.assembleCcd 

1214 if self.config.doAssembleIsrExposures else None) 

1215 if self.config.doFringe and self.fringe.checkFilter(rawExposure) 

1216 else pipeBase.Struct(fringes=None)) 

1217 

1218 if self.config.doAttachTransmissionCurve: 

1219 opticsTransmission = (dataRef.get("transmission_optics") 

1220 if self.config.doUseOpticsTransmission else None) 

1221 filterTransmission = (dataRef.get("transmission_filter") 

1222 if self.config.doUseFilterTransmission else None) 

1223 sensorTransmission = (dataRef.get("transmission_sensor") 

1224 if self.config.doUseSensorTransmission else None) 

1225 atmosphereTransmission = (dataRef.get("transmission_atmosphere") 

1226 if self.config.doUseAtmosphereTransmission else None) 

1227 else: 

1228 opticsTransmission = None 

1229 filterTransmission = None 

1230 sensorTransmission = None 

1231 atmosphereTransmission = None 

1232 

1233 if self.config.doStrayLight: 

1234 strayLightData = self.strayLight.readIsrData(dataRef, rawExposure) 

1235 else: 

1236 strayLightData = None 

1237 

1238 illumMaskedImage = (self.getIsrExposure(dataRef, 

1239 self.config.illuminationCorrectionDataProductName).getMaskedImage() 

1240 if (self.config.doIlluminationCorrection 

1241 and physicalFilter in self.config.illumFilters) 

1242 else None) 

1243 

1244 # Struct should include only kwargs to run() 

1245 return pipeBase.Struct(bias=biasExposure, 

1246 linearizer=linearizer, 

1247 crosstalk=crosstalkCalib, 

1248 crosstalkSources=crosstalkSources, 

1249 dark=darkExposure, 

1250 flat=flatExposure, 

1251 bfKernel=brighterFatterKernel, 

1252 bfGains=brighterFatterGains, 

1253 defects=defectList, 

1254 fringes=fringeStruct, 

1255 opticsTransmission=opticsTransmission, 

1256 filterTransmission=filterTransmission, 

1257 sensorTransmission=sensorTransmission, 

1258 atmosphereTransmission=atmosphereTransmission, 

1259 strayLightData=strayLightData, 

1260 illumMaskedImage=illumMaskedImage 

1261 ) 

1262 

1263 @timeMethod 

1264 def run(self, ccdExposure, *, camera=None, bias=None, linearizer=None, 

1265 crosstalk=None, crosstalkSources=None, 

1266 dark=None, flat=None, ptc=None, bfKernel=None, bfGains=None, defects=None, 

1267 fringes=pipeBase.Struct(fringes=None), opticsTransmission=None, filterTransmission=None, 

1268 sensorTransmission=None, atmosphereTransmission=None, 

1269 detectorNum=None, strayLightData=None, illumMaskedImage=None, 

1270 isGen3=False, 

1271 ): 

1272 """Perform instrument signature removal on an exposure. 

1273 

1274 Steps included in the ISR processing, in order performed, are: 

1275 - saturation and suspect pixel masking 

1276 - overscan subtraction 

1277 - CCD assembly of individual amplifiers 

1278 - bias subtraction 

1279 - variance image construction 

1280 - linearization of non-linear response 

1281 - crosstalk masking 

1282 - brighter-fatter correction 

1283 - dark subtraction 

1284 - fringe correction 

1285 - stray light subtraction 

1286 - flat correction 

1287 - masking of known defects and camera specific features 

1288 - vignette calculation 

1289 - appending transmission curve and distortion model 

1290 

1291 Parameters 

1292 ---------- 

1293 ccdExposure : `lsst.afw.image.Exposure` 

1294 The raw exposure that is to be run through ISR. The 

1295 exposure is modified by this method. 

1296 camera : `lsst.afw.cameraGeom.Camera`, optional 

1297 The camera geometry for this exposure. Required if 

1298 one or more of ``ccdExposure``, ``bias``, ``dark``, or 

1299 ``flat`` does not have an associated detector. 

1300 bias : `lsst.afw.image.Exposure`, optional 

1301 Bias calibration frame. 

1302 linearizer : `lsst.ip.isr.linearize.LinearizeBase`, optional 

1303 Functor for linearization. 

1304 crosstalk : `lsst.ip.isr.crosstalk.CrosstalkCalib`, optional 

1305 Calibration for crosstalk. 

1306 crosstalkSources : `list`, optional 

1307 List of possible crosstalk sources. 

1308 dark : `lsst.afw.image.Exposure`, optional 

1309 Dark calibration frame. 

1310 flat : `lsst.afw.image.Exposure`, optional 

1311 Flat calibration frame. 

1312 ptc : `lsst.ip.isr.PhotonTransferCurveDataset`, optional 

1313 Photon transfer curve dataset, with, e.g., gains 

1314 and read noise. 

1315 bfKernel : `numpy.ndarray`, optional 

1316 Brighter-fatter kernel. 

1317 bfGains : `dict` of `float`, optional 

1318 Gains used to override the detector's nominal gains for the 

1319 brighter-fatter correction. A dict keyed by amplifier name for 

1320 the detector in question. 

1321 defects : `lsst.ip.isr.Defects`, optional 

1322 List of defects. 

1323 fringes : `lsst.pipe.base.Struct`, optional 

1324 Struct containing the fringe correction data, with 

1325 elements: 

1326 - ``fringes``: fringe calibration frame (`afw.image.Exposure`) 

1327 - ``seed``: random seed derived from the ccdExposureId for random 

1328 number generator (`uint32`) 

1329 opticsTransmission: `lsst.afw.image.TransmissionCurve`, optional 

1330 A ``TransmissionCurve`` that represents the throughput of the, 

1331 optics, to be evaluated in focal-plane coordinates. 

1332 filterTransmission : `lsst.afw.image.TransmissionCurve` 

1333 A ``TransmissionCurve`` that represents the throughput of the 

1334 filter itself, to be evaluated in focal-plane coordinates. 

1335 sensorTransmission : `lsst.afw.image.TransmissionCurve` 

1336 A ``TransmissionCurve`` that represents the throughput of the 

1337 sensor itself, to be evaluated in post-assembly trimmed detector 

1338 coordinates. 

1339 atmosphereTransmission : `lsst.afw.image.TransmissionCurve` 

1340 A ``TransmissionCurve`` that represents the throughput of the 

1341 atmosphere, assumed to be spatially constant. 

1342 detectorNum : `int`, optional 

1343 The integer number for the detector to process. 

1344 isGen3 : bool, optional 

1345 Flag this call to run() as using the Gen3 butler environment. 

1346 strayLightData : `object`, optional 

1347 Opaque object containing calibration information for stray-light 

1348 correction. If `None`, no correction will be performed. 

1349 illumMaskedImage : `lsst.afw.image.MaskedImage`, optional 

1350 Illumination correction image. 

1351 

1352 Returns 

1353 ------- 

1354 result : `lsst.pipe.base.Struct` 

1355 Result struct with component: 

1356 - ``exposure`` : `afw.image.Exposure` 

1357 The fully ISR corrected exposure. 

1358 - ``outputExposure`` : `afw.image.Exposure` 

1359 An alias for `exposure` 

1360 - ``ossThumb`` : `numpy.ndarray` 

1361 Thumbnail image of the exposure after overscan subtraction. 

1362 - ``flattenedThumb`` : `numpy.ndarray` 

1363 Thumbnail image of the exposure after flat-field correction. 

1364 

1365 Raises 

1366 ------ 

1367 RuntimeError 

1368 Raised if a configuration option is set to True, but the 

1369 required calibration data has not been specified. 

1370 

1371 Notes 

1372 ----- 

1373 The current processed exposure can be viewed by setting the 

1374 appropriate lsstDebug entries in the `debug.display` 

1375 dictionary. The names of these entries correspond to some of 

1376 the IsrTaskConfig Boolean options, with the value denoting the 

1377 frame to use. The exposure is shown inside the matching 

1378 option check and after the processing of that step has 

1379 finished. The steps with debug points are: 

1380 

1381 doAssembleCcd 

1382 doBias 

1383 doCrosstalk 

1384 doBrighterFatter 

1385 doDark 

1386 doFringe 

1387 doStrayLight 

1388 doFlat 

1389 

1390 In addition, setting the "postISRCCD" entry displays the 

1391 exposure after all ISR processing has finished. 

1392 

1393 """ 

1394 

1395 if isGen3 is True: 

1396 # Gen3 currently cannot automatically do configuration overrides. 

1397 # DM-15257 looks to discuss this issue. 

1398 # Configure input exposures; 

1399 

1400 ccdExposure = self.ensureExposure(ccdExposure, camera, detectorNum) 

1401 bias = self.ensureExposure(bias, camera, detectorNum) 

1402 dark = self.ensureExposure(dark, camera, detectorNum) 

1403 flat = self.ensureExposure(flat, camera, detectorNum) 

1404 else: 

1405 if isinstance(ccdExposure, ButlerDataRef): 

1406 return self.runDataRef(ccdExposure) 

1407 

1408 ccd = ccdExposure.getDetector() 

1409 filterLabel = ccdExposure.getFilterLabel() 

1410 physicalFilter = isrFunctions.getPhysicalFilter(filterLabel, self.log) 

1411 

1412 if not ccd: 

1413 assert not self.config.doAssembleCcd, "You need a Detector to run assembleCcd." 

1414 ccd = [FakeAmp(ccdExposure, self.config)] 

1415 

1416 # Validate Input 

1417 if self.config.doBias and bias is None: 

1418 raise RuntimeError("Must supply a bias exposure if config.doBias=True.") 

1419 if self.doLinearize(ccd) and linearizer is None: 

1420 raise RuntimeError("Must supply a linearizer if config.doLinearize=True for this detector.") 

1421 if self.config.doBrighterFatter and bfKernel is None: 

1422 raise RuntimeError("Must supply a kernel if config.doBrighterFatter=True.") 

1423 if self.config.doDark and dark is None: 

1424 raise RuntimeError("Must supply a dark exposure if config.doDark=True.") 

1425 if self.config.doFlat and flat is None: 

1426 raise RuntimeError("Must supply a flat exposure if config.doFlat=True.") 

1427 if self.config.doDefect and defects is None: 

1428 raise RuntimeError("Must supply defects if config.doDefect=True.") 

1429 if (self.config.doFringe and physicalFilter in self.fringe.config.filters 

1430 and fringes.fringes is None): 

1431 # The `fringes` object needs to be a pipeBase.Struct, as 

1432 # we use it as a `dict` for the parameters of 

1433 # `FringeTask.run()`. The `fringes.fringes` `list` may 

1434 # not be `None` if `doFringe=True`. Otherwise, raise. 

1435 raise RuntimeError("Must supply fringe exposure as a pipeBase.Struct.") 

1436 if (self.config.doIlluminationCorrection and physicalFilter in self.config.illumFilters 

1437 and illumMaskedImage is None): 

1438 raise RuntimeError("Must supply an illumcor if config.doIlluminationCorrection=True.") 

1439 

1440 # Begin ISR processing. 

1441 if self.config.doConvertIntToFloat: 

1442 self.log.info("Converting exposure to floating point values.") 

1443 ccdExposure = self.convertIntToFloat(ccdExposure) 

1444 

1445 if self.config.doBias and self.config.doBiasBeforeOverscan: 

1446 self.log.info("Applying bias correction.") 

1447 isrFunctions.biasCorrection(ccdExposure.getMaskedImage(), bias.getMaskedImage(), 

1448 trimToFit=self.config.doTrimToMatchCalib) 

1449 self.debugView(ccdExposure, "doBias") 

1450 

1451 # Amplifier level processing. 

1452 overscans = [] 

1453 for amp in ccd: 

1454 # if ccdExposure is one amp, 

1455 # check for coverage to prevent performing ops multiple times 

1456 if ccdExposure.getBBox().contains(amp.getBBox()): 

1457 # Check for fully masked bad amplifiers, 

1458 # and generate masks for SUSPECT and SATURATED values. 

1459 badAmp = self.maskAmplifier(ccdExposure, amp, defects) 

1460 

1461 if self.config.doOverscan and not badAmp: 

1462 # Overscan correction on amp-by-amp basis. 

1463 overscanResults = self.overscanCorrection(ccdExposure, amp) 

1464 self.log.debug("Corrected overscan for amplifier %s.", amp.getName()) 

1465 if overscanResults is not None and \ 

1466 self.config.qa is not None and self.config.qa.saveStats is True: 

1467 if isinstance(overscanResults.overscanFit, float): 

1468 qaMedian = overscanResults.overscanFit 

1469 qaStdev = float("NaN") 

1470 else: 

1471 qaStats = afwMath.makeStatistics(overscanResults.overscanFit, 

1472 afwMath.MEDIAN | afwMath.STDEVCLIP) 

1473 qaMedian = qaStats.getValue(afwMath.MEDIAN) 

1474 qaStdev = qaStats.getValue(afwMath.STDEVCLIP) 

1475 

1476 self.metadata[f"FIT MEDIAN {amp.getName()}"] = qaMedian 

1477 self.metadata[f"FIT STDEV {amp.getName()}"] = qaStdev 

1478 self.log.debug(" Overscan stats for amplifer %s: %f +/- %f", 

1479 amp.getName(), qaMedian, qaStdev) 

1480 

1481 # Residuals after overscan correction 

1482 qaStatsAfter = afwMath.makeStatistics(overscanResults.overscanImage, 

1483 afwMath.MEDIAN | afwMath.STDEVCLIP) 

1484 qaMedianAfter = qaStatsAfter.getValue(afwMath.MEDIAN) 

1485 qaStdevAfter = qaStatsAfter.getValue(afwMath.STDEVCLIP) 

1486 

1487 self.metadata[f"RESIDUAL MEDIAN {amp.getName()}"] = qaMedianAfter 

1488 self.metadata[f"RESIDUAL STDEV {amp.getName()}"] = qaStdevAfter 

1489 self.log.debug(" Overscan stats for amplifer %s after correction: %f +/- %f", 

1490 amp.getName(), qaMedianAfter, qaStdevAfter) 

1491 

1492 ccdExposure.getMetadata().set('OVERSCAN', "Overscan corrected") 

1493 else: 

1494 if badAmp: 

1495 self.log.warning("Amplifier %s is bad.", amp.getName()) 

1496 overscanResults = None 

1497 

1498 overscans.append(overscanResults if overscanResults is not None else None) 

1499 else: 

1500 self.log.info("Skipped OSCAN for %s.", amp.getName()) 

1501 

1502 if self.config.doCrosstalk and self.config.doCrosstalkBeforeAssemble: 

1503 self.log.info("Applying crosstalk correction.") 

1504 self.crosstalk.run(ccdExposure, crosstalk=crosstalk, 

1505 crosstalkSources=crosstalkSources, camera=camera) 

1506 self.debugView(ccdExposure, "doCrosstalk") 

1507 

1508 if self.config.doAssembleCcd: 

1509 self.log.info("Assembling CCD from amplifiers.") 

1510 ccdExposure = self.assembleCcd.assembleCcd(ccdExposure) 

1511 

1512 if self.config.expectWcs and not ccdExposure.getWcs(): 

1513 self.log.warning("No WCS found in input exposure.") 

1514 self.debugView(ccdExposure, "doAssembleCcd") 

1515 

1516 ossThumb = None 

1517 if self.config.qa.doThumbnailOss: 

1518 ossThumb = isrQa.makeThumbnail(ccdExposure, isrQaConfig=self.config.qa) 

1519 

1520 if self.config.doBias and not self.config.doBiasBeforeOverscan: 

1521 self.log.info("Applying bias correction.") 

1522 isrFunctions.biasCorrection(ccdExposure.getMaskedImage(), bias.getMaskedImage(), 

1523 trimToFit=self.config.doTrimToMatchCalib) 

1524 self.debugView(ccdExposure, "doBias") 

1525 

1526 if self.config.doVariance: 

1527 for amp, overscanResults in zip(ccd, overscans): 

1528 if ccdExposure.getBBox().contains(amp.getBBox()): 

1529 self.log.debug("Constructing variance map for amplifer %s.", amp.getName()) 

1530 ampExposure = ccdExposure.Factory(ccdExposure, amp.getBBox()) 

1531 if overscanResults is not None: 

1532 self.updateVariance(ampExposure, amp, 

1533 overscanImage=overscanResults.overscanImage, 

1534 ptcDataset=ptc) 

1535 else: 

1536 self.updateVariance(ampExposure, amp, 

1537 overscanImage=None, 

1538 ptcDataset=ptc) 

1539 if self.config.qa is not None and self.config.qa.saveStats is True: 

1540 qaStats = afwMath.makeStatistics(ampExposure.getVariance(), 

1541 afwMath.MEDIAN | afwMath.STDEVCLIP) 

1542 self.metadata[f"ISR VARIANCE {amp.getName()} MEDIAN"] = \ 

1543 qaStats.getValue(afwMath.MEDIAN) 

1544 self.metadata[f"ISR VARIANCE {amp.getName()} STDEV"] = \ 

1545 qaStats.getValue(afwMath.STDEVCLIP) 

1546 self.log.debug(" Variance stats for amplifer %s: %f +/- %f.", 

1547 amp.getName(), qaStats.getValue(afwMath.MEDIAN), 

1548 qaStats.getValue(afwMath.STDEVCLIP)) 

1549 if self.config.maskNegativeVariance: 

1550 self.maskNegativeVariance(ccdExposure) 

1551 

1552 if self.doLinearize(ccd): 

1553 self.log.info("Applying linearizer.") 

1554 linearizer.applyLinearity(image=ccdExposure.getMaskedImage().getImage(), 

1555 detector=ccd, log=self.log) 

1556 

1557 if self.config.doCrosstalk and not self.config.doCrosstalkBeforeAssemble: 

1558 self.log.info("Applying crosstalk correction.") 

1559 self.crosstalk.run(ccdExposure, crosstalk=crosstalk, 

1560 crosstalkSources=crosstalkSources, isTrimmed=True) 

1561 self.debugView(ccdExposure, "doCrosstalk") 

1562 

1563 # Masking block. Optionally mask known defects, NAN/inf pixels, 

1564 # widen trails, and do anything else the camera needs. Saturated and 

1565 # suspect pixels have already been masked. 

1566 if self.config.doDefect: 

1567 self.log.info("Masking defects.") 

1568 self.maskDefect(ccdExposure, defects) 

1569 

1570 if self.config.numEdgeSuspect > 0: 

1571 self.log.info("Masking edges as SUSPECT.") 

1572 self.maskEdges(ccdExposure, numEdgePixels=self.config.numEdgeSuspect, 

1573 maskPlane="SUSPECT", level=self.config.edgeMaskLevel) 

1574 

1575 if self.config.doNanMasking: 

1576 self.log.info("Masking non-finite (NAN, inf) value pixels.") 

1577 self.maskNan(ccdExposure) 

1578 

1579 if self.config.doWidenSaturationTrails: 

1580 self.log.info("Widening saturation trails.") 

1581 isrFunctions.widenSaturationTrails(ccdExposure.getMaskedImage().getMask()) 

1582 

1583 if self.config.doCameraSpecificMasking: 

1584 self.log.info("Masking regions for camera specific reasons.") 

1585 self.masking.run(ccdExposure) 

1586 

1587 if self.config.doBrighterFatter: 

1588 # We need to apply flats and darks before we can interpolate, and 

1589 # we need to interpolate before we do B-F, but we do B-F without 

1590 # the flats and darks applied so we can work in units of electrons 

1591 # or holes. This context manager applies and then removes the darks 

1592 # and flats. 

1593 # 

1594 # We also do not want to interpolate values here, so operate on 

1595 # temporary images so we can apply only the BF-correction and roll 

1596 # back the interpolation. 

1597 interpExp = ccdExposure.clone() 

1598 with self.flatContext(interpExp, flat, dark): 

1599 isrFunctions.interpolateFromMask( 

1600 maskedImage=interpExp.getMaskedImage(), 

1601 fwhm=self.config.fwhm, 

1602 growSaturatedFootprints=self.config.growSaturationFootprintSize, 

1603 maskNameList=list(self.config.brighterFatterMaskListToInterpolate) 

1604 ) 

1605 bfExp = interpExp.clone() 

1606 

1607 self.log.info("Applying brighter-fatter correction using kernel type %s / gains %s.", 

1608 type(bfKernel), type(bfGains)) 

1609 bfResults = isrFunctions.brighterFatterCorrection(bfExp, bfKernel, 

1610 self.config.brighterFatterMaxIter, 

1611 self.config.brighterFatterThreshold, 

1612 self.config.brighterFatterApplyGain, 

1613 bfGains) 

1614 if bfResults[1] == self.config.brighterFatterMaxIter: 

1615 self.log.warning("Brighter-fatter correction did not converge, final difference %f.", 

1616 bfResults[0]) 

1617 else: 

1618 self.log.info("Finished brighter-fatter correction in %d iterations.", 

1619 bfResults[1]) 

1620 image = ccdExposure.getMaskedImage().getImage() 

1621 bfCorr = bfExp.getMaskedImage().getImage() 

1622 bfCorr -= interpExp.getMaskedImage().getImage() 

1623 image += bfCorr 

1624 

1625 # Applying the brighter-fatter correction applies a 

1626 # convolution to the science image. At the edges this 

1627 # convolution may not have sufficient valid pixels to 

1628 # produce a valid correction. Mark pixels within the size 

1629 # of the brighter-fatter kernel as EDGE to warn of this 

1630 # fact. 

1631 self.log.info("Ensuring image edges are masked as EDGE to the brighter-fatter kernel size.") 

1632 self.maskEdges(ccdExposure, numEdgePixels=numpy.max(bfKernel.shape) // 2, 

1633 maskPlane="EDGE") 

1634 

1635 if self.config.brighterFatterMaskGrowSize > 0: 

1636 self.log.info("Growing masks to account for brighter-fatter kernel convolution.") 

1637 for maskPlane in self.config.brighterFatterMaskListToInterpolate: 

1638 isrFunctions.growMasks(ccdExposure.getMask(), 

1639 radius=self.config.brighterFatterMaskGrowSize, 

1640 maskNameList=maskPlane, 

1641 maskValue=maskPlane) 

1642 

1643 self.debugView(ccdExposure, "doBrighterFatter") 

1644 

1645 if self.config.doDark: 

1646 self.log.info("Applying dark correction.") 

1647 self.darkCorrection(ccdExposure, dark) 

1648 self.debugView(ccdExposure, "doDark") 

1649 

1650 if self.config.doFringe and not self.config.fringeAfterFlat: 

1651 self.log.info("Applying fringe correction before flat.") 

1652 self.fringe.run(ccdExposure, **fringes.getDict()) 

1653 self.debugView(ccdExposure, "doFringe") 

1654 

1655 if self.config.doStrayLight and self.strayLight.check(ccdExposure): 

1656 self.log.info("Checking strayLight correction.") 

1657 self.strayLight.run(ccdExposure, strayLightData) 

1658 self.debugView(ccdExposure, "doStrayLight") 

1659 

1660 if self.config.doFlat: 

1661 self.log.info("Applying flat correction.") 

1662 self.flatCorrection(ccdExposure, flat) 

1663 self.debugView(ccdExposure, "doFlat") 

1664 

1665 if self.config.doApplyGains: 

1666 self.log.info("Applying gain correction instead of flat.") 

1667 if self.config.usePtcGains: 

1668 self.log.info("Using gains from the Photon Transfer Curve.") 

1669 isrFunctions.applyGains(ccdExposure, self.config.normalizeGains, 

1670 ptcGains=ptc.gain) 

1671 else: 

1672 isrFunctions.applyGains(ccdExposure, self.config.normalizeGains) 

1673 

1674 if self.config.doFringe and self.config.fringeAfterFlat: 

1675 self.log.info("Applying fringe correction after flat.") 

1676 self.fringe.run(ccdExposure, **fringes.getDict()) 

1677 

1678 if self.config.doVignette: 

1679 self.log.info("Constructing Vignette polygon.") 

1680 self.vignettePolygon = self.vignette.run(ccdExposure) 

1681 

1682 if self.config.vignette.doWriteVignettePolygon: 

1683 self.setValidPolygonIntersect(ccdExposure, self.vignettePolygon) 

1684 

1685 if self.config.doAttachTransmissionCurve: 

1686 self.log.info("Adding transmission curves.") 

1687 isrFunctions.attachTransmissionCurve(ccdExposure, opticsTransmission=opticsTransmission, 

1688 filterTransmission=filterTransmission, 

1689 sensorTransmission=sensorTransmission, 

1690 atmosphereTransmission=atmosphereTransmission) 

1691 

1692 flattenedThumb = None 

1693 if self.config.qa.doThumbnailFlattened: 

1694 flattenedThumb = isrQa.makeThumbnail(ccdExposure, isrQaConfig=self.config.qa) 

1695 

1696 if self.config.doIlluminationCorrection and physicalFilter in self.config.illumFilters: 

1697 self.log.info("Performing illumination correction.") 

1698 isrFunctions.illuminationCorrection(ccdExposure.getMaskedImage(), 

1699 illumMaskedImage, illumScale=self.config.illumScale, 

1700 trimToFit=self.config.doTrimToMatchCalib) 

1701 

1702 preInterpExp = None 

1703 if self.config.doSaveInterpPixels: 

1704 preInterpExp = ccdExposure.clone() 

1705 

1706 # Reset and interpolate bad pixels. 

1707 # 

1708 # Large contiguous bad regions (which should have the BAD mask 

1709 # bit set) should have their values set to the image median. 

1710 # This group should include defects and bad amplifiers. As the 

1711 # area covered by these defects are large, there's little 

1712 # reason to expect that interpolation would provide a more 

1713 # useful value. 

1714 # 

1715 # Smaller defects can be safely interpolated after the larger 

1716 # regions have had their pixel values reset. This ensures 

1717 # that the remaining defects adjacent to bad amplifiers (as an 

1718 # example) do not attempt to interpolate extreme values. 

1719 if self.config.doSetBadRegions: 

1720 badPixelCount, badPixelValue = isrFunctions.setBadRegions(ccdExposure) 

1721 if badPixelCount > 0: 

1722 self.log.info("Set %d BAD pixels to %f.", badPixelCount, badPixelValue) 

1723 

1724 if self.config.doInterpolate: 

1725 self.log.info("Interpolating masked pixels.") 

1726 isrFunctions.interpolateFromMask( 

1727 maskedImage=ccdExposure.getMaskedImage(), 

1728 fwhm=self.config.fwhm, 

1729 growSaturatedFootprints=self.config.growSaturationFootprintSize, 

1730 maskNameList=list(self.config.maskListToInterpolate) 

1731 ) 

1732 

1733 self.roughZeroPoint(ccdExposure) 

1734 

1735 # correct for amp offsets within the CCD 

1736 if self.config.doAmpOffset: 

1737 self.log.info("Correcting amp offsets.") 

1738 self.ampOffset.run(ccdExposure) 

1739 

1740 if self.config.doMeasureBackground: 

1741 self.log.info("Measuring background level.") 

1742 self.measureBackground(ccdExposure, self.config.qa) 

1743 

1744 if self.config.qa is not None and self.config.qa.saveStats is True: 

1745 for amp in ccd: 

1746 ampExposure = ccdExposure.Factory(ccdExposure, amp.getBBox()) 

1747 qaStats = afwMath.makeStatistics(ampExposure.getImage(), 

1748 afwMath.MEDIAN | afwMath.STDEVCLIP) 

1749 self.metadata[f"ISR BACKGROUND {amp.getName()} MEDIAN"] = qaStats.getValue(afwMath.MEDIAN) 

1750 self.metadata[f"ISR BACKGROUND {amp.getName()} STDEV"] = \ 

1751 qaStats.getValue(afwMath.STDEVCLIP) 

1752 self.log.debug(" Background stats for amplifer %s: %f +/- %f", 

1753 amp.getName(), qaStats.getValue(afwMath.MEDIAN), 

1754 qaStats.getValue(afwMath.STDEVCLIP)) 

1755 

1756 self.debugView(ccdExposure, "postISRCCD") 

1757 

1758 return pipeBase.Struct( 

1759 exposure=ccdExposure, 

1760 ossThumb=ossThumb, 

1761 flattenedThumb=flattenedThumb, 

1762 

1763 preInterpExposure=preInterpExp, 

1764 outputExposure=ccdExposure, 

1765 outputOssThumbnail=ossThumb, 

1766 outputFlattenedThumbnail=flattenedThumb, 

1767 ) 

1768 

1769 @timeMethod 

1770 def runDataRef(self, sensorRef): 

1771 """Perform instrument signature removal on a ButlerDataRef of a Sensor. 

1772 

1773 This method contains the `CmdLineTask` interface to the ISR 

1774 processing. All IO is handled here, freeing the `run()` method 

1775 to manage only pixel-level calculations. The steps performed 

1776 are: 

1777 - Read in necessary detrending/isr/calibration data. 

1778 - Process raw exposure in `run()`. 

1779 - Persist the ISR-corrected exposure as "postISRCCD" if 

1780 config.doWrite=True. 

1781 

1782 Parameters 

1783 ---------- 

1784 sensorRef : `daf.persistence.butlerSubset.ButlerDataRef` 

1785 DataRef of the detector data to be processed 

1786 

1787 Returns 

1788 ------- 

1789 result : `lsst.pipe.base.Struct` 

1790 Result struct with component: 

1791 - ``exposure`` : `afw.image.Exposure` 

1792 The fully ISR corrected exposure. 

1793 

1794 Raises 

1795 ------ 

1796 RuntimeError 

1797 Raised if a configuration option is set to True, but the 

1798 required calibration data does not exist. 

1799 

1800 """ 

1801 self.log.info("Performing ISR on sensor %s.", sensorRef.dataId) 

1802 

1803 ccdExposure = sensorRef.get(self.config.datasetType) 

1804 

1805 camera = sensorRef.get("camera") 

1806 isrData = self.readIsrData(sensorRef, ccdExposure) 

1807 

1808 result = self.run(ccdExposure, camera=camera, **isrData.getDict()) 

1809 

1810 if self.config.doWrite: 

1811 sensorRef.put(result.exposure, "postISRCCD") 

1812 if result.preInterpExposure is not None: 

1813 sensorRef.put(result.preInterpExposure, "postISRCCD_uninterpolated") 

1814 if result.ossThumb is not None: 

1815 isrQa.writeThumbnail(sensorRef, result.ossThumb, "ossThumb") 

1816 if result.flattenedThumb is not None: 

1817 isrQa.writeThumbnail(sensorRef, result.flattenedThumb, "flattenedThumb") 

1818 

1819 return result 

1820 

1821 def getIsrExposure(self, dataRef, datasetType, dateObs=None, immediate=True): 

1822 """Retrieve a calibration dataset for removing instrument signature. 

1823 

1824 Parameters 

1825 ---------- 

1826 

1827 dataRef : `daf.persistence.butlerSubset.ButlerDataRef` 

1828 DataRef of the detector data to find calibration datasets 

1829 for. 

1830 datasetType : `str` 

1831 Type of dataset to retrieve (e.g. 'bias', 'flat', etc). 

1832 dateObs : `str`, optional 

1833 Date of the observation. Used to correct butler failures 

1834 when using fallback filters. 

1835 immediate : `Bool` 

1836 If True, disable butler proxies to enable error handling 

1837 within this routine. 

1838 

1839 Returns 

1840 ------- 

1841 exposure : `lsst.afw.image.Exposure` 

1842 Requested calibration frame. 

1843 

1844 Raises 

1845 ------ 

1846 RuntimeError 

1847 Raised if no matching calibration frame can be found. 

1848 """ 

1849 try: 

1850 exp = dataRef.get(datasetType, immediate=immediate) 

1851 except Exception as exc1: 

1852 if not self.config.fallbackFilterName: 

1853 raise RuntimeError("Unable to retrieve %s for %s: %s." % (datasetType, dataRef.dataId, exc1)) 

1854 try: 

1855 if self.config.useFallbackDate and dateObs: 

1856 exp = dataRef.get(datasetType, filter=self.config.fallbackFilterName, 

1857 dateObs=dateObs, immediate=immediate) 

1858 else: 

1859 exp = dataRef.get(datasetType, filter=self.config.fallbackFilterName, immediate=immediate) 

1860 except Exception as exc2: 

1861 raise RuntimeError("Unable to retrieve %s for %s, even with fallback filter %s: %s AND %s." % 

1862 (datasetType, dataRef.dataId, self.config.fallbackFilterName, exc1, exc2)) 

1863 self.log.warning("Using fallback calibration from filter %s.", self.config.fallbackFilterName) 

1864 

1865 if self.config.doAssembleIsrExposures: 

1866 exp = self.assembleCcd.assembleCcd(exp) 

1867 return exp 

1868 

1869 def ensureExposure(self, inputExp, camera=None, detectorNum=None): 

1870 """Ensure that the data returned by Butler is a fully constructed exp. 

1871 

1872 ISR requires exposure-level image data for historical reasons, so if we 

1873 did not recieve that from Butler, construct it from what we have, 

1874 modifying the input in place. 

1875 

1876 Parameters 

1877 ---------- 

1878 inputExp : `lsst.afw.image.Exposure`, `lsst.afw.image.DecoratedImageU`, 

1879 or `lsst.afw.image.ImageF` 

1880 The input data structure obtained from Butler. 

1881 camera : `lsst.afw.cameraGeom.camera`, optional 

1882 The camera associated with the image. Used to find the appropriate 

1883 detector if detector is not already set. 

1884 detectorNum : `int`, optional 

1885 The detector in the camera to attach, if the detector is not 

1886 already set. 

1887 

1888 Returns 

1889 ------- 

1890 inputExp : `lsst.afw.image.Exposure` 

1891 The re-constructed exposure, with appropriate detector parameters. 

1892 

1893 Raises 

1894 ------ 

1895 TypeError 

1896 Raised if the input data cannot be used to construct an exposure. 

1897 """ 

1898 if isinstance(inputExp, afwImage.DecoratedImageU): 

1899 inputExp = afwImage.makeExposure(afwImage.makeMaskedImage(inputExp)) 

1900 elif isinstance(inputExp, afwImage.ImageF): 

1901 inputExp = afwImage.makeExposure(afwImage.makeMaskedImage(inputExp)) 

1902 elif isinstance(inputExp, afwImage.MaskedImageF): 

1903 inputExp = afwImage.makeExposure(inputExp) 

1904 elif isinstance(inputExp, afwImage.Exposure): 

1905 pass 

1906 elif inputExp is None: 

1907 # Assume this will be caught by the setup if it is a problem. 

1908 return inputExp 

1909 else: 

1910 raise TypeError("Input Exposure is not known type in isrTask.ensureExposure: %s." % 

1911 (type(inputExp), )) 

1912 

1913 if inputExp.getDetector() is None: 

1914 if camera is None or detectorNum is None: 

1915 raise RuntimeError('Must supply both a camera and detector number when using exposures ' 

1916 'without a detector set.') 

1917 inputExp.setDetector(camera[detectorNum]) 

1918 

1919 return inputExp 

1920 

1921 def convertIntToFloat(self, exposure): 

1922 """Convert exposure image from uint16 to float. 

1923 

1924 If the exposure does not need to be converted, the input is 

1925 immediately returned. For exposures that are converted to use 

1926 floating point pixels, the variance is set to unity and the 

1927 mask to zero. 

1928 

1929 Parameters 

1930 ---------- 

1931 exposure : `lsst.afw.image.Exposure` 

1932 The raw exposure to be converted. 

1933 

1934 Returns 

1935 ------- 

1936 newexposure : `lsst.afw.image.Exposure` 

1937 The input ``exposure``, converted to floating point pixels. 

1938 

1939 Raises 

1940 ------ 

1941 RuntimeError 

1942 Raised if the exposure type cannot be converted to float. 

1943 

1944 """ 

1945 if isinstance(exposure, afwImage.ExposureF): 

1946 # Nothing to be done 

1947 self.log.debug("Exposure already of type float.") 

1948 return exposure 

1949 if not hasattr(exposure, "convertF"): 

1950 raise RuntimeError("Unable to convert exposure (%s) to float." % type(exposure)) 

1951 

1952 newexposure = exposure.convertF() 

1953 newexposure.variance[:] = 1 

1954 newexposure.mask[:] = 0x0 

1955 

1956 return newexposure 

1957 

1958 def maskAmplifier(self, ccdExposure, amp, defects): 

1959 """Identify bad amplifiers, saturated and suspect pixels. 

1960 

1961 Parameters 

1962 ---------- 

1963 ccdExposure : `lsst.afw.image.Exposure` 

1964 Input exposure to be masked. 

1965 amp : `lsst.afw.table.AmpInfoCatalog` 

1966 Catalog of parameters defining the amplifier on this 

1967 exposure to mask. 

1968 defects : `lsst.ip.isr.Defects` 

1969 List of defects. Used to determine if the entire 

1970 amplifier is bad. 

1971 

1972 Returns 

1973 ------- 

1974 badAmp : `Bool` 

1975 If this is true, the entire amplifier area is covered by 

1976 defects and unusable. 

1977 

1978 """ 

1979 maskedImage = ccdExposure.getMaskedImage() 

1980 

1981 badAmp = False 

1982 

1983 # Check if entire amp region is defined as a defect 

1984 # NB: need to use amp.getBBox() for correct comparison with current 

1985 # defects definition. 

1986 if defects is not None: 

1987 badAmp = bool(sum([v.getBBox().contains(amp.getBBox()) for v in defects])) 

1988 

1989 # In the case of a bad amp, we will set mask to "BAD" 

1990 # (here use amp.getRawBBox() for correct association with pixels in 

1991 # current ccdExposure). 

1992 if badAmp: 

1993 dataView = afwImage.MaskedImageF(maskedImage, amp.getRawBBox(), 

1994 afwImage.PARENT) 

1995 maskView = dataView.getMask() 

1996 maskView |= maskView.getPlaneBitMask("BAD") 

1997 del maskView 

1998 return badAmp 

1999 

2000 # Mask remaining defects after assembleCcd() to allow for defects that 

2001 # cross amplifier boundaries. Saturation and suspect pixels can be 

2002 # masked now, though. 

2003 limits = dict() 

2004 if self.config.doSaturation and not badAmp: 

2005 limits.update({self.config.saturatedMaskName: amp.getSaturation()}) 

2006 if self.config.doSuspect and not badAmp: 

2007 limits.update({self.config.suspectMaskName: amp.getSuspectLevel()}) 

2008 if math.isfinite(self.config.saturation): 

2009 limits.update({self.config.saturatedMaskName: self.config.saturation}) 

2010 

2011 for maskName, maskThreshold in limits.items(): 

2012 if not math.isnan(maskThreshold): 

2013 dataView = maskedImage.Factory(maskedImage, amp.getRawBBox()) 

2014 isrFunctions.makeThresholdMask( 

2015 maskedImage=dataView, 

2016 threshold=maskThreshold, 

2017 growFootprints=0, 

2018 maskName=maskName 

2019 ) 

2020 

2021 # Determine if we've fully masked this amplifier with SUSPECT and 

2022 # SAT pixels. 

2023 maskView = afwImage.Mask(maskedImage.getMask(), amp.getRawDataBBox(), 

2024 afwImage.PARENT) 

2025 maskVal = maskView.getPlaneBitMask([self.config.saturatedMaskName, 

2026 self.config.suspectMaskName]) 

2027 if numpy.all(maskView.getArray() & maskVal > 0): 

2028 badAmp = True 

2029 maskView |= maskView.getPlaneBitMask("BAD") 

2030 

2031 return badAmp 

2032 

2033 def overscanCorrection(self, ccdExposure, amp): 

2034 """Apply overscan correction in place. 

2035 

2036 This method does initial pixel rejection of the overscan 

2037 region. The overscan can also be optionally segmented to 

2038 allow for discontinuous overscan responses to be fit 

2039 separately. The actual overscan subtraction is performed by 

2040 the `lsst.ip.isr.isrFunctions.overscanCorrection` function, 

2041 which is called here after the amplifier is preprocessed. 

2042 

2043 Parameters 

2044 ---------- 

2045 ccdExposure : `lsst.afw.image.Exposure` 

2046 Exposure to have overscan correction performed. 

2047 amp : `lsst.afw.cameraGeom.Amplifer` 

2048 The amplifier to consider while correcting the overscan. 

2049 

2050 Returns 

2051 ------- 

2052 overscanResults : `lsst.pipe.base.Struct` 

2053 Result struct with components: 

2054 - ``imageFit`` : scalar or `lsst.afw.image.Image` 

2055 Value or fit subtracted from the amplifier image data. 

2056 - ``overscanFit`` : scalar or `lsst.afw.image.Image` 

2057 Value or fit subtracted from the overscan image data. 

2058 - ``overscanImage`` : `lsst.afw.image.Image` 

2059 Image of the overscan region with the overscan 

2060 correction applied. This quantity is used to estimate 

2061 the amplifier read noise empirically. 

2062 

2063 Raises 

2064 ------ 

2065 RuntimeError 

2066 Raised if the ``amp`` does not contain raw pixel information. 

2067 

2068 See Also 

2069 -------- 

2070 lsst.ip.isr.isrFunctions.overscanCorrection 

2071 """ 

2072 if amp.getRawHorizontalOverscanBBox().isEmpty(): 

2073 self.log.info("ISR_OSCAN: No overscan region. Not performing overscan correction.") 

2074 return None 

2075 

2076 statControl = afwMath.StatisticsControl() 

2077 statControl.setAndMask(ccdExposure.mask.getPlaneBitMask("SAT")) 

2078 

2079 # Determine the bounding boxes 

2080 dataBBox = amp.getRawDataBBox() 

2081 oscanBBox = amp.getRawHorizontalOverscanBBox() 

2082 dx0 = 0 

2083 dx1 = 0 

2084 

2085 prescanBBox = amp.getRawPrescanBBox() 

2086 if (oscanBBox.getBeginX() > prescanBBox.getBeginX()): # amp is at the right 

2087 dx0 += self.config.overscanNumLeadingColumnsToSkip 

2088 dx1 -= self.config.overscanNumTrailingColumnsToSkip 

2089 else: 

2090 dx0 += self.config.overscanNumTrailingColumnsToSkip 

2091 dx1 -= self.config.overscanNumLeadingColumnsToSkip 

2092 

2093 # Determine if we need to work on subregions of the amplifier 

2094 # and overscan. 

2095 imageBBoxes = [] 

2096 overscanBBoxes = [] 

2097 

2098 if ((self.config.overscanBiasJump 

2099 and self.config.overscanBiasJumpLocation) 

2100 and (ccdExposure.getMetadata().exists(self.config.overscanBiasJumpKeyword) 

2101 and ccdExposure.getMetadata().getScalar(self.config.overscanBiasJumpKeyword) in 

2102 self.config.overscanBiasJumpDevices)): 

2103 if amp.getReadoutCorner() in (ReadoutCorner.LL, ReadoutCorner.LR): 

2104 yLower = self.config.overscanBiasJumpLocation 

2105 yUpper = dataBBox.getHeight() - yLower 

2106 else: 

2107 yUpper = self.config.overscanBiasJumpLocation 

2108 yLower = dataBBox.getHeight() - yUpper 

2109 

2110 imageBBoxes.append(lsst.geom.Box2I(dataBBox.getBegin(), 

2111 lsst.geom.Extent2I(dataBBox.getWidth(), yLower))) 

2112 overscanBBoxes.append(lsst.geom.Box2I(oscanBBox.getBegin() + lsst.geom.Extent2I(dx0, 0), 

2113 lsst.geom.Extent2I(oscanBBox.getWidth() - dx0 + dx1, 

2114 yLower))) 

2115 

2116 imageBBoxes.append(lsst.geom.Box2I(dataBBox.getBegin() + lsst.geom.Extent2I(0, yLower), 

2117 lsst.geom.Extent2I(dataBBox.getWidth(), yUpper))) 

2118 overscanBBoxes.append(lsst.geom.Box2I(oscanBBox.getBegin() + lsst.geom.Extent2I(dx0, yLower), 

2119 lsst.geom.Extent2I(oscanBBox.getWidth() - dx0 + dx1, 

2120 yUpper))) 

2121 else: 

2122 imageBBoxes.append(lsst.geom.Box2I(dataBBox.getBegin(), 

2123 lsst.geom.Extent2I(dataBBox.getWidth(), dataBBox.getHeight()))) 

2124 overscanBBoxes.append(lsst.geom.Box2I(oscanBBox.getBegin() + lsst.geom.Extent2I(dx0, 0), 

2125 lsst.geom.Extent2I(oscanBBox.getWidth() - dx0 + dx1, 

2126 oscanBBox.getHeight()))) 

2127 

2128 # Perform overscan correction on subregions, ensuring saturated 

2129 # pixels are masked. 

2130 for imageBBox, overscanBBox in zip(imageBBoxes, overscanBBoxes): 

2131 ampImage = ccdExposure.maskedImage[imageBBox] 

2132 overscanImage = ccdExposure.maskedImage[overscanBBox] 

2133 

2134 overscanArray = overscanImage.image.array 

2135 median = numpy.ma.median(numpy.ma.masked_where(overscanImage.mask.array, overscanArray)) 

2136 bad = numpy.where(numpy.abs(overscanArray - median) > self.config.overscanMaxDev) 

2137 overscanImage.mask.array[bad] = overscanImage.mask.getPlaneBitMask("SAT") 

2138 

2139 statControl = afwMath.StatisticsControl() 

2140 statControl.setAndMask(ccdExposure.mask.getPlaneBitMask("SAT")) 

2141 

2142 overscanResults = self.overscan.run(ampImage.getImage(), overscanImage, amp) 

2143 

2144 # Measure average overscan levels and record them in the metadata. 

2145 levelStat = afwMath.MEDIAN 

2146 sigmaStat = afwMath.STDEVCLIP 

2147 

2148 sctrl = afwMath.StatisticsControl(self.config.qa.flatness.clipSigma, 

2149 self.config.qa.flatness.nIter) 

2150 metadata = ccdExposure.getMetadata() 

2151 ampNum = amp.getName() 

2152 # if self.config.overscanFitType in ("MEDIAN", "MEAN", "MEANCLIP"): 

2153 if isinstance(overscanResults.overscanFit, float): 

2154 metadata[f"ISR_OSCAN_LEVEL{ampNum}"] = overscanResults.overscanFit 

2155 metadata[f"ISR_OSCAN_SIGMA{ampNum}"] = 0.0 

2156 else: 

2157 stats = afwMath.makeStatistics(overscanResults.overscanFit, levelStat | sigmaStat, sctrl) 

2158 metadata[f"ISR_OSCAN_LEVEL{ampNum}"] = stats.getValue(levelStat) 

2159 metadata[f"ISR_OSCAN_SIGMA%{ampNum}"] = stats.getValue(sigmaStat) 

2160 

2161 return overscanResults 

2162 

2163 def updateVariance(self, ampExposure, amp, overscanImage=None, ptcDataset=None): 

2164 """Set the variance plane using the gain and read noise 

2165 

2166 The read noise is calculated from the ``overscanImage`` if the 

2167 ``doEmpiricalReadNoise`` option is set in the configuration; otherwise 

2168 the value from the amplifier data is used. 

2169 

2170 Parameters 

2171 ---------- 

2172 ampExposure : `lsst.afw.image.Exposure` 

2173 Exposure to process. 

2174 amp : `lsst.afw.table.AmpInfoRecord` or `FakeAmp` 

2175 Amplifier detector data. 

2176 overscanImage : `lsst.afw.image.MaskedImage`, optional. 

2177 Image of overscan, required only for empirical read noise. 

2178 ptcDataset : `lsst.ip.isr.PhotonTransferCurveDataset`, optional 

2179 PTC dataset containing the gains and read noise. 

2180 

2181 

2182 Raises 

2183 ------ 

2184 RuntimeError 

2185 Raised if either ``usePtcGains`` of ``usePtcReadNoise`` 

2186 are ``True``, but ptcDataset is not provided. 

2187 

2188 Raised if ```doEmpiricalReadNoise`` is ``True`` but 

2189 ``overscanImage`` is ``None``. 

2190 

2191 See also 

2192 -------- 

2193 lsst.ip.isr.isrFunctions.updateVariance 

2194 """ 

2195 maskPlanes = [self.config.saturatedMaskName, self.config.suspectMaskName] 

2196 if self.config.usePtcGains: 

2197 if ptcDataset is None: 

2198 raise RuntimeError("No ptcDataset provided to use PTC gains.") 

2199 else: 

2200 gain = ptcDataset.gain[amp.getName()] 

2201 self.log.info("Using gain from Photon Transfer Curve.") 

2202 else: 

2203 gain = amp.getGain() 

2204 

2205 if math.isnan(gain): 

2206 gain = 1.0 

2207 self.log.warning("Gain set to NAN! Updating to 1.0 to generate Poisson variance.") 

2208 elif gain <= 0: 

2209 patchedGain = 1.0 

2210 self.log.warning("Gain for amp %s == %g <= 0; setting to %f.", 

2211 amp.getName(), gain, patchedGain) 

2212 gain = patchedGain 

2213 

2214 if self.config.doEmpiricalReadNoise and overscanImage is None: 

2215 raise RuntimeError("Overscan is none for EmpiricalReadNoise.") 

2216 

2217 if self.config.doEmpiricalReadNoise and overscanImage is not None: 

2218 stats = afwMath.StatisticsControl() 

2219 stats.setAndMask(overscanImage.mask.getPlaneBitMask(maskPlanes)) 

2220 readNoise = afwMath.makeStatistics(overscanImage, afwMath.STDEVCLIP, stats).getValue() 

2221 self.log.info("Calculated empirical read noise for amp %s: %f.", 

2222 amp.getName(), readNoise) 

2223 elif self.config.usePtcReadNoise: 

2224 if ptcDataset is None: 

2225 raise RuntimeError("No ptcDataset provided to use PTC readnoise.") 

2226 else: 

2227 readNoise = ptcDataset.noise[amp.getName()] 

2228 self.log.info("Using read noise from Photon Transfer Curve.") 

2229 else: 

2230 readNoise = amp.getReadNoise() 

2231 

2232 isrFunctions.updateVariance( 

2233 maskedImage=ampExposure.getMaskedImage(), 

2234 gain=gain, 

2235 readNoise=readNoise, 

2236 ) 

2237 

2238 def maskNegativeVariance(self, exposure): 

2239 """Identify and mask pixels with negative variance values. 

2240 

2241 Parameters 

2242 ---------- 

2243 exposure : `lsst.afw.image.Exposure` 

2244 Exposure to process. 

2245 

2246 See Also 

2247 -------- 

2248 lsst.ip.isr.isrFunctions.updateVariance 

2249 """ 

2250 maskPlane = exposure.getMask().getPlaneBitMask(self.config.negativeVarianceMaskName) 

2251 bad = numpy.where(exposure.getVariance().getArray() <= 0.0) 

2252 exposure.mask.array[bad] |= maskPlane 

2253 

2254 def darkCorrection(self, exposure, darkExposure, invert=False): 

2255 """Apply dark correction in place. 

2256 

2257 Parameters 

2258 ---------- 

2259 exposure : `lsst.afw.image.Exposure` 

2260 Exposure to process. 

2261 darkExposure : `lsst.afw.image.Exposure` 

2262 Dark exposure of the same size as ``exposure``. 

2263 invert : `Bool`, optional 

2264 If True, re-add the dark to an already corrected image. 

2265 

2266 Raises 

2267 ------ 

2268 RuntimeError 

2269 Raised if either ``exposure`` or ``darkExposure`` do not 

2270 have their dark time defined. 

2271 

2272 See Also 

2273 -------- 

2274 lsst.ip.isr.isrFunctions.darkCorrection 

2275 """ 

2276 expScale = exposure.getInfo().getVisitInfo().getDarkTime() 

2277 if math.isnan(expScale): 

2278 raise RuntimeError("Exposure darktime is NAN.") 

2279 if darkExposure.getInfo().getVisitInfo() is not None \ 

2280 and not math.isnan(darkExposure.getInfo().getVisitInfo().getDarkTime()): 

2281 darkScale = darkExposure.getInfo().getVisitInfo().getDarkTime() 

2282 else: 

2283 # DM-17444: darkExposure.getInfo.getVisitInfo() is None 

2284 # so getDarkTime() does not exist. 

2285 self.log.warning("darkExposure.getInfo().getVisitInfo() does not exist. Using darkScale = 1.0.") 

2286 darkScale = 1.0 

2287 

2288 isrFunctions.darkCorrection( 

2289 maskedImage=exposure.getMaskedImage(), 

2290 darkMaskedImage=darkExposure.getMaskedImage(), 

2291 expScale=expScale, 

2292 darkScale=darkScale, 

2293 invert=invert, 

2294 trimToFit=self.config.doTrimToMatchCalib 

2295 ) 

2296 

2297 def doLinearize(self, detector): 

2298 """Check if linearization is needed for the detector cameraGeom. 

2299 

2300 Checks config.doLinearize and the linearity type of the first 

2301 amplifier. 

2302 

2303 Parameters 

2304 ---------- 

2305 detector : `lsst.afw.cameraGeom.Detector` 

2306 Detector to get linearity type from. 

2307 

2308 Returns 

2309 ------- 

2310 doLinearize : `Bool` 

2311 If True, linearization should be performed. 

2312 """ 

2313 return self.config.doLinearize and \ 

2314 detector.getAmplifiers()[0].getLinearityType() != NullLinearityType 

2315 

2316 def flatCorrection(self, exposure, flatExposure, invert=False): 

2317 """Apply flat correction in place. 

2318 

2319 Parameters 

2320 ---------- 

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

2322 Exposure to process. 

2323 flatExposure : `lsst.afw.image.Exposure` 

2324 Flat exposure of the same size as ``exposure``. 

2325 invert : `Bool`, optional 

2326 If True, unflatten an already flattened image. 

2327 

2328 See Also 

2329 -------- 

2330 lsst.ip.isr.isrFunctions.flatCorrection 

2331 """ 

2332 isrFunctions.flatCorrection( 

2333 maskedImage=exposure.getMaskedImage(), 

2334 flatMaskedImage=flatExposure.getMaskedImage(), 

2335 scalingType=self.config.flatScalingType, 

2336 userScale=self.config.flatUserScale, 

2337 invert=invert, 

2338 trimToFit=self.config.doTrimToMatchCalib 

2339 ) 

2340 

2341 def saturationDetection(self, exposure, amp): 

2342 """Detect and mask saturated pixels in config.saturatedMaskName. 

2343 

2344 Parameters 

2345 ---------- 

2346 exposure : `lsst.afw.image.Exposure` 

2347 Exposure to process. Only the amplifier DataSec is processed. 

2348 amp : `lsst.afw.table.AmpInfoCatalog` 

2349 Amplifier detector data. 

2350 

2351 See Also 

2352 -------- 

2353 lsst.ip.isr.isrFunctions.makeThresholdMask 

2354 """ 

2355 if not math.isnan(amp.getSaturation()): 

2356 maskedImage = exposure.getMaskedImage() 

2357 dataView = maskedImage.Factory(maskedImage, amp.getRawBBox()) 

2358 isrFunctions.makeThresholdMask( 

2359 maskedImage=dataView, 

2360 threshold=amp.getSaturation(), 

2361 growFootprints=0, 

2362 maskName=self.config.saturatedMaskName, 

2363 ) 

2364 

2365 def saturationInterpolation(self, exposure): 

2366 """Interpolate over saturated pixels, in place. 

2367 

2368 This method should be called after `saturationDetection`, to 

2369 ensure that the saturated pixels have been identified in the 

2370 SAT mask. It should also be called after `assembleCcd`, since 

2371 saturated regions may cross amplifier boundaries. 

2372 

2373 Parameters 

2374 ---------- 

2375 exposure : `lsst.afw.image.Exposure` 

2376 Exposure to process. 

2377 

2378 See Also 

2379 -------- 

2380 lsst.ip.isr.isrTask.saturationDetection 

2381 lsst.ip.isr.isrFunctions.interpolateFromMask 

2382 """ 

2383 isrFunctions.interpolateFromMask( 

2384 maskedImage=exposure.getMaskedImage(), 

2385 fwhm=self.config.fwhm, 

2386 growSaturatedFootprints=self.config.growSaturationFootprintSize, 

2387 maskNameList=list(self.config.saturatedMaskName), 

2388 ) 

2389 

2390 def suspectDetection(self, exposure, amp): 

2391 """Detect and mask suspect pixels in config.suspectMaskName. 

2392 

2393 Parameters 

2394 ---------- 

2395 exposure : `lsst.afw.image.Exposure` 

2396 Exposure to process. Only the amplifier DataSec is processed. 

2397 amp : `lsst.afw.table.AmpInfoCatalog` 

2398 Amplifier detector data. 

2399 

2400 See Also 

2401 -------- 

2402 lsst.ip.isr.isrFunctions.makeThresholdMask 

2403 

2404 Notes 

2405 ----- 

2406 Suspect pixels are pixels whose value is greater than 

2407 amp.getSuspectLevel(). This is intended to indicate pixels that may be 

2408 affected by unknown systematics; for example if non-linearity 

2409 corrections above a certain level are unstable then that would be a 

2410 useful value for suspectLevel. A value of `nan` indicates that no such 

2411 level exists and no pixels are to be masked as suspicious. 

2412 """ 

2413 suspectLevel = amp.getSuspectLevel() 

2414 if math.isnan(suspectLevel): 

2415 return 

2416 

2417 maskedImage = exposure.getMaskedImage() 

2418 dataView = maskedImage.Factory(maskedImage, amp.getRawBBox()) 

2419 isrFunctions.makeThresholdMask( 

2420 maskedImage=dataView, 

2421 threshold=suspectLevel, 

2422 growFootprints=0, 

2423 maskName=self.config.suspectMaskName, 

2424 ) 

2425 

2426 def maskDefect(self, exposure, defectBaseList): 

2427 """Mask defects using mask plane "BAD", in place. 

2428 

2429 Parameters 

2430 ---------- 

2431 exposure : `lsst.afw.image.Exposure` 

2432 Exposure to process. 

2433 defectBaseList : `lsst.ip.isr.Defects` or `list` of 

2434 `lsst.afw.image.DefectBase`. 

2435 List of defects to mask. 

2436 

2437 Notes 

2438 ----- 

2439 Call this after CCD assembly, since defects may cross amplifier 

2440 boundaries. 

2441 """ 

2442 maskedImage = exposure.getMaskedImage() 

2443 if not isinstance(defectBaseList, Defects): 

2444 # Promotes DefectBase to Defect 

2445 defectList = Defects(defectBaseList) 

2446 else: 

2447 defectList = defectBaseList 

2448 defectList.maskPixels(maskedImage, maskName="BAD") 

2449 

2450 def maskEdges(self, exposure, numEdgePixels=0, maskPlane="SUSPECT", level='DETECTOR'): 

2451 """Mask edge pixels with applicable mask plane. 

2452 

2453 Parameters 

2454 ---------- 

2455 exposure : `lsst.afw.image.Exposure` 

2456 Exposure to process. 

2457 numEdgePixels : `int`, optional 

2458 Number of edge pixels to mask. 

2459 maskPlane : `str`, optional 

2460 Mask plane name to use. 

2461 level : `str`, optional 

2462 Level at which to mask edges. 

2463 """ 

2464 maskedImage = exposure.getMaskedImage() 

2465 maskBitMask = maskedImage.getMask().getPlaneBitMask(maskPlane) 

2466 

2467 if numEdgePixels > 0: 

2468 if level == 'DETECTOR': 

2469 boxes = [maskedImage.getBBox()] 

2470 elif level == 'AMP': 

2471 boxes = [amp.getBBox() for amp in exposure.getDetector()] 

2472 

2473 for box in boxes: 

2474 # This makes a bbox numEdgeSuspect pixels smaller than the 

2475 # image on each side 

2476 subImage = maskedImage[box] 

2477 box.grow(-numEdgePixels) 

2478 # Mask pixels outside box 

2479 SourceDetectionTask.setEdgeBits( 

2480 subImage, 

2481 box, 

2482 maskBitMask) 

2483 

2484 def maskAndInterpolateDefects(self, exposure, defectBaseList): 

2485 """Mask and interpolate defects using mask plane "BAD", in place. 

2486 

2487 Parameters 

2488 ---------- 

2489 exposure : `lsst.afw.image.Exposure` 

2490 Exposure to process. 

2491 defectBaseList : `lsst.ip.isr.Defects` or `list` of 

2492 `lsst.afw.image.DefectBase`. 

2493 List of defects to mask and interpolate. 

2494 

2495 See Also 

2496 -------- 

2497 lsst.ip.isr.isrTask.maskDefect 

2498 """ 

2499 self.maskDefect(exposure, defectBaseList) 

2500 self.maskEdges(exposure, numEdgePixels=self.config.numEdgeSuspect, 

2501 maskPlane="SUSPECT", level=self.config.edgeMaskLevel) 

2502 isrFunctions.interpolateFromMask( 

2503 maskedImage=exposure.getMaskedImage(), 

2504 fwhm=self.config.fwhm, 

2505 growSaturatedFootprints=0, 

2506 maskNameList=["BAD"], 

2507 ) 

2508 

2509 def maskNan(self, exposure): 

2510 """Mask NaNs using mask plane "UNMASKEDNAN", in place. 

2511 

2512 Parameters 

2513 ---------- 

2514 exposure : `lsst.afw.image.Exposure` 

2515 Exposure to process. 

2516 

2517 Notes 

2518 ----- 

2519 We mask over all non-finite values (NaN, inf), including those 

2520 that are masked with other bits (because those may or may not be 

2521 interpolated over later, and we want to remove all NaN/infs). 

2522 Despite this behaviour, the "UNMASKEDNAN" mask plane is used to 

2523 preserve the historical name. 

2524 """ 

2525 maskedImage = exposure.getMaskedImage() 

2526 

2527 # Find and mask NaNs 

2528 maskedImage.getMask().addMaskPlane("UNMASKEDNAN") 

2529 maskVal = maskedImage.getMask().getPlaneBitMask("UNMASKEDNAN") 

2530 numNans = maskNans(maskedImage, maskVal) 

2531 self.metadata["NUMNANS"] = numNans 

2532 if numNans > 0: 

2533 self.log.warning("There were %d unmasked NaNs.", numNans) 

2534 

2535 def maskAndInterpolateNan(self, exposure): 

2536 """"Mask and interpolate NaN/infs using mask plane "UNMASKEDNAN", 

2537 in place. 

2538 

2539 Parameters 

2540 ---------- 

2541 exposure : `lsst.afw.image.Exposure` 

2542 Exposure to process. 

2543 

2544 See Also 

2545 -------- 

2546 lsst.ip.isr.isrTask.maskNan 

2547 """ 

2548 self.maskNan(exposure) 

2549 isrFunctions.interpolateFromMask( 

2550 maskedImage=exposure.getMaskedImage(), 

2551 fwhm=self.config.fwhm, 

2552 growSaturatedFootprints=0, 

2553 maskNameList=["UNMASKEDNAN"], 

2554 ) 

2555 

2556 def measureBackground(self, exposure, IsrQaConfig=None): 

2557 """Measure the image background in subgrids, for quality control. 

2558 

2559 Parameters 

2560 ---------- 

2561 exposure : `lsst.afw.image.Exposure` 

2562 Exposure to process. 

2563 IsrQaConfig : `lsst.ip.isr.isrQa.IsrQaConfig` 

2564 Configuration object containing parameters on which background 

2565 statistics and subgrids to use. 

2566 """ 

2567 if IsrQaConfig is not None: 

2568 statsControl = afwMath.StatisticsControl(IsrQaConfig.flatness.clipSigma, 

2569 IsrQaConfig.flatness.nIter) 

2570 maskVal = exposure.getMaskedImage().getMask().getPlaneBitMask(["BAD", "SAT", "DETECTED"]) 

2571 statsControl.setAndMask(maskVal) 

2572 maskedImage = exposure.getMaskedImage() 

2573 stats = afwMath.makeStatistics(maskedImage, afwMath.MEDIAN | afwMath.STDEVCLIP, statsControl) 

2574 skyLevel = stats.getValue(afwMath.MEDIAN) 

2575 skySigma = stats.getValue(afwMath.STDEVCLIP) 

2576 self.log.info("Flattened sky level: %f +/- %f.", skyLevel, skySigma) 

2577 metadata = exposure.getMetadata() 

2578 metadata["SKYLEVEL"] = skyLevel 

2579 metadata["SKYSIGMA"] = skySigma 

2580 

2581 # calcluating flatlevel over the subgrids 

2582 stat = afwMath.MEANCLIP if IsrQaConfig.flatness.doClip else afwMath.MEAN 

2583 meshXHalf = int(IsrQaConfig.flatness.meshX/2.) 

2584 meshYHalf = int(IsrQaConfig.flatness.meshY/2.) 

2585 nX = int((exposure.getWidth() + meshXHalf) / IsrQaConfig.flatness.meshX) 

2586 nY = int((exposure.getHeight() + meshYHalf) / IsrQaConfig.flatness.meshY) 

2587 skyLevels = numpy.zeros((nX, nY)) 

2588 

2589 for j in range(nY): 

2590 yc = meshYHalf + j * IsrQaConfig.flatness.meshY 

2591 for i in range(nX): 

2592 xc = meshXHalf + i * IsrQaConfig.flatness.meshX 

2593 

2594 xLLC = xc - meshXHalf 

2595 yLLC = yc - meshYHalf 

2596 xURC = xc + meshXHalf - 1 

2597 yURC = yc + meshYHalf - 1 

2598 

2599 bbox = lsst.geom.Box2I(lsst.geom.Point2I(xLLC, yLLC), lsst.geom.Point2I(xURC, yURC)) 

2600 miMesh = maskedImage.Factory(exposure.getMaskedImage(), bbox, afwImage.LOCAL) 

2601 

2602 skyLevels[i, j] = afwMath.makeStatistics(miMesh, stat, statsControl).getValue() 

2603 

2604 good = numpy.where(numpy.isfinite(skyLevels)) 

2605 skyMedian = numpy.median(skyLevels[good]) 

2606 flatness = (skyLevels[good] - skyMedian) / skyMedian 

2607 flatness_rms = numpy.std(flatness) 

2608 flatness_pp = flatness.max() - flatness.min() if len(flatness) > 0 else numpy.nan 

2609 

2610 self.log.info("Measuring sky levels in %dx%d grids: %f.", nX, nY, skyMedian) 

2611 self.log.info("Sky flatness in %dx%d grids - pp: %f rms: %f.", 

2612 nX, nY, flatness_pp, flatness_rms) 

2613 

2614 metadata["FLATNESS_PP"] = float(flatness_pp) 

2615 metadata["FLATNESS_RMS"] = float(flatness_rms) 

2616 metadata["FLATNESS_NGRIDS"] = '%dx%d' % (nX, nY) 

2617 metadata["FLATNESS_MESHX"] = IsrQaConfig.flatness.meshX 

2618 metadata["FLATNESS_MESHY"] = IsrQaConfig.flatness.meshY 

2619 

2620 def roughZeroPoint(self, exposure): 

2621 """Set an approximate magnitude zero point for the exposure. 

2622 

2623 Parameters 

2624 ---------- 

2625 exposure : `lsst.afw.image.Exposure` 

2626 Exposure to process. 

2627 """ 

2628 filterLabel = exposure.getFilterLabel() 

2629 physicalFilter = isrFunctions.getPhysicalFilter(filterLabel, self.log) 

2630 

2631 if physicalFilter in self.config.fluxMag0T1: 

2632 fluxMag0 = self.config.fluxMag0T1[physicalFilter] 

2633 else: 

2634 self.log.warning("No rough magnitude zero point defined for filter %s.", physicalFilter) 

2635 fluxMag0 = self.config.defaultFluxMag0T1 

2636 

2637 expTime = exposure.getInfo().getVisitInfo().getExposureTime() 

2638 if not expTime > 0: # handle NaN as well as <= 0 

2639 self.log.warning("Non-positive exposure time; skipping rough zero point.") 

2640 return 

2641 

2642 self.log.info("Setting rough magnitude zero point for filter %s: %f", 

2643 physicalFilter, 2.5*math.log10(fluxMag0*expTime)) 

2644 exposure.setPhotoCalib(afwImage.makePhotoCalibFromCalibZeroPoint(fluxMag0*expTime, 0.0)) 

2645 

2646 def setValidPolygonIntersect(self, ccdExposure, fpPolygon): 

2647 """Set valid polygon as the intersection of fpPolygon and chip corners. 

2648 

2649 Parameters 

2650 ---------- 

2651 ccdExposure : `lsst.afw.image.Exposure` 

2652 Exposure to process. 

2653 fpPolygon : `lsst.afw.geom.Polygon` 

2654 Polygon in focal plane coordinates. 

2655 """ 

2656 # Get ccd corners in focal plane coordinates 

2657 ccd = ccdExposure.getDetector() 

2658 fpCorners = ccd.getCorners(FOCAL_PLANE) 

2659 ccdPolygon = Polygon(fpCorners) 

2660 

2661 # Get intersection of ccd corners with fpPolygon 

2662 intersect = ccdPolygon.intersectionSingle(fpPolygon) 

2663 

2664 # Transform back to pixel positions and build new polygon 

2665 ccdPoints = ccd.transform(intersect, FOCAL_PLANE, PIXELS) 

2666 validPolygon = Polygon(ccdPoints) 

2667 ccdExposure.getInfo().setValidPolygon(validPolygon) 

2668 

2669 @contextmanager 

2670 def flatContext(self, exp, flat, dark=None): 

2671 """Context manager that applies and removes flats and darks, 

2672 if the task is configured to apply them. 

2673 

2674 Parameters 

2675 ---------- 

2676 exp : `lsst.afw.image.Exposure` 

2677 Exposure to process. 

2678 flat : `lsst.afw.image.Exposure` 

2679 Flat exposure the same size as ``exp``. 

2680 dark : `lsst.afw.image.Exposure`, optional 

2681 Dark exposure the same size as ``exp``. 

2682 

2683 Yields 

2684 ------ 

2685 exp : `lsst.afw.image.Exposure` 

2686 The flat and dark corrected exposure. 

2687 """ 

2688 if self.config.doDark and dark is not None: 

2689 self.darkCorrection(exp, dark) 

2690 if self.config.doFlat: 

2691 self.flatCorrection(exp, flat) 

2692 try: 

2693 yield exp 

2694 finally: 

2695 if self.config.doFlat: 

2696 self.flatCorrection(exp, flat, invert=True) 

2697 if self.config.doDark and dark is not None: 

2698 self.darkCorrection(exp, dark, invert=True) 

2699 

2700 def debugView(self, exposure, stepname): 

2701 """Utility function to examine ISR exposure at different stages. 

2702 

2703 Parameters 

2704 ---------- 

2705 exposure : `lsst.afw.image.Exposure` 

2706 Exposure to view. 

2707 stepname : `str` 

2708 State of processing to view. 

2709 """ 

2710 frame = getDebugFrame(self._display, stepname) 

2711 if frame: 

2712 display = getDisplay(frame) 

2713 display.scale('asinh', 'zscale') 

2714 display.mtv(exposure) 

2715 prompt = "Press Enter to continue [c]... " 

2716 while True: 

2717 ans = input(prompt).lower() 

2718 if ans in ("", "c",): 

2719 break 

2720 

2721 

2722class FakeAmp(object): 

2723 """A Detector-like object that supports returning gain and saturation level 

2724 

2725 This is used when the input exposure does not have a detector. 

2726 

2727 Parameters 

2728 ---------- 

2729 exposure : `lsst.afw.image.Exposure` 

2730 Exposure to generate a fake amplifier for. 

2731 config : `lsst.ip.isr.isrTaskConfig` 

2732 Configuration to apply to the fake amplifier. 

2733 """ 

2734 

2735 def __init__(self, exposure, config): 

2736 self._bbox = exposure.getBBox(afwImage.LOCAL) 

2737 self._RawHorizontalOverscanBBox = lsst.geom.Box2I() 

2738 self._gain = config.gain 

2739 self._readNoise = config.readNoise 

2740 self._saturation = config.saturation 

2741 

2742 def getBBox(self): 

2743 return self._bbox 

2744 

2745 def getRawBBox(self): 

2746 return self._bbox 

2747 

2748 def getRawHorizontalOverscanBBox(self): 

2749 return self._RawHorizontalOverscanBBox 

2750 

2751 def getGain(self): 

2752 return self._gain 

2753 

2754 def getReadNoise(self): 

2755 return self._readNoise 

2756 

2757 def getSaturation(self): 

2758 return self._saturation 

2759 

2760 def getSuspectLevel(self): 

2761 return float("NaN") 

2762 

2763 

2764class RunIsrConfig(pexConfig.Config): 

2765 isr = pexConfig.ConfigurableField(target=IsrTask, doc="Instrument signature removal") 

2766 

2767 

2768class RunIsrTask(pipeBase.CmdLineTask): 

2769 """Task to wrap the default IsrTask to allow it to be retargeted. 

2770 

2771 The standard IsrTask can be called directly from a command line 

2772 program, but doing so removes the ability of the task to be 

2773 retargeted. As most cameras override some set of the IsrTask 

2774 methods, this would remove those data-specific methods in the 

2775 output post-ISR images. This wrapping class fixes the issue, 

2776 allowing identical post-ISR images to be generated by both the 

2777 processCcd and isrTask code. 

2778 """ 

2779 ConfigClass = RunIsrConfig 

2780 _DefaultName = "runIsr" 

2781 

2782 def __init__(self, *args, **kwargs): 

2783 super().__init__(*args, **kwargs) 

2784 self.makeSubtask("isr") 

2785 

2786 def runDataRef(self, dataRef): 

2787 """ 

2788 Parameters 

2789 ---------- 

2790 dataRef : `lsst.daf.persistence.ButlerDataRef` 

2791 data reference of the detector data to be processed 

2792 

2793 Returns 

2794 ------- 

2795 result : `pipeBase.Struct` 

2796 Result struct with component: 

2797 

2798 - exposure : `lsst.afw.image.Exposure` 

2799 Post-ISR processed exposure. 

2800 """ 

2801 return self.isr.runDataRef(dataRef)