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1# This file is part of ip_isr. 

2# 

3# Developed for the LSST Data Management System. 

4# This product includes software developed by the LSST Project 

5# (https://www.lsst.org). 

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

7# for details of code ownership. 

8# 

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

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

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

12# (at your option) any later version. 

13# 

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

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

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

17# GNU General Public License for more details. 

18# 

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

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

21 

22import math 

23import numpy 

24 

25import lsst.geom 

26import lsst.afw.image as afwImage 

27import lsst.afw.math as afwMath 

28import lsst.pex.config as pexConfig 

29import lsst.pipe.base as pipeBase 

30import lsst.pipe.base.connectionTypes as cT 

31 

32from contextlib import contextmanager 

33from lsstDebug import getDebugFrame 

34 

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

36 ReadoutCorner) 

37from lsst.afw.display import getDisplay 

38from lsst.afw.geom import Polygon 

39from lsst.daf.persistence import ButlerDataRef 

40from lsst.daf.persistence.butler import NoResults 

41from lsst.meas.algorithms.detection import SourceDetectionTask 

42 

43from . import isrFunctions 

44from . import isrQa 

45from . import linearize 

46from .defects import Defects 

47 

48from .assembleCcdTask import AssembleCcdTask 

49from .crosstalk import CrosstalkTask, CrosstalkCalib 

50from .fringe import FringeTask 

51from .isr import maskNans 

52from .masking import MaskingTask 

53from .overscan import OverscanCorrectionTask 

54from .straylight import StrayLightTask 

55from .vignette import VignetteTask 

56from lsst.daf.butler import DimensionGraph 

57 

58 

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

60 

61 

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

63 """Lookup function to identify crosstalkSource entries. 

64 

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

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

67 populated. 

68 

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

70 generation issue. 

71 

72 Parameters 

73 ---------- 

74 datasetType : `str` 

75 Dataset to lookup. 

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

77 Butler registry to query. 

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

79 Data id to transform to identify crosstalkSources. The 

80 ``detector`` entry will be stripped. 

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

82 Collections to search through. 

83 

84 Returns 

85 ------- 

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

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

88 crosstalkSources. 

89 """ 

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

91 results = list(registry.queryDatasets(datasetType, 

92 collections=collections, 

93 dataId=newDataId, 

94 findFirst=True, 

95 ).expanded()) 

96 return results 

97 

98 

99class IsrTaskConnections(pipeBase.PipelineTaskConnections, 

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

101 defaultTemplates={}): 

102 ccdExposure = cT.Input( 

103 name="raw", 

104 doc="Input exposure to process.", 

105 storageClass="Exposure", 

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

107 ) 

108 camera = cT.PrerequisiteInput( 

109 name="camera", 

110 storageClass="Camera", 

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

112 dimensions=["instrument"], 

113 isCalibration=True, 

114 ) 

115 

116 crosstalk = cT.PrerequisiteInput( 

117 name="crosstalk", 

118 doc="Input crosstalk object", 

119 storageClass="CrosstalkCalib", 

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

121 isCalibration=True, 

122 ) 

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

124 # possible crosstalk sources. 

125 crosstalkSources = cT.PrerequisiteInput( 

126 name="isrOverscanCorrected", 

127 doc="Overscan corrected input images.", 

128 storageClass="Exposure", 

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

130 deferLoad=True, 

131 multiple=True, 

132 lookupFunction=crosstalkSourceLookup, 

133 ) 

134 bias = cT.PrerequisiteInput( 

135 name="bias", 

136 doc="Input bias calibration.", 

137 storageClass="ExposureF", 

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

139 isCalibration=True, 

140 ) 

141 dark = cT.PrerequisiteInput( 

142 name='dark', 

143 doc="Input dark calibration.", 

144 storageClass="ExposureF", 

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

146 isCalibration=True, 

147 ) 

148 flat = cT.PrerequisiteInput( 

149 name="flat", 

150 doc="Input flat calibration.", 

151 storageClass="ExposureF", 

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

153 isCalibration=True, 

154 ) 

155 fringes = cT.PrerequisiteInput( 

156 name="fringe", 

157 doc="Input fringe calibration.", 

158 storageClass="ExposureF", 

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

160 isCalibration=True, 

161 ) 

162 strayLightData = cT.PrerequisiteInput( 

163 name='yBackground', 

164 doc="Input stray light calibration.", 

165 storageClass="StrayLightData", 

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

167 isCalibration=True, 

168 ) 

169 bfKernel = cT.PrerequisiteInput( 

170 name='bfKernel', 

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

172 storageClass="NumpyArray", 

173 dimensions=["instrument"], 

174 isCalibration=True, 

175 ) 

176 newBFKernel = cT.PrerequisiteInput( 

177 name='brighterFatterKernel', 

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

179 storageClass="BrighterFatterKernel", 

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

181 isCalibration=True, 

182 ) 

183 defects = cT.PrerequisiteInput( 

184 name='defects', 

185 doc="Input defect tables.", 

186 storageClass="Defects", 

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

188 isCalibration=True, 

189 ) 

190 linearizer = cT.PrerequisiteInput( 

191 name='linearizer', 

192 storageClass="Linearizer", 

193 doc="Linearity correction calibration.", 

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

195 isCalibration=True, 

196 ) 

197 opticsTransmission = cT.PrerequisiteInput( 

198 name="transmission_optics", 

199 storageClass="TransmissionCurve", 

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

201 dimensions=["instrument"], 

202 isCalibration=True, 

203 ) 

204 filterTransmission = cT.PrerequisiteInput( 

205 name="transmission_filter", 

206 storageClass="TransmissionCurve", 

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

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

209 isCalibration=True, 

210 ) 

211 sensorTransmission = cT.PrerequisiteInput( 

212 name="transmission_sensor", 

213 storageClass="TransmissionCurve", 

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

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

216 isCalibration=True, 

217 ) 

218 atmosphereTransmission = cT.PrerequisiteInput( 

219 name="transmission_atmosphere", 

220 storageClass="TransmissionCurve", 

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

222 dimensions=["instrument"], 

223 isCalibration=True, 

224 ) 

225 illumMaskedImage = cT.PrerequisiteInput( 

226 name="illum", 

227 doc="Input illumination correction.", 

228 storageClass="MaskedImageF", 

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

230 isCalibration=True, 

231 ) 

232 

233 outputExposure = cT.Output( 

234 name='postISRCCD', 

235 doc="Output ISR processed exposure.", 

236 storageClass="Exposure", 

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

238 ) 

239 preInterpExposure = cT.Output( 

240 name='preInterpISRCCD', 

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

242 storageClass="ExposureF", 

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

244 ) 

245 outputOssThumbnail = cT.Output( 

246 name="OssThumb", 

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

248 storageClass="Thumbnail", 

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

250 ) 

251 outputFlattenedThumbnail = cT.Output( 

252 name="FlattenedThumb", 

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

254 storageClass="Thumbnail", 

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

256 ) 

257 

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

259 super().__init__(config=config) 

260 

261 if config.doBias is not True: 

262 self.prerequisiteInputs.discard("bias") 

263 if config.doLinearize is not True: 

264 self.prerequisiteInputs.discard("linearizer") 

265 if config.doCrosstalk is not True: 

266 self.inputs.discard("crosstalkSources") 

267 self.prerequisiteInputs.discard("crosstalk") 

268 if config.doBrighterFatter is not True: 

269 self.prerequisiteInputs.discard("bfKernel") 

270 self.prerequisiteInputs.discard("newBFKernel") 

271 if config.doDefect is not True: 

272 self.prerequisiteInputs.discard("defects") 

273 if config.doDark is not True: 

274 self.prerequisiteInputs.discard("dark") 

275 if config.doFlat is not True: 

276 self.prerequisiteInputs.discard("flat") 

277 if config.doAttachTransmissionCurve is not True: 

278 self.prerequisiteInputs.discard("opticsTransmission") 

279 self.prerequisiteInputs.discard("filterTransmission") 

280 self.prerequisiteInputs.discard("sensorTransmission") 

281 self.prerequisiteInputs.discard("atmosphereTransmission") 

282 if config.doUseOpticsTransmission is not True: 

283 self.prerequisiteInputs.discard("opticsTransmission") 

284 if config.doUseFilterTransmission is not True: 

285 self.prerequisiteInputs.discard("filterTransmission") 

286 if config.doUseSensorTransmission is not True: 

287 self.prerequisiteInputs.discard("sensorTransmission") 

288 if config.doUseAtmosphereTransmission is not True: 

289 self.prerequisiteInputs.discard("atmosphereTransmission") 

290 if config.doIlluminationCorrection is not True: 

291 self.prerequisiteInputs.discard("illumMaskedImage") 

292 

293 if config.doWrite is not True: 

294 self.outputs.discard("outputExposure") 

295 self.outputs.discard("preInterpExposure") 

296 self.outputs.discard("outputFlattenedThumbnail") 

297 self.outputs.discard("outputOssThumbnail") 

298 if config.doSaveInterpPixels is not True: 

299 self.outputs.discard("preInterpExposure") 

300 if config.qa.doThumbnailOss is not True: 

301 self.outputs.discard("outputOssThumbnail") 

302 if config.qa.doThumbnailFlattened is not True: 

303 self.outputs.discard("outputFlattenedThumbnail") 

304 

305 

306class IsrTaskConfig(pipeBase.PipelineTaskConfig, 

307 pipelineConnections=IsrTaskConnections): 

308 """Configuration parameters for IsrTask. 

309 

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

311 """ 

312 datasetType = pexConfig.Field( 

313 dtype=str, 

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

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

316 default="raw", 

317 ) 

318 

319 fallbackFilterName = pexConfig.Field( 

320 dtype=str, 

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

322 optional=True 

323 ) 

324 useFallbackDate = pexConfig.Field( 

325 dtype=bool, 

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

327 default=False, 

328 ) 

329 expectWcs = pexConfig.Field( 

330 dtype=bool, 

331 default=True, 

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

333 ) 

334 fwhm = pexConfig.Field( 

335 dtype=float, 

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

337 default=1.0, 

338 ) 

339 qa = pexConfig.ConfigField( 

340 dtype=isrQa.IsrQaConfig, 

341 doc="QA related configuration options.", 

342 ) 

343 

344 # Image conversion configuration 

345 doConvertIntToFloat = pexConfig.Field( 

346 dtype=bool, 

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

348 default=True, 

349 ) 

350 

351 # Saturated pixel handling. 

352 doSaturation = pexConfig.Field( 

353 dtype=bool, 

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

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

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

357 default=True, 

358 ) 

359 saturatedMaskName = pexConfig.Field( 

360 dtype=str, 

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

362 default="SAT", 

363 ) 

364 saturation = pexConfig.Field( 

365 dtype=float, 

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

367 default=float("NaN"), 

368 ) 

369 growSaturationFootprintSize = pexConfig.Field( 

370 dtype=int, 

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

372 default=1, 

373 ) 

374 

375 # Suspect pixel handling. 

376 doSuspect = pexConfig.Field( 

377 dtype=bool, 

378 doc="Mask suspect pixels?", 

379 default=False, 

380 ) 

381 suspectMaskName = pexConfig.Field( 

382 dtype=str, 

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

384 default="SUSPECT", 

385 ) 

386 numEdgeSuspect = pexConfig.Field( 

387 dtype=int, 

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

389 default=0, 

390 ) 

391 edgeMaskLevel = pexConfig.ChoiceField( 

392 dtype=str, 

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

394 default="DETECTOR", 

395 allowed={ 

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

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

398 }, 

399 ) 

400 

401 # Initial masking options. 

402 doSetBadRegions = pexConfig.Field( 

403 dtype=bool, 

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

405 default=True, 

406 ) 

407 badStatistic = pexConfig.ChoiceField( 

408 dtype=str, 

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

410 default='MEANCLIP', 

411 allowed={ 

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

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

414 }, 

415 ) 

416 

417 # Overscan subtraction configuration. 

418 doOverscan = pexConfig.Field( 

419 dtype=bool, 

420 doc="Do overscan subtraction?", 

421 default=True, 

422 ) 

423 overscan = pexConfig.ConfigurableField( 

424 target=OverscanCorrectionTask, 

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

426 ) 

427 

428 overscanFitType = pexConfig.ChoiceField( 

429 dtype=str, 

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

431 default='MEDIAN', 

432 allowed={ 

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

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

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

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

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

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

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

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

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

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

443 }, 

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

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

446 ) 

447 overscanOrder = pexConfig.Field( 

448 dtype=int, 

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

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

451 default=1, 

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

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

454 ) 

455 overscanNumSigmaClip = pexConfig.Field( 

456 dtype=float, 

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

458 default=3.0, 

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

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

461 ) 

462 overscanIsInt = pexConfig.Field( 

463 dtype=bool, 

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

465 " and overscan.FitType=MEDIAN_PER_ROW.", 

466 default=True, 

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

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

469 ) 

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

471 overscanNumLeadingColumnsToSkip = pexConfig.Field( 

472 dtype=int, 

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

474 default=0, 

475 ) 

476 overscanNumTrailingColumnsToSkip = pexConfig.Field( 

477 dtype=int, 

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

479 default=0, 

480 ) 

481 overscanMaxDev = pexConfig.Field( 481 ↛ exitline 481 didn't jump to the function exit

482 dtype=float, 

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

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

485 ) 

486 overscanBiasJump = pexConfig.Field( 

487 dtype=bool, 

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

489 default=False, 

490 ) 

491 overscanBiasJumpKeyword = pexConfig.Field( 

492 dtype=str, 

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

494 default="NO_SUCH_KEY", 

495 ) 

496 overscanBiasJumpDevices = pexConfig.ListField( 

497 dtype=str, 

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

499 default=(), 

500 ) 

501 overscanBiasJumpLocation = pexConfig.Field( 

502 dtype=int, 

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

504 default=0, 

505 ) 

506 

507 # Amplifier to CCD assembly configuration 

508 doAssembleCcd = pexConfig.Field( 

509 dtype=bool, 

510 default=True, 

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

512 ) 

513 assembleCcd = pexConfig.ConfigurableField( 

514 target=AssembleCcdTask, 

515 doc="CCD assembly task", 

516 ) 

517 

518 # General calibration configuration. 

519 doAssembleIsrExposures = pexConfig.Field( 

520 dtype=bool, 

521 default=False, 

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

523 ) 

524 doTrimToMatchCalib = pexConfig.Field( 

525 dtype=bool, 

526 default=False, 

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

528 ) 

529 

530 # Bias subtraction. 

531 doBias = pexConfig.Field( 

532 dtype=bool, 

533 doc="Apply bias frame correction?", 

534 default=True, 

535 ) 

536 biasDataProductName = pexConfig.Field( 

537 dtype=str, 

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

539 default="bias", 

540 ) 

541 doBiasBeforeOverscan = pexConfig.Field( 

542 dtype=bool, 

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

544 default=False 

545 ) 

546 

547 # Variance construction 

548 doVariance = pexConfig.Field( 

549 dtype=bool, 

550 doc="Calculate variance?", 

551 default=True 

552 ) 

553 gain = pexConfig.Field( 

554 dtype=float, 

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

556 default=float("NaN"), 

557 ) 

558 readNoise = pexConfig.Field( 

559 dtype=float, 

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

561 default=0.0, 

562 ) 

563 doEmpiricalReadNoise = pexConfig.Field( 

564 dtype=bool, 

565 default=False, 

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

567 ) 

568 

569 # Linearization. 

570 doLinearize = pexConfig.Field( 

571 dtype=bool, 

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

573 default=True, 

574 ) 

575 

576 # Crosstalk. 

577 doCrosstalk = pexConfig.Field( 

578 dtype=bool, 

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

580 default=False, 

581 ) 

582 doCrosstalkBeforeAssemble = pexConfig.Field( 

583 dtype=bool, 

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

585 default=False, 

586 ) 

587 crosstalk = pexConfig.ConfigurableField( 

588 target=CrosstalkTask, 

589 doc="Intra-CCD crosstalk correction", 

590 ) 

591 

592 # Masking options. 

593 doDefect = pexConfig.Field( 

594 dtype=bool, 

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

596 default=True, 

597 ) 

598 doNanMasking = pexConfig.Field( 

599 dtype=bool, 

600 doc="Mask NAN pixels?", 

601 default=True, 

602 ) 

603 doWidenSaturationTrails = pexConfig.Field( 

604 dtype=bool, 

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

606 default=True 

607 ) 

608 

609 # Brighter-Fatter correction. 

610 doBrighterFatter = pexConfig.Field( 

611 dtype=bool, 

612 default=False, 

613 doc="Apply the brighter fatter correction" 

614 ) 

615 brighterFatterLevel = pexConfig.ChoiceField( 

616 dtype=str, 

617 default="DETECTOR", 

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

619 allowed={ 

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

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

622 } 

623 ) 

624 brighterFatterMaxIter = pexConfig.Field( 

625 dtype=int, 

626 default=10, 

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

628 ) 

629 brighterFatterThreshold = pexConfig.Field( 

630 dtype=float, 

631 default=1000, 

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

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

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

635 ) 

636 brighterFatterApplyGain = pexConfig.Field( 

637 dtype=bool, 

638 default=True, 

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

640 ) 

641 brighterFatterMaskGrowSize = pexConfig.Field( 

642 dtype=int, 

643 default=0, 

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

645 " when brighter-fatter correction is applied." 

646 ) 

647 

648 # Dark subtraction. 

649 doDark = pexConfig.Field( 

650 dtype=bool, 

651 doc="Apply dark frame correction?", 

652 default=True, 

653 ) 

654 darkDataProductName = pexConfig.Field( 

655 dtype=str, 

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

657 default="dark", 

658 ) 

659 

660 # Camera-specific stray light removal. 

661 doStrayLight = pexConfig.Field( 

662 dtype=bool, 

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

664 default=False, 

665 ) 

666 strayLight = pexConfig.ConfigurableField( 

667 target=StrayLightTask, 

668 doc="y-band stray light correction" 

669 ) 

670 

671 # Flat correction. 

672 doFlat = pexConfig.Field( 

673 dtype=bool, 

674 doc="Apply flat field correction?", 

675 default=True, 

676 ) 

677 flatDataProductName = pexConfig.Field( 

678 dtype=str, 

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

680 default="flat", 

681 ) 

682 flatScalingType = pexConfig.ChoiceField( 

683 dtype=str, 

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

685 default='USER', 

686 allowed={ 

687 "USER": "Scale by flatUserScale", 

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

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

690 }, 

691 ) 

692 flatUserScale = pexConfig.Field( 

693 dtype=float, 

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

695 default=1.0, 

696 ) 

697 doTweakFlat = pexConfig.Field( 

698 dtype=bool, 

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

700 default=False 

701 ) 

702 

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

704 doApplyGains = pexConfig.Field( 

705 dtype=bool, 

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

707 default=False, 

708 ) 

709 normalizeGains = pexConfig.Field( 

710 dtype=bool, 

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

712 default=False, 

713 ) 

714 

715 # Fringe correction. 

716 doFringe = pexConfig.Field( 

717 dtype=bool, 

718 doc="Apply fringe correction?", 

719 default=True, 

720 ) 

721 fringe = pexConfig.ConfigurableField( 

722 target=FringeTask, 

723 doc="Fringe subtraction task", 

724 ) 

725 fringeAfterFlat = pexConfig.Field( 

726 dtype=bool, 

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

728 default=True, 

729 ) 

730 

731 # Initial CCD-level background statistics options. 

732 doMeasureBackground = pexConfig.Field( 

733 dtype=bool, 

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

735 default=False, 

736 ) 

737 

738 # Camera-specific masking configuration. 

739 doCameraSpecificMasking = pexConfig.Field( 

740 dtype=bool, 

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

742 default=False, 

743 ) 

744 masking = pexConfig.ConfigurableField( 

745 target=MaskingTask, 

746 doc="Masking task." 

747 ) 

748 

749 # Interpolation options. 

750 

751 doInterpolate = pexConfig.Field( 

752 dtype=bool, 

753 doc="Interpolate masked pixels?", 

754 default=True, 

755 ) 

756 doSaturationInterpolation = pexConfig.Field( 

757 dtype=bool, 

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

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

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

761 default=True, 

762 ) 

763 doNanInterpolation = pexConfig.Field( 

764 dtype=bool, 

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

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

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

768 default=True, 

769 ) 

770 doNanInterpAfterFlat = pexConfig.Field( 

771 dtype=bool, 

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

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

774 default=False, 

775 ) 

776 maskListToInterpolate = pexConfig.ListField( 

777 dtype=str, 

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

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

780 ) 

781 doSaveInterpPixels = pexConfig.Field( 

782 dtype=bool, 

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

784 default=False, 

785 ) 

786 

787 # Default photometric calibration options. 

788 fluxMag0T1 = pexConfig.DictField( 

789 keytype=str, 

790 itemtype=float, 

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

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

793 )) 

794 ) 

795 defaultFluxMag0T1 = pexConfig.Field( 

796 dtype=float, 

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

798 default=pow(10.0, 0.4*28.0) 

799 ) 

800 

801 # Vignette correction configuration. 

802 doVignette = pexConfig.Field( 

803 dtype=bool, 

804 doc="Apply vignetting parameters?", 

805 default=False, 

806 ) 

807 vignette = pexConfig.ConfigurableField( 

808 target=VignetteTask, 

809 doc="Vignetting task.", 

810 ) 

811 

812 # Transmission curve configuration. 

813 doAttachTransmissionCurve = pexConfig.Field( 

814 dtype=bool, 

815 default=False, 

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

817 ) 

818 doUseOpticsTransmission = pexConfig.Field( 

819 dtype=bool, 

820 default=True, 

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

822 ) 

823 doUseFilterTransmission = pexConfig.Field( 

824 dtype=bool, 

825 default=True, 

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

827 ) 

828 doUseSensorTransmission = pexConfig.Field( 

829 dtype=bool, 

830 default=True, 

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

832 ) 

833 doUseAtmosphereTransmission = pexConfig.Field( 

834 dtype=bool, 

835 default=True, 

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

837 ) 

838 

839 # Illumination correction. 

840 doIlluminationCorrection = pexConfig.Field( 

841 dtype=bool, 

842 default=False, 

843 doc="Perform illumination correction?" 

844 ) 

845 illuminationCorrectionDataProductName = pexConfig.Field( 

846 dtype=str, 

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

848 default="illumcor", 

849 ) 

850 illumScale = pexConfig.Field( 

851 dtype=float, 

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

853 default=1.0, 

854 ) 

855 illumFilters = pexConfig.ListField( 

856 dtype=str, 

857 default=[], 

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

859 ) 

860 

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

862 doWrite = pexConfig.Field( 

863 dtype=bool, 

864 doc="Persist postISRCCD?", 

865 default=True, 

866 ) 

867 

868 def validate(self): 

869 super().validate() 

870 if self.doFlat and self.doApplyGains: 

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

872 if self.doBiasBeforeOverscan and self.doTrimToMatchCalib: 

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

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

875 self.maskListToInterpolate.append(self.saturatedMaskName) 

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

877 self.maskListToInterpolate.remove(self.saturatedMaskName) 

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

879 self.maskListToInterpolate.append("UNMASKEDNAN") 

880 

881 

882class IsrTask(pipeBase.PipelineTask, pipeBase.CmdLineTask): 

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

884 

885 The process for correcting imaging data is very similar from 

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

887 doing these corrections, including the ability to turn certain 

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

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

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

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

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

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

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

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

896 subclassed for different camera, although the most camera specific 

897 methods have been split into subtasks that can be redirected 

898 appropriately. 

899 

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

901 the defaults from `lsst.ip.isr`. 

902 

903 Parameters 

904 ---------- 

905 args : `list` 

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

907 kwargs : `dict`, optional 

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

909 """ 

910 ConfigClass = IsrTaskConfig 

911 _DefaultName = "isr" 

912 

913 def __init__(self, **kwargs): 

914 super().__init__(**kwargs) 

915 self.makeSubtask("assembleCcd") 

916 self.makeSubtask("crosstalk") 

917 self.makeSubtask("strayLight") 

918 self.makeSubtask("fringe") 

919 self.makeSubtask("masking") 

920 self.makeSubtask("overscan") 

921 self.makeSubtask("vignette") 

922 

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

924 inputs = butlerQC.get(inputRefs) 

925 

926 try: 

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

928 except Exception as e: 

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

930 (inputRefs, e)) 

931 

932 inputs['isGen3'] = True 

933 

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

935 

936 if self.config.doCrosstalk is True: 

937 # Crosstalk sources need to be defined by the pipeline 

938 # yaml if they exist. 

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

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

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

942 else: 

943 coeffVector = (self.config.crosstalk.crosstalkValues 

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

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

946 inputs['crosstalk'] = crosstalkCalib 

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

948 if 'crosstalkSources' not in inputs: 

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

950 

951 if self.doLinearize(detector) is True: 

952 if 'linearizer' in inputs: 

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

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

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

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

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

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

959 detector=detector, 

960 log=self.log) 

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

962 else: 

963 linearizer = inputs['linearizer'] 

964 linearizer.log = self.log 

965 inputs['linearizer'] = linearizer 

966 else: 

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

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

969 

970 if self.config.doDefect is True: 

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

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

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

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

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

976 

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

978 # the information as a numpy array. 

979 if self.config.doBrighterFatter: 

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

981 if brighterFatterKernel is None: 

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

983 

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

985 detId = detector.getId() 

986 inputs['bfGains'] = brighterFatterKernel.gain 

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

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

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

990 if brighterFatterKernel.detectorKernel: 

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

992 elif brighterFatterKernel.detectorKernelFromAmpKernels: 

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

994 else: 

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

996 else: 

997 # TODO DM-15631 for implementing this 

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

999 

1000 if self.config.doFringe is True and self.fringe.checkFilter(inputs['ccdExposure']): 

1001 expId = inputs['ccdExposure'].getInfo().getVisitInfo().getExposureId() 

1002 inputs['fringes'] = self.fringe.loadFringes(inputs['fringes'], 

1003 expId=expId, 

1004 assembler=self.assembleCcd 

1005 if self.config.doAssembleIsrExposures else None) 

1006 else: 

1007 inputs['fringes'] = pipeBase.Struct(fringes=None) 

1008 

1009 if self.config.doStrayLight is True and self.strayLight.checkFilter(inputs['ccdExposure']): 

1010 if 'strayLightData' not in inputs: 

1011 inputs['strayLightData'] = None 

1012 

1013 outputs = self.run(**inputs) 

1014 butlerQC.put(outputs, outputRefs) 

1015 

1016 def readIsrData(self, dataRef, rawExposure): 

1017 """Retrieve necessary frames for instrument signature removal. 

1018 

1019 Pre-fetching all required ISR data products limits the IO 

1020 required by the ISR. Any conflict between the calibration data 

1021 available and that needed for ISR is also detected prior to 

1022 doing processing, allowing it to fail quickly. 

1023 

1024 Parameters 

1025 ---------- 

1026 dataRef : `daf.persistence.butlerSubset.ButlerDataRef` 

1027 Butler reference of the detector data to be processed 

1028 rawExposure : `afw.image.Exposure` 

1029 The raw exposure that will later be corrected with the 

1030 retrieved calibration data; should not be modified in this 

1031 method. 

1032 

1033 Returns 

1034 ------- 

1035 result : `lsst.pipe.base.Struct` 

1036 Result struct with components (which may be `None`): 

1037 - ``bias``: bias calibration frame (`afw.image.Exposure`) 

1038 - ``linearizer``: functor for linearization (`ip.isr.linearize.LinearizeBase`) 

1039 - ``crosstalkSources``: list of possible crosstalk sources (`list`) 

1040 - ``dark``: dark calibration frame (`afw.image.Exposure`) 

1041 - ``flat``: flat calibration frame (`afw.image.Exposure`) 

1042 - ``bfKernel``: Brighter-Fatter kernel (`numpy.ndarray`) 

1043 - ``defects``: list of defects (`lsst.ip.isr.Defects`) 

1044 - ``fringes``: `lsst.pipe.base.Struct` with components: 

1045 - ``fringes``: fringe calibration frame (`afw.image.Exposure`) 

1046 - ``seed``: random seed derived from the ccdExposureId for random 

1047 number generator (`uint32`). 

1048 - ``opticsTransmission``: `lsst.afw.image.TransmissionCurve` 

1049 A ``TransmissionCurve`` that represents the throughput of the optics, 

1050 to be evaluated in focal-plane coordinates. 

1051 - ``filterTransmission`` : `lsst.afw.image.TransmissionCurve` 

1052 A ``TransmissionCurve`` that represents the throughput of the filter 

1053 itself, to be evaluated in focal-plane coordinates. 

1054 - ``sensorTransmission`` : `lsst.afw.image.TransmissionCurve` 

1055 A ``TransmissionCurve`` that represents the throughput of the sensor 

1056 itself, to be evaluated in post-assembly trimmed detector coordinates. 

1057 - ``atmosphereTransmission`` : `lsst.afw.image.TransmissionCurve` 

1058 A ``TransmissionCurve`` that represents the throughput of the 

1059 atmosphere, assumed to be spatially constant. 

1060 - ``strayLightData`` : `object` 

1061 An opaque object containing calibration information for 

1062 stray-light correction. If `None`, no correction will be 

1063 performed. 

1064 - ``illumMaskedImage`` : illumination correction image (`lsst.afw.image.MaskedImage`) 

1065 

1066 Raises 

1067 ------ 

1068 NotImplementedError : 

1069 Raised if a per-amplifier brighter-fatter kernel is requested by the configuration. 

1070 """ 

1071 try: 

1072 dateObs = rawExposure.getInfo().getVisitInfo().getDate() 

1073 dateObs = dateObs.toPython().isoformat() 

1074 except RuntimeError: 

1075 self.log.warn("Unable to identify dateObs for rawExposure.") 

1076 dateObs = None 

1077 

1078 ccd = rawExposure.getDetector() 

1079 filterLabel = rawExposure.getFilterLabel() 

1080 rawExposure.mask.addMaskPlane("UNMASKEDNAN") # needed to match pre DM-15862 processing. 

1081 biasExposure = (self.getIsrExposure(dataRef, self.config.biasDataProductName) 

1082 if self.config.doBias else None) 

1083 # immediate=True required for functors and linearizers are functors; see ticket DM-6515 

1084 linearizer = (dataRef.get("linearizer", immediate=True) 

1085 if self.doLinearize(ccd) else None) 

1086 if linearizer is not None and not isinstance(linearizer, numpy.ndarray): 

1087 linearizer.log = self.log 

1088 if isinstance(linearizer, numpy.ndarray): 

1089 linearizer = linearize.Linearizer(table=linearizer, detector=ccd) 

1090 

1091 crosstalkCalib = None 

1092 if self.config.doCrosstalk: 

1093 try: 

1094 crosstalkCalib = dataRef.get("crosstalk", immediate=True) 

1095 except NoResults: 

1096 coeffVector = (self.config.crosstalk.crosstalkValues 

1097 if self.config.crosstalk.useConfigCoefficients else None) 

1098 crosstalkCalib = CrosstalkCalib().fromDetector(ccd, coeffVector=coeffVector) 

1099 crosstalkSources = (self.crosstalk.prepCrosstalk(dataRef, crosstalkCalib) 

1100 if self.config.doCrosstalk else None) 

1101 

1102 darkExposure = (self.getIsrExposure(dataRef, self.config.darkDataProductName) 

1103 if self.config.doDark else None) 

1104 flatExposure = (self.getIsrExposure(dataRef, self.config.flatDataProductName, 

1105 dateObs=dateObs) 

1106 if self.config.doFlat else None) 

1107 

1108 brighterFatterKernel = None 

1109 brighterFatterGains = None 

1110 if self.config.doBrighterFatter is True: 

1111 try: 

1112 # Use the new-style cp_pipe version of the kernel if it exists 

1113 # If using a new-style kernel, always use the self-consistent 

1114 # gains, i.e. the ones inside the kernel object itself 

1115 brighterFatterKernel = dataRef.get("brighterFatterKernel") 

1116 brighterFatterGains = brighterFatterKernel.gain 

1117 self.log.info("New style bright-fatter kernel (brighterFatterKernel) loaded") 

1118 except NoResults: 

1119 try: # Fall back to the old-style numpy-ndarray style kernel if necessary. 

1120 brighterFatterKernel = dataRef.get("bfKernel") 

1121 self.log.info("Old style bright-fatter kernel (np.array) loaded") 

1122 except NoResults: 

1123 brighterFatterKernel = None 

1124 if brighterFatterKernel is not None and not isinstance(brighterFatterKernel, numpy.ndarray): 

1125 # If the kernel is not an ndarray, it's the cp_pipe version 

1126 # so extract the kernel for this detector, or raise an error 

1127 if self.config.brighterFatterLevel == 'DETECTOR': 

1128 if brighterFatterKernel.detectorKernel: 

1129 brighterFatterKernel = brighterFatterKernel.detectorKernel[ccd.getId()] 

1130 elif brighterFatterKernel.detectorKernelFromAmpKernels: 

1131 brighterFatterKernel = brighterFatterKernel.detectorKernelFromAmpKernels[ccd.getId()] 

1132 else: 

1133 raise RuntimeError("Failed to extract kernel from new-style BF kernel.") 

1134 else: 

1135 # TODO DM-15631 for implementing this 

1136 raise NotImplementedError("Per-amplifier brighter-fatter correction not implemented") 

1137 

1138 defectList = (dataRef.get("defects") 

1139 if self.config.doDefect else None) 

1140 fringeStruct = (self.fringe.readFringes(dataRef, assembler=self.assembleCcd 

1141 if self.config.doAssembleIsrExposures else None) 

1142 if self.config.doFringe and self.fringe.checkFilter(rawExposure) 

1143 else pipeBase.Struct(fringes=None)) 

1144 

1145 if self.config.doAttachTransmissionCurve: 

1146 opticsTransmission = (dataRef.get("transmission_optics") 

1147 if self.config.doUseOpticsTransmission else None) 

1148 filterTransmission = (dataRef.get("transmission_filter") 

1149 if self.config.doUseFilterTransmission else None) 

1150 sensorTransmission = (dataRef.get("transmission_sensor") 

1151 if self.config.doUseSensorTransmission else None) 

1152 atmosphereTransmission = (dataRef.get("transmission_atmosphere") 

1153 if self.config.doUseAtmosphereTransmission else None) 

1154 else: 

1155 opticsTransmission = None 

1156 filterTransmission = None 

1157 sensorTransmission = None 

1158 atmosphereTransmission = None 

1159 

1160 if self.config.doStrayLight: 

1161 strayLightData = self.strayLight.readIsrData(dataRef, rawExposure) 

1162 else: 

1163 strayLightData = None 

1164 

1165 illumMaskedImage = (self.getIsrExposure(dataRef, 

1166 self.config.illuminationCorrectionDataProductName).getMaskedImage() 

1167 if (self.config.doIlluminationCorrection 

1168 and filterLabel in self.config.illumFilters) 

1169 else None) 

1170 

1171 # Struct should include only kwargs to run() 

1172 return pipeBase.Struct(bias=biasExposure, 

1173 linearizer=linearizer, 

1174 crosstalk=crosstalkCalib, 

1175 crosstalkSources=crosstalkSources, 

1176 dark=darkExposure, 

1177 flat=flatExposure, 

1178 bfKernel=brighterFatterKernel, 

1179 bfGains=brighterFatterGains, 

1180 defects=defectList, 

1181 fringes=fringeStruct, 

1182 opticsTransmission=opticsTransmission, 

1183 filterTransmission=filterTransmission, 

1184 sensorTransmission=sensorTransmission, 

1185 atmosphereTransmission=atmosphereTransmission, 

1186 strayLightData=strayLightData, 

1187 illumMaskedImage=illumMaskedImage 

1188 ) 

1189 

1190 @pipeBase.timeMethod 

1191 def run(self, ccdExposure, camera=None, bias=None, linearizer=None, 

1192 crosstalk=None, crosstalkSources=None, 

1193 dark=None, flat=None, bfKernel=None, bfGains=None, defects=None, 

1194 fringes=pipeBase.Struct(fringes=None), opticsTransmission=None, filterTransmission=None, 

1195 sensorTransmission=None, atmosphereTransmission=None, 

1196 detectorNum=None, strayLightData=None, illumMaskedImage=None, 

1197 isGen3=False, 

1198 ): 

1199 """Perform instrument signature removal on an exposure. 

1200 

1201 Steps included in the ISR processing, in order performed, are: 

1202 - saturation and suspect pixel masking 

1203 - overscan subtraction 

1204 - CCD assembly of individual amplifiers 

1205 - bias subtraction 

1206 - variance image construction 

1207 - linearization of non-linear response 

1208 - crosstalk masking 

1209 - brighter-fatter correction 

1210 - dark subtraction 

1211 - fringe correction 

1212 - stray light subtraction 

1213 - flat correction 

1214 - masking of known defects and camera specific features 

1215 - vignette calculation 

1216 - appending transmission curve and distortion model 

1217 

1218 Parameters 

1219 ---------- 

1220 ccdExposure : `lsst.afw.image.Exposure` 

1221 The raw exposure that is to be run through ISR. The 

1222 exposure is modified by this method. 

1223 camera : `lsst.afw.cameraGeom.Camera`, optional 

1224 The camera geometry for this exposure. Required if ``isGen3`` is 

1225 `True` and one or more of ``ccdExposure``, ``bias``, ``dark``, or 

1226 ``flat`` does not have an associated detector. 

1227 bias : `lsst.afw.image.Exposure`, optional 

1228 Bias calibration frame. 

1229 linearizer : `lsst.ip.isr.linearize.LinearizeBase`, optional 

1230 Functor for linearization. 

1231 crosstalk : `lsst.ip.isr.crosstalk.CrosstalkCalib`, optional 

1232 Calibration for crosstalk. 

1233 crosstalkSources : `list`, optional 

1234 List of possible crosstalk sources. 

1235 dark : `lsst.afw.image.Exposure`, optional 

1236 Dark calibration frame. 

1237 flat : `lsst.afw.image.Exposure`, optional 

1238 Flat calibration frame. 

1239 bfKernel : `numpy.ndarray`, optional 

1240 Brighter-fatter kernel. 

1241 bfGains : `dict` of `float`, optional 

1242 Gains used to override the detector's nominal gains for the 

1243 brighter-fatter correction. A dict keyed by amplifier name for 

1244 the detector in question. 

1245 defects : `lsst.ip.isr.Defects`, optional 

1246 List of defects. 

1247 fringes : `lsst.pipe.base.Struct`, optional 

1248 Struct containing the fringe correction data, with 

1249 elements: 

1250 - ``fringes``: fringe calibration frame (`afw.image.Exposure`) 

1251 - ``seed``: random seed derived from the ccdExposureId for random 

1252 number generator (`uint32`) 

1253 opticsTransmission: `lsst.afw.image.TransmissionCurve`, optional 

1254 A ``TransmissionCurve`` that represents the throughput of the optics, 

1255 to be evaluated in focal-plane coordinates. 

1256 filterTransmission : `lsst.afw.image.TransmissionCurve` 

1257 A ``TransmissionCurve`` that represents the throughput of the filter 

1258 itself, to be evaluated in focal-plane coordinates. 

1259 sensorTransmission : `lsst.afw.image.TransmissionCurve` 

1260 A ``TransmissionCurve`` that represents the throughput of the sensor 

1261 itself, to be evaluated in post-assembly trimmed detector coordinates. 

1262 atmosphereTransmission : `lsst.afw.image.TransmissionCurve` 

1263 A ``TransmissionCurve`` that represents the throughput of the 

1264 atmosphere, assumed to be spatially constant. 

1265 detectorNum : `int`, optional 

1266 The integer number for the detector to process. 

1267 isGen3 : bool, optional 

1268 Flag this call to run() as using the Gen3 butler environment. 

1269 strayLightData : `object`, optional 

1270 Opaque object containing calibration information for stray-light 

1271 correction. If `None`, no correction will be performed. 

1272 illumMaskedImage : `lsst.afw.image.MaskedImage`, optional 

1273 Illumination correction image. 

1274 

1275 Returns 

1276 ------- 

1277 result : `lsst.pipe.base.Struct` 

1278 Result struct with component: 

1279 - ``exposure`` : `afw.image.Exposure` 

1280 The fully ISR corrected exposure. 

1281 - ``outputExposure`` : `afw.image.Exposure` 

1282 An alias for `exposure` 

1283 - ``ossThumb`` : `numpy.ndarray` 

1284 Thumbnail image of the exposure after overscan subtraction. 

1285 - ``flattenedThumb`` : `numpy.ndarray` 

1286 Thumbnail image of the exposure after flat-field correction. 

1287 

1288 Raises 

1289 ------ 

1290 RuntimeError 

1291 Raised if a configuration option is set to True, but the 

1292 required calibration data has not been specified. 

1293 

1294 Notes 

1295 ----- 

1296 The current processed exposure can be viewed by setting the 

1297 appropriate lsstDebug entries in the `debug.display` 

1298 dictionary. The names of these entries correspond to some of 

1299 the IsrTaskConfig Boolean options, with the value denoting the 

1300 frame to use. The exposure is shown inside the matching 

1301 option check and after the processing of that step has 

1302 finished. The steps with debug points are: 

1303 

1304 doAssembleCcd 

1305 doBias 

1306 doCrosstalk 

1307 doBrighterFatter 

1308 doDark 

1309 doFringe 

1310 doStrayLight 

1311 doFlat 

1312 

1313 In addition, setting the "postISRCCD" entry displays the 

1314 exposure after all ISR processing has finished. 

1315 

1316 """ 

1317 

1318 if isGen3 is True: 

1319 # Gen3 currently cannot automatically do configuration overrides. 

1320 # DM-15257 looks to discuss this issue. 

1321 # Configure input exposures; 

1322 if detectorNum is None: 

1323 raise RuntimeError("Must supply the detectorNum if running as Gen3.") 

1324 

1325 ccdExposure = self.ensureExposure(ccdExposure, camera, detectorNum) 

1326 bias = self.ensureExposure(bias, camera, detectorNum) 

1327 dark = self.ensureExposure(dark, camera, detectorNum) 

1328 flat = self.ensureExposure(flat, camera, detectorNum) 

1329 else: 

1330 if isinstance(ccdExposure, ButlerDataRef): 

1331 return self.runDataRef(ccdExposure) 

1332 

1333 ccd = ccdExposure.getDetector() 

1334 filterLabel = ccdExposure.getFilterLabel() 

1335 

1336 if not ccd: 

1337 assert not self.config.doAssembleCcd, "You need a Detector to run assembleCcd." 

1338 ccd = [FakeAmp(ccdExposure, self.config)] 

1339 

1340 # Validate Input 

1341 if self.config.doBias and bias is None: 

1342 raise RuntimeError("Must supply a bias exposure if config.doBias=True.") 

1343 if self.doLinearize(ccd) and linearizer is None: 

1344 raise RuntimeError("Must supply a linearizer if config.doLinearize=True for this detector.") 

1345 if self.config.doBrighterFatter and bfKernel is None: 

1346 raise RuntimeError("Must supply a kernel if config.doBrighterFatter=True.") 

1347 if self.config.doDark and dark is None: 

1348 raise RuntimeError("Must supply a dark exposure if config.doDark=True.") 

1349 if self.config.doFlat and flat is None: 

1350 raise RuntimeError("Must supply a flat exposure if config.doFlat=True.") 

1351 if self.config.doDefect and defects is None: 

1352 raise RuntimeError("Must supply defects if config.doDefect=True.") 

1353 if (self.config.doFringe and filterLabel in self.fringe.config.filters 

1354 and fringes.fringes is None): 

1355 # The `fringes` object needs to be a pipeBase.Struct, as 

1356 # we use it as a `dict` for the parameters of 

1357 # `FringeTask.run()`. The `fringes.fringes` `list` may 

1358 # not be `None` if `doFringe=True`. Otherwise, raise. 

1359 raise RuntimeError("Must supply fringe exposure as a pipeBase.Struct.") 

1360 if (self.config.doIlluminationCorrection and filterLabel in self.config.illumFilters 

1361 and illumMaskedImage is None): 

1362 raise RuntimeError("Must supply an illumcor if config.doIlluminationCorrection=True.") 

1363 

1364 # Begin ISR processing. 

1365 if self.config.doConvertIntToFloat: 

1366 self.log.info("Converting exposure to floating point values.") 

1367 ccdExposure = self.convertIntToFloat(ccdExposure) 

1368 

1369 if self.config.doBias and self.config.doBiasBeforeOverscan: 

1370 self.log.info("Applying bias correction.") 

1371 isrFunctions.biasCorrection(ccdExposure.getMaskedImage(), bias.getMaskedImage(), 

1372 trimToFit=self.config.doTrimToMatchCalib) 

1373 self.debugView(ccdExposure, "doBias") 

1374 

1375 # Amplifier level processing. 

1376 overscans = [] 

1377 for amp in ccd: 

1378 # if ccdExposure is one amp, check for coverage to prevent performing ops multiple times 

1379 if ccdExposure.getBBox().contains(amp.getBBox()): 

1380 # Check for fully masked bad amplifiers, and generate masks for SUSPECT and SATURATED values. 

1381 badAmp = self.maskAmplifier(ccdExposure, amp, defects) 

1382 

1383 if self.config.doOverscan and not badAmp: 

1384 # Overscan correction on amp-by-amp basis. 

1385 overscanResults = self.overscanCorrection(ccdExposure, amp) 

1386 self.log.debug("Corrected overscan for amplifier %s.", amp.getName()) 

1387 if overscanResults is not None and \ 

1388 self.config.qa is not None and self.config.qa.saveStats is True: 

1389 if isinstance(overscanResults.overscanFit, float): 

1390 qaMedian = overscanResults.overscanFit 

1391 qaStdev = float("NaN") 

1392 else: 

1393 qaStats = afwMath.makeStatistics(overscanResults.overscanFit, 

1394 afwMath.MEDIAN | afwMath.STDEVCLIP) 

1395 qaMedian = qaStats.getValue(afwMath.MEDIAN) 

1396 qaStdev = qaStats.getValue(afwMath.STDEVCLIP) 

1397 

1398 self.metadata.set(f"FIT MEDIAN {amp.getName()}", qaMedian) 

1399 self.metadata.set(f"FIT STDEV {amp.getName()}", qaStdev) 

1400 self.log.debug(" Overscan stats for amplifer %s: %f +/- %f", 

1401 amp.getName(), qaMedian, qaStdev) 

1402 

1403 # Residuals after overscan correction 

1404 qaStatsAfter = afwMath.makeStatistics(overscanResults.overscanImage, 

1405 afwMath.MEDIAN | afwMath.STDEVCLIP) 

1406 qaMedianAfter = qaStatsAfter.getValue(afwMath.MEDIAN) 

1407 qaStdevAfter = qaStatsAfter.getValue(afwMath.STDEVCLIP) 

1408 

1409 self.metadata.set(f"RESIDUAL MEDIAN {amp.getName()}", qaMedianAfter) 

1410 self.metadata.set(f"RESIDUAL STDEV {amp.getName()}", qaStdevAfter) 

1411 self.log.debug(" Overscan stats for amplifer %s after correction: %f +/- %f", 

1412 amp.getName(), qaMedianAfter, qaStdevAfter) 

1413 

1414 ccdExposure.getMetadata().set('OVERSCAN', "Overscan corrected") 

1415 else: 

1416 if badAmp: 

1417 self.log.warn("Amplifier %s is bad.", amp.getName()) 

1418 overscanResults = None 

1419 

1420 overscans.append(overscanResults if overscanResults is not None else None) 

1421 else: 

1422 self.log.info("Skipped OSCAN for %s.", amp.getName()) 

1423 

1424 if self.config.doCrosstalk and self.config.doCrosstalkBeforeAssemble: 

1425 self.log.info("Applying crosstalk correction.") 

1426 self.crosstalk.run(ccdExposure, crosstalk=crosstalk, 

1427 crosstalkSources=crosstalkSources) 

1428 self.debugView(ccdExposure, "doCrosstalk") 

1429 

1430 if self.config.doAssembleCcd: 

1431 self.log.info("Assembling CCD from amplifiers.") 

1432 ccdExposure = self.assembleCcd.assembleCcd(ccdExposure) 

1433 

1434 if self.config.expectWcs and not ccdExposure.getWcs(): 

1435 self.log.warn("No WCS found in input exposure.") 

1436 self.debugView(ccdExposure, "doAssembleCcd") 

1437 

1438 ossThumb = None 

1439 if self.config.qa.doThumbnailOss: 

1440 ossThumb = isrQa.makeThumbnail(ccdExposure, isrQaConfig=self.config.qa) 

1441 

1442 if self.config.doBias and not self.config.doBiasBeforeOverscan: 

1443 self.log.info("Applying bias correction.") 

1444 isrFunctions.biasCorrection(ccdExposure.getMaskedImage(), bias.getMaskedImage(), 

1445 trimToFit=self.config.doTrimToMatchCalib) 

1446 self.debugView(ccdExposure, "doBias") 

1447 

1448 if self.config.doVariance: 

1449 for amp, overscanResults in zip(ccd, overscans): 

1450 if ccdExposure.getBBox().contains(amp.getBBox()): 

1451 self.log.debug("Constructing variance map for amplifer %s.", amp.getName()) 

1452 ampExposure = ccdExposure.Factory(ccdExposure, amp.getBBox()) 

1453 if overscanResults is not None: 

1454 self.updateVariance(ampExposure, amp, 

1455 overscanImage=overscanResults.overscanImage) 

1456 else: 

1457 self.updateVariance(ampExposure, amp, 

1458 overscanImage=None) 

1459 if self.config.qa is not None and self.config.qa.saveStats is True: 

1460 qaStats = afwMath.makeStatistics(ampExposure.getVariance(), 

1461 afwMath.MEDIAN | afwMath.STDEVCLIP) 

1462 self.metadata.set(f"ISR VARIANCE {amp.getName()} MEDIAN", 

1463 qaStats.getValue(afwMath.MEDIAN)) 

1464 self.metadata.set(f"ISR VARIANCE {amp.getName()} STDEV", 

1465 qaStats.getValue(afwMath.STDEVCLIP)) 

1466 self.log.debug(" Variance stats for amplifer %s: %f +/- %f.", 

1467 amp.getName(), qaStats.getValue(afwMath.MEDIAN), 

1468 qaStats.getValue(afwMath.STDEVCLIP)) 

1469 

1470 if self.doLinearize(ccd): 

1471 self.log.info("Applying linearizer.") 

1472 linearizer.applyLinearity(image=ccdExposure.getMaskedImage().getImage(), 

1473 detector=ccd, log=self.log) 

1474 

1475 if self.config.doCrosstalk and not self.config.doCrosstalkBeforeAssemble: 

1476 self.log.info("Applying crosstalk correction.") 

1477 self.crosstalk.run(ccdExposure, crosstalk=crosstalk, 

1478 crosstalkSources=crosstalkSources, isTrimmed=True) 

1479 self.debugView(ccdExposure, "doCrosstalk") 

1480 

1481 # Masking block. Optionally mask known defects, NAN pixels, widen trails, and do 

1482 # anything else the camera needs. Saturated and suspect pixels have already been masked. 

1483 if self.config.doDefect: 

1484 self.log.info("Masking defects.") 

1485 self.maskDefect(ccdExposure, defects) 

1486 

1487 if self.config.numEdgeSuspect > 0: 

1488 self.log.info("Masking edges as SUSPECT.") 

1489 self.maskEdges(ccdExposure, numEdgePixels=self.config.numEdgeSuspect, 

1490 maskPlane="SUSPECT", level=self.config.edgeMaskLevel) 

1491 

1492 if self.config.doNanMasking: 

1493 self.log.info("Masking NAN value pixels.") 

1494 self.maskNan(ccdExposure) 

1495 

1496 if self.config.doWidenSaturationTrails: 

1497 self.log.info("Widening saturation trails.") 

1498 isrFunctions.widenSaturationTrails(ccdExposure.getMaskedImage().getMask()) 

1499 

1500 if self.config.doCameraSpecificMasking: 

1501 self.log.info("Masking regions for camera specific reasons.") 

1502 self.masking.run(ccdExposure) 

1503 

1504 if self.config.doBrighterFatter: 

1505 # We need to apply flats and darks before we can interpolate, and we 

1506 # need to interpolate before we do B-F, but we do B-F without the 

1507 # flats and darks applied so we can work in units of electrons or holes. 

1508 # This context manager applies and then removes the darks and flats. 

1509 # 

1510 # We also do not want to interpolate values here, so operate on temporary 

1511 # images so we can apply only the BF-correction and roll back the 

1512 # interpolation. 

1513 interpExp = ccdExposure.clone() 

1514 with self.flatContext(interpExp, flat, dark): 

1515 isrFunctions.interpolateFromMask( 

1516 maskedImage=interpExp.getMaskedImage(), 

1517 fwhm=self.config.fwhm, 

1518 growSaturatedFootprints=self.config.growSaturationFootprintSize, 

1519 maskNameList=self.config.maskListToInterpolate 

1520 ) 

1521 bfExp = interpExp.clone() 

1522 

1523 self.log.info("Applying brighter fatter correction using kernel type %s / gains %s.", 

1524 type(bfKernel), type(bfGains)) 

1525 bfResults = isrFunctions.brighterFatterCorrection(bfExp, bfKernel, 

1526 self.config.brighterFatterMaxIter, 

1527 self.config.brighterFatterThreshold, 

1528 self.config.brighterFatterApplyGain, 

1529 bfGains) 

1530 if bfResults[1] == self.config.brighterFatterMaxIter: 

1531 self.log.warn("Brighter fatter correction did not converge, final difference %f.", 

1532 bfResults[0]) 

1533 else: 

1534 self.log.info("Finished brighter fatter correction in %d iterations.", 

1535 bfResults[1]) 

1536 image = ccdExposure.getMaskedImage().getImage() 

1537 bfCorr = bfExp.getMaskedImage().getImage() 

1538 bfCorr -= interpExp.getMaskedImage().getImage() 

1539 image += bfCorr 

1540 

1541 # Applying the brighter-fatter correction applies a 

1542 # convolution to the science image. At the edges this 

1543 # convolution may not have sufficient valid pixels to 

1544 # produce a valid correction. Mark pixels within the size 

1545 # of the brighter-fatter kernel as EDGE to warn of this 

1546 # fact. 

1547 self.log.info("Ensuring image edges are masked as SUSPECT to the brighter-fatter kernel size.") 

1548 self.maskEdges(ccdExposure, numEdgePixels=numpy.max(bfKernel.shape) // 2, 

1549 maskPlane="EDGE") 

1550 

1551 if self.config.brighterFatterMaskGrowSize > 0: 

1552 self.log.info("Growing masks to account for brighter-fatter kernel convolution.") 

1553 for maskPlane in self.config.maskListToInterpolate: 

1554 isrFunctions.growMasks(ccdExposure.getMask(), 

1555 radius=self.config.brighterFatterMaskGrowSize, 

1556 maskNameList=maskPlane, 

1557 maskValue=maskPlane) 

1558 

1559 self.debugView(ccdExposure, "doBrighterFatter") 

1560 

1561 if self.config.doDark: 

1562 self.log.info("Applying dark correction.") 

1563 self.darkCorrection(ccdExposure, dark) 

1564 self.debugView(ccdExposure, "doDark") 

1565 

1566 if self.config.doFringe and not self.config.fringeAfterFlat: 

1567 self.log.info("Applying fringe correction before flat.") 

1568 self.fringe.run(ccdExposure, **fringes.getDict()) 

1569 self.debugView(ccdExposure, "doFringe") 

1570 

1571 if self.config.doStrayLight and self.strayLight.check(ccdExposure): 

1572 self.log.info("Checking strayLight correction.") 

1573 self.strayLight.run(ccdExposure, strayLightData) 

1574 self.debugView(ccdExposure, "doStrayLight") 

1575 

1576 if self.config.doFlat: 

1577 self.log.info("Applying flat correction.") 

1578 self.flatCorrection(ccdExposure, flat) 

1579 self.debugView(ccdExposure, "doFlat") 

1580 

1581 if self.config.doApplyGains: 

1582 self.log.info("Applying gain correction instead of flat.") 

1583 isrFunctions.applyGains(ccdExposure, self.config.normalizeGains) 

1584 

1585 if self.config.doFringe and self.config.fringeAfterFlat: 

1586 self.log.info("Applying fringe correction after flat.") 

1587 self.fringe.run(ccdExposure, **fringes.getDict()) 

1588 

1589 if self.config.doVignette: 

1590 self.log.info("Constructing Vignette polygon.") 

1591 self.vignettePolygon = self.vignette.run(ccdExposure) 

1592 

1593 if self.config.vignette.doWriteVignettePolygon: 

1594 self.setValidPolygonIntersect(ccdExposure, self.vignettePolygon) 

1595 

1596 if self.config.doAttachTransmissionCurve: 

1597 self.log.info("Adding transmission curves.") 

1598 isrFunctions.attachTransmissionCurve(ccdExposure, opticsTransmission=opticsTransmission, 

1599 filterTransmission=filterTransmission, 

1600 sensorTransmission=sensorTransmission, 

1601 atmosphereTransmission=atmosphereTransmission) 

1602 

1603 flattenedThumb = None 

1604 if self.config.qa.doThumbnailFlattened: 

1605 flattenedThumb = isrQa.makeThumbnail(ccdExposure, isrQaConfig=self.config.qa) 

1606 

1607 if self.config.doIlluminationCorrection and filterLabel in self.config.illumFilters: 

1608 self.log.info("Performing illumination correction.") 

1609 isrFunctions.illuminationCorrection(ccdExposure.getMaskedImage(), 

1610 illumMaskedImage, illumScale=self.config.illumScale, 

1611 trimToFit=self.config.doTrimToMatchCalib) 

1612 

1613 preInterpExp = None 

1614 if self.config.doSaveInterpPixels: 

1615 preInterpExp = ccdExposure.clone() 

1616 

1617 # Reset and interpolate bad pixels. 

1618 # 

1619 # Large contiguous bad regions (which should have the BAD mask 

1620 # bit set) should have their values set to the image median. 

1621 # This group should include defects and bad amplifiers. As the 

1622 # area covered by these defects are large, there's little 

1623 # reason to expect that interpolation would provide a more 

1624 # useful value. 

1625 # 

1626 # Smaller defects can be safely interpolated after the larger 

1627 # regions have had their pixel values reset. This ensures 

1628 # that the remaining defects adjacent to bad amplifiers (as an 

1629 # example) do not attempt to interpolate extreme values. 

1630 if self.config.doSetBadRegions: 

1631 badPixelCount, badPixelValue = isrFunctions.setBadRegions(ccdExposure) 

1632 if badPixelCount > 0: 

1633 self.log.info("Set %d BAD pixels to %f.", badPixelCount, badPixelValue) 

1634 

1635 if self.config.doInterpolate: 

1636 self.log.info("Interpolating masked pixels.") 

1637 isrFunctions.interpolateFromMask( 

1638 maskedImage=ccdExposure.getMaskedImage(), 

1639 fwhm=self.config.fwhm, 

1640 growSaturatedFootprints=self.config.growSaturationFootprintSize, 

1641 maskNameList=list(self.config.maskListToInterpolate) 

1642 ) 

1643 

1644 self.roughZeroPoint(ccdExposure) 

1645 

1646 if self.config.doMeasureBackground: 

1647 self.log.info("Measuring background level.") 

1648 self.measureBackground(ccdExposure, self.config.qa) 

1649 

1650 if self.config.qa is not None and self.config.qa.saveStats is True: 

1651 for amp in ccd: 

1652 ampExposure = ccdExposure.Factory(ccdExposure, amp.getBBox()) 

1653 qaStats = afwMath.makeStatistics(ampExposure.getImage(), 

1654 afwMath.MEDIAN | afwMath.STDEVCLIP) 

1655 self.metadata.set("ISR BACKGROUND {} MEDIAN".format(amp.getName()), 

1656 qaStats.getValue(afwMath.MEDIAN)) 

1657 self.metadata.set("ISR BACKGROUND {} STDEV".format(amp.getName()), 

1658 qaStats.getValue(afwMath.STDEVCLIP)) 

1659 self.log.debug(" Background stats for amplifer %s: %f +/- %f", 

1660 amp.getName(), qaStats.getValue(afwMath.MEDIAN), 

1661 qaStats.getValue(afwMath.STDEVCLIP)) 

1662 

1663 self.debugView(ccdExposure, "postISRCCD") 

1664 

1665 return pipeBase.Struct( 

1666 exposure=ccdExposure, 

1667 ossThumb=ossThumb, 

1668 flattenedThumb=flattenedThumb, 

1669 

1670 preInterpolatedExposure=preInterpExp, 

1671 outputExposure=ccdExposure, 

1672 outputOssThumbnail=ossThumb, 

1673 outputFlattenedThumbnail=flattenedThumb, 

1674 ) 

1675 

1676 @pipeBase.timeMethod 

1677 def runDataRef(self, sensorRef): 

1678 """Perform instrument signature removal on a ButlerDataRef of a Sensor. 

1679 

1680 This method contains the `CmdLineTask` interface to the ISR 

1681 processing. All IO is handled here, freeing the `run()` method 

1682 to manage only pixel-level calculations. The steps performed 

1683 are: 

1684 - Read in necessary detrending/isr/calibration data. 

1685 - Process raw exposure in `run()`. 

1686 - Persist the ISR-corrected exposure as "postISRCCD" if 

1687 config.doWrite=True. 

1688 

1689 Parameters 

1690 ---------- 

1691 sensorRef : `daf.persistence.butlerSubset.ButlerDataRef` 

1692 DataRef of the detector data to be processed 

1693 

1694 Returns 

1695 ------- 

1696 result : `lsst.pipe.base.Struct` 

1697 Result struct with component: 

1698 - ``exposure`` : `afw.image.Exposure` 

1699 The fully ISR corrected exposure. 

1700 

1701 Raises 

1702 ------ 

1703 RuntimeError 

1704 Raised if a configuration option is set to True, but the 

1705 required calibration data does not exist. 

1706 

1707 """ 

1708 self.log.info("Performing ISR on sensor %s.", sensorRef.dataId) 

1709 

1710 ccdExposure = sensorRef.get(self.config.datasetType) 

1711 

1712 camera = sensorRef.get("camera") 

1713 isrData = self.readIsrData(sensorRef, ccdExposure) 

1714 

1715 result = self.run(ccdExposure, camera=camera, **isrData.getDict()) 

1716 

1717 if self.config.doWrite: 

1718 sensorRef.put(result.exposure, "postISRCCD") 

1719 if result.preInterpolatedExposure is not None: 

1720 sensorRef.put(result.preInterpolatedExposure, "postISRCCD_uninterpolated") 

1721 if result.ossThumb is not None: 

1722 isrQa.writeThumbnail(sensorRef, result.ossThumb, "ossThumb") 

1723 if result.flattenedThumb is not None: 

1724 isrQa.writeThumbnail(sensorRef, result.flattenedThumb, "flattenedThumb") 

1725 

1726 return result 

1727 

1728 def getIsrExposure(self, dataRef, datasetType, dateObs=None, immediate=True): 

1729 """Retrieve a calibration dataset for removing instrument signature. 

1730 

1731 Parameters 

1732 ---------- 

1733 

1734 dataRef : `daf.persistence.butlerSubset.ButlerDataRef` 

1735 DataRef of the detector data to find calibration datasets 

1736 for. 

1737 datasetType : `str` 

1738 Type of dataset to retrieve (e.g. 'bias', 'flat', etc). 

1739 dateObs : `str`, optional 

1740 Date of the observation. Used to correct butler failures 

1741 when using fallback filters. 

1742 immediate : `Bool` 

1743 If True, disable butler proxies to enable error handling 

1744 within this routine. 

1745 

1746 Returns 

1747 ------- 

1748 exposure : `lsst.afw.image.Exposure` 

1749 Requested calibration frame. 

1750 

1751 Raises 

1752 ------ 

1753 RuntimeError 

1754 Raised if no matching calibration frame can be found. 

1755 """ 

1756 try: 

1757 exp = dataRef.get(datasetType, immediate=immediate) 

1758 except Exception as exc1: 

1759 if not self.config.fallbackFilterName: 

1760 raise RuntimeError("Unable to retrieve %s for %s: %s." % (datasetType, dataRef.dataId, exc1)) 

1761 try: 

1762 if self.config.useFallbackDate and dateObs: 

1763 exp = dataRef.get(datasetType, filter=self.config.fallbackFilterName, 

1764 dateObs=dateObs, immediate=immediate) 

1765 else: 

1766 exp = dataRef.get(datasetType, filter=self.config.fallbackFilterName, immediate=immediate) 

1767 except Exception as exc2: 

1768 raise RuntimeError("Unable to retrieve %s for %s, even with fallback filter %s: %s AND %s." % 

1769 (datasetType, dataRef.dataId, self.config.fallbackFilterName, exc1, exc2)) 

1770 self.log.warn("Using fallback calibration from filter %s.", self.config.fallbackFilterName) 

1771 

1772 if self.config.doAssembleIsrExposures: 

1773 exp = self.assembleCcd.assembleCcd(exp) 

1774 return exp 

1775 

1776 def ensureExposure(self, inputExp, camera, detectorNum): 

1777 """Ensure that the data returned by Butler is a fully constructed exposure. 

1778 

1779 ISR requires exposure-level image data for historical reasons, so if we did 

1780 not recieve that from Butler, construct it from what we have, modifying the 

1781 input in place. 

1782 

1783 Parameters 

1784 ---------- 

1785 inputExp : `lsst.afw.image.Exposure`, `lsst.afw.image.DecoratedImageU`, or 

1786 `lsst.afw.image.ImageF` 

1787 The input data structure obtained from Butler. 

1788 camera : `lsst.afw.cameraGeom.camera` 

1789 The camera associated with the image. Used to find the appropriate 

1790 detector. 

1791 detectorNum : `int` 

1792 The detector this exposure should match. 

1793 

1794 Returns 

1795 ------- 

1796 inputExp : `lsst.afw.image.Exposure` 

1797 The re-constructed exposure, with appropriate detector parameters. 

1798 

1799 Raises 

1800 ------ 

1801 TypeError 

1802 Raised if the input data cannot be used to construct an exposure. 

1803 """ 

1804 if isinstance(inputExp, afwImage.DecoratedImageU): 

1805 inputExp = afwImage.makeExposure(afwImage.makeMaskedImage(inputExp)) 

1806 elif isinstance(inputExp, afwImage.ImageF): 

1807 inputExp = afwImage.makeExposure(afwImage.makeMaskedImage(inputExp)) 

1808 elif isinstance(inputExp, afwImage.MaskedImageF): 

1809 inputExp = afwImage.makeExposure(inputExp) 

1810 elif isinstance(inputExp, afwImage.Exposure): 

1811 pass 

1812 elif inputExp is None: 

1813 # Assume this will be caught by the setup if it is a problem. 

1814 return inputExp 

1815 else: 

1816 raise TypeError("Input Exposure is not known type in isrTask.ensureExposure: %s." % 

1817 (type(inputExp), )) 

1818 

1819 if inputExp.getDetector() is None: 

1820 inputExp.setDetector(camera[detectorNum]) 

1821 

1822 return inputExp 

1823 

1824 def convertIntToFloat(self, exposure): 

1825 """Convert exposure image from uint16 to float. 

1826 

1827 If the exposure does not need to be converted, the input is 

1828 immediately returned. For exposures that are converted to use 

1829 floating point pixels, the variance is set to unity and the 

1830 mask to zero. 

1831 

1832 Parameters 

1833 ---------- 

1834 exposure : `lsst.afw.image.Exposure` 

1835 The raw exposure to be converted. 

1836 

1837 Returns 

1838 ------- 

1839 newexposure : `lsst.afw.image.Exposure` 

1840 The input ``exposure``, converted to floating point pixels. 

1841 

1842 Raises 

1843 ------ 

1844 RuntimeError 

1845 Raised if the exposure type cannot be converted to float. 

1846 

1847 """ 

1848 if isinstance(exposure, afwImage.ExposureF): 

1849 # Nothing to be done 

1850 self.log.debug("Exposure already of type float.") 

1851 return exposure 

1852 if not hasattr(exposure, "convertF"): 

1853 raise RuntimeError("Unable to convert exposure (%s) to float." % type(exposure)) 

1854 

1855 newexposure = exposure.convertF() 

1856 newexposure.variance[:] = 1 

1857 newexposure.mask[:] = 0x0 

1858 

1859 return newexposure 

1860 

1861 def maskAmplifier(self, ccdExposure, amp, defects): 

1862 """Identify bad amplifiers, saturated and suspect pixels. 

1863 

1864 Parameters 

1865 ---------- 

1866 ccdExposure : `lsst.afw.image.Exposure` 

1867 Input exposure to be masked. 

1868 amp : `lsst.afw.table.AmpInfoCatalog` 

1869 Catalog of parameters defining the amplifier on this 

1870 exposure to mask. 

1871 defects : `lsst.ip.isr.Defects` 

1872 List of defects. Used to determine if the entire 

1873 amplifier is bad. 

1874 

1875 Returns 

1876 ------- 

1877 badAmp : `Bool` 

1878 If this is true, the entire amplifier area is covered by 

1879 defects and unusable. 

1880 

1881 """ 

1882 maskedImage = ccdExposure.getMaskedImage() 

1883 

1884 badAmp = False 

1885 

1886 # Check if entire amp region is defined as a defect (need to use amp.getBBox() for correct 

1887 # comparison with current defects definition. 

1888 if defects is not None: 

1889 badAmp = bool(sum([v.getBBox().contains(amp.getBBox()) for v in defects])) 

1890 

1891 # In the case of a bad amp, we will set mask to "BAD" (here use amp.getRawBBox() for correct 

1892 # association with pixels in current ccdExposure). 

1893 if badAmp: 

1894 dataView = afwImage.MaskedImageF(maskedImage, amp.getRawBBox(), 

1895 afwImage.PARENT) 

1896 maskView = dataView.getMask() 

1897 maskView |= maskView.getPlaneBitMask("BAD") 

1898 del maskView 

1899 return badAmp 

1900 

1901 # Mask remaining defects after assembleCcd() to allow for defects that cross amplifier boundaries. 

1902 # Saturation and suspect pixels can be masked now, though. 

1903 limits = dict() 

1904 if self.config.doSaturation and not badAmp: 

1905 limits.update({self.config.saturatedMaskName: amp.getSaturation()}) 

1906 if self.config.doSuspect and not badAmp: 

1907 limits.update({self.config.suspectMaskName: amp.getSuspectLevel()}) 

1908 if math.isfinite(self.config.saturation): 

1909 limits.update({self.config.saturatedMaskName: self.config.saturation}) 

1910 

1911 for maskName, maskThreshold in limits.items(): 

1912 if not math.isnan(maskThreshold): 

1913 dataView = maskedImage.Factory(maskedImage, amp.getRawBBox()) 

1914 isrFunctions.makeThresholdMask( 

1915 maskedImage=dataView, 

1916 threshold=maskThreshold, 

1917 growFootprints=0, 

1918 maskName=maskName 

1919 ) 

1920 

1921 # Determine if we've fully masked this amplifier with SUSPECT and SAT pixels. 

1922 maskView = afwImage.Mask(maskedImage.getMask(), amp.getRawDataBBox(), 

1923 afwImage.PARENT) 

1924 maskVal = maskView.getPlaneBitMask([self.config.saturatedMaskName, 

1925 self.config.suspectMaskName]) 

1926 if numpy.all(maskView.getArray() & maskVal > 0): 

1927 badAmp = True 

1928 maskView |= maskView.getPlaneBitMask("BAD") 

1929 

1930 return badAmp 

1931 

1932 def overscanCorrection(self, ccdExposure, amp): 

1933 """Apply overscan correction in place. 

1934 

1935 This method does initial pixel rejection of the overscan 

1936 region. The overscan can also be optionally segmented to 

1937 allow for discontinuous overscan responses to be fit 

1938 separately. The actual overscan subtraction is performed by 

1939 the `lsst.ip.isr.isrFunctions.overscanCorrection` function, 

1940 which is called here after the amplifier is preprocessed. 

1941 

1942 Parameters 

1943 ---------- 

1944 ccdExposure : `lsst.afw.image.Exposure` 

1945 Exposure to have overscan correction performed. 

1946 amp : `lsst.afw.cameraGeom.Amplifer` 

1947 The amplifier to consider while correcting the overscan. 

1948 

1949 Returns 

1950 ------- 

1951 overscanResults : `lsst.pipe.base.Struct` 

1952 Result struct with components: 

1953 - ``imageFit`` : scalar or `lsst.afw.image.Image` 

1954 Value or fit subtracted from the amplifier image data. 

1955 - ``overscanFit`` : scalar or `lsst.afw.image.Image` 

1956 Value or fit subtracted from the overscan image data. 

1957 - ``overscanImage`` : `lsst.afw.image.Image` 

1958 Image of the overscan region with the overscan 

1959 correction applied. This quantity is used to estimate 

1960 the amplifier read noise empirically. 

1961 

1962 Raises 

1963 ------ 

1964 RuntimeError 

1965 Raised if the ``amp`` does not contain raw pixel information. 

1966 

1967 See Also 

1968 -------- 

1969 lsst.ip.isr.isrFunctions.overscanCorrection 

1970 """ 

1971 if amp.getRawHorizontalOverscanBBox().isEmpty(): 

1972 self.log.info("ISR_OSCAN: No overscan region. Not performing overscan correction.") 

1973 return None 

1974 

1975 statControl = afwMath.StatisticsControl() 

1976 statControl.setAndMask(ccdExposure.mask.getPlaneBitMask("SAT")) 

1977 

1978 # Determine the bounding boxes 

1979 dataBBox = amp.getRawDataBBox() 

1980 oscanBBox = amp.getRawHorizontalOverscanBBox() 

1981 dx0 = 0 

1982 dx1 = 0 

1983 

1984 prescanBBox = amp.getRawPrescanBBox() 

1985 if (oscanBBox.getBeginX() > prescanBBox.getBeginX()): # amp is at the right 

1986 dx0 += self.config.overscanNumLeadingColumnsToSkip 

1987 dx1 -= self.config.overscanNumTrailingColumnsToSkip 

1988 else: 

1989 dx0 += self.config.overscanNumTrailingColumnsToSkip 

1990 dx1 -= self.config.overscanNumLeadingColumnsToSkip 

1991 

1992 # Determine if we need to work on subregions of the amplifier and overscan. 

1993 imageBBoxes = [] 

1994 overscanBBoxes = [] 

1995 

1996 if ((self.config.overscanBiasJump 

1997 and self.config.overscanBiasJumpLocation) 

1998 and (ccdExposure.getMetadata().exists(self.config.overscanBiasJumpKeyword) 

1999 and ccdExposure.getMetadata().getScalar(self.config.overscanBiasJumpKeyword) in 

2000 self.config.overscanBiasJumpDevices)): 

2001 if amp.getReadoutCorner() in (ReadoutCorner.LL, ReadoutCorner.LR): 

2002 yLower = self.config.overscanBiasJumpLocation 

2003 yUpper = dataBBox.getHeight() - yLower 

2004 else: 

2005 yUpper = self.config.overscanBiasJumpLocation 

2006 yLower = dataBBox.getHeight() - yUpper 

2007 

2008 imageBBoxes.append(lsst.geom.Box2I(dataBBox.getBegin(), 

2009 lsst.geom.Extent2I(dataBBox.getWidth(), yLower))) 

2010 overscanBBoxes.append(lsst.geom.Box2I(oscanBBox.getBegin() + lsst.geom.Extent2I(dx0, 0), 

2011 lsst.geom.Extent2I(oscanBBox.getWidth() - dx0 + dx1, 

2012 yLower))) 

2013 

2014 imageBBoxes.append(lsst.geom.Box2I(dataBBox.getBegin() + lsst.geom.Extent2I(0, yLower), 

2015 lsst.geom.Extent2I(dataBBox.getWidth(), yUpper))) 

2016 overscanBBoxes.append(lsst.geom.Box2I(oscanBBox.getBegin() + lsst.geom.Extent2I(dx0, yLower), 

2017 lsst.geom.Extent2I(oscanBBox.getWidth() - dx0 + dx1, 

2018 yUpper))) 

2019 else: 

2020 imageBBoxes.append(lsst.geom.Box2I(dataBBox.getBegin(), 

2021 lsst.geom.Extent2I(dataBBox.getWidth(), dataBBox.getHeight()))) 

2022 overscanBBoxes.append(lsst.geom.Box2I(oscanBBox.getBegin() + lsst.geom.Extent2I(dx0, 0), 

2023 lsst.geom.Extent2I(oscanBBox.getWidth() - dx0 + dx1, 

2024 oscanBBox.getHeight()))) 

2025 

2026 # Perform overscan correction on subregions, ensuring saturated pixels are masked. 

2027 for imageBBox, overscanBBox in zip(imageBBoxes, overscanBBoxes): 

2028 ampImage = ccdExposure.maskedImage[imageBBox] 

2029 overscanImage = ccdExposure.maskedImage[overscanBBox] 

2030 

2031 overscanArray = overscanImage.image.array 

2032 median = numpy.ma.median(numpy.ma.masked_where(overscanImage.mask.array, overscanArray)) 

2033 bad = numpy.where(numpy.abs(overscanArray - median) > self.config.overscanMaxDev) 

2034 overscanImage.mask.array[bad] = overscanImage.mask.getPlaneBitMask("SAT") 

2035 

2036 statControl = afwMath.StatisticsControl() 

2037 statControl.setAndMask(ccdExposure.mask.getPlaneBitMask("SAT")) 

2038 

2039 overscanResults = self.overscan.run(ampImage.getImage(), overscanImage, amp) 

2040 

2041 # Measure average overscan levels and record them in the metadata. 

2042 levelStat = afwMath.MEDIAN 

2043 sigmaStat = afwMath.STDEVCLIP 

2044 

2045 sctrl = afwMath.StatisticsControl(self.config.qa.flatness.clipSigma, 

2046 self.config.qa.flatness.nIter) 

2047 metadata = ccdExposure.getMetadata() 

2048 ampNum = amp.getName() 

2049 # if self.config.overscanFitType in ("MEDIAN", "MEAN", "MEANCLIP"): 

2050 if isinstance(overscanResults.overscanFit, float): 

2051 metadata.set("ISR_OSCAN_LEVEL%s" % ampNum, overscanResults.overscanFit) 

2052 metadata.set("ISR_OSCAN_SIGMA%s" % ampNum, 0.0) 

2053 else: 

2054 stats = afwMath.makeStatistics(overscanResults.overscanFit, levelStat | sigmaStat, sctrl) 

2055 metadata.set("ISR_OSCAN_LEVEL%s" % ampNum, stats.getValue(levelStat)) 

2056 metadata.set("ISR_OSCAN_SIGMA%s" % ampNum, stats.getValue(sigmaStat)) 

2057 

2058 return overscanResults 

2059 

2060 def updateVariance(self, ampExposure, amp, overscanImage=None): 

2061 """Set the variance plane using the amplifier gain and read noise 

2062 

2063 The read noise is calculated from the ``overscanImage`` if the 

2064 ``doEmpiricalReadNoise`` option is set in the configuration; otherwise 

2065 the value from the amplifier data is used. 

2066 

2067 Parameters 

2068 ---------- 

2069 ampExposure : `lsst.afw.image.Exposure` 

2070 Exposure to process. 

2071 amp : `lsst.afw.table.AmpInfoRecord` or `FakeAmp` 

2072 Amplifier detector data. 

2073 overscanImage : `lsst.afw.image.MaskedImage`, optional. 

2074 Image of overscan, required only for empirical read noise. 

2075 

2076 See also 

2077 -------- 

2078 lsst.ip.isr.isrFunctions.updateVariance 

2079 """ 

2080 maskPlanes = [self.config.saturatedMaskName, self.config.suspectMaskName] 

2081 gain = amp.getGain() 

2082 

2083 if math.isnan(gain): 

2084 gain = 1.0 

2085 self.log.warn("Gain set to NAN! Updating to 1.0 to generate Poisson variance.") 

2086 elif gain <= 0: 

2087 patchedGain = 1.0 

2088 self.log.warn("Gain for amp %s == %g <= 0; setting to %f.", 

2089 amp.getName(), gain, patchedGain) 

2090 gain = patchedGain 

2091 

2092 if self.config.doEmpiricalReadNoise and overscanImage is None: 

2093 self.log.info("Overscan is none for EmpiricalReadNoise.") 

2094 

2095 if self.config.doEmpiricalReadNoise and overscanImage is not None: 

2096 stats = afwMath.StatisticsControl() 

2097 stats.setAndMask(overscanImage.mask.getPlaneBitMask(maskPlanes)) 

2098 readNoise = afwMath.makeStatistics(overscanImage, afwMath.STDEVCLIP, stats).getValue() 

2099 self.log.info("Calculated empirical read noise for amp %s: %f.", 

2100 amp.getName(), readNoise) 

2101 else: 

2102 readNoise = amp.getReadNoise() 

2103 

2104 isrFunctions.updateVariance( 

2105 maskedImage=ampExposure.getMaskedImage(), 

2106 gain=gain, 

2107 readNoise=readNoise, 

2108 ) 

2109 

2110 def darkCorrection(self, exposure, darkExposure, invert=False): 

2111 """Apply dark correction in place. 

2112 

2113 Parameters 

2114 ---------- 

2115 exposure : `lsst.afw.image.Exposure` 

2116 Exposure to process. 

2117 darkExposure : `lsst.afw.image.Exposure` 

2118 Dark exposure of the same size as ``exposure``. 

2119 invert : `Bool`, optional 

2120 If True, re-add the dark to an already corrected image. 

2121 

2122 Raises 

2123 ------ 

2124 RuntimeError 

2125 Raised if either ``exposure`` or ``darkExposure`` do not 

2126 have their dark time defined. 

2127 

2128 See Also 

2129 -------- 

2130 lsst.ip.isr.isrFunctions.darkCorrection 

2131 """ 

2132 expScale = exposure.getInfo().getVisitInfo().getDarkTime() 

2133 if math.isnan(expScale): 

2134 raise RuntimeError("Exposure darktime is NAN.") 

2135 if darkExposure.getInfo().getVisitInfo() is not None \ 

2136 and not math.isnan(darkExposure.getInfo().getVisitInfo().getDarkTime()): 

2137 darkScale = darkExposure.getInfo().getVisitInfo().getDarkTime() 

2138 else: 

2139 # DM-17444: darkExposure.getInfo.getVisitInfo() is None 

2140 # so getDarkTime() does not exist. 

2141 self.log.warn("darkExposure.getInfo().getVisitInfo() does not exist. Using darkScale = 1.0.") 

2142 darkScale = 1.0 

2143 

2144 isrFunctions.darkCorrection( 

2145 maskedImage=exposure.getMaskedImage(), 

2146 darkMaskedImage=darkExposure.getMaskedImage(), 

2147 expScale=expScale, 

2148 darkScale=darkScale, 

2149 invert=invert, 

2150 trimToFit=self.config.doTrimToMatchCalib 

2151 ) 

2152 

2153 def doLinearize(self, detector): 

2154 """Check if linearization is needed for the detector cameraGeom. 

2155 

2156 Checks config.doLinearize and the linearity type of the first 

2157 amplifier. 

2158 

2159 Parameters 

2160 ---------- 

2161 detector : `lsst.afw.cameraGeom.Detector` 

2162 Detector to get linearity type from. 

2163 

2164 Returns 

2165 ------- 

2166 doLinearize : `Bool` 

2167 If True, linearization should be performed. 

2168 """ 

2169 return self.config.doLinearize and \ 

2170 detector.getAmplifiers()[0].getLinearityType() != NullLinearityType 

2171 

2172 def flatCorrection(self, exposure, flatExposure, invert=False): 

2173 """Apply flat correction in place. 

2174 

2175 Parameters 

2176 ---------- 

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

2178 Exposure to process. 

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

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

2181 invert : `Bool`, optional 

2182 If True, unflatten an already flattened image. 

2183 

2184 See Also 

2185 -------- 

2186 lsst.ip.isr.isrFunctions.flatCorrection 

2187 """ 

2188 isrFunctions.flatCorrection( 

2189 maskedImage=exposure.getMaskedImage(), 

2190 flatMaskedImage=flatExposure.getMaskedImage(), 

2191 scalingType=self.config.flatScalingType, 

2192 userScale=self.config.flatUserScale, 

2193 invert=invert, 

2194 trimToFit=self.config.doTrimToMatchCalib 

2195 ) 

2196 

2197 def saturationDetection(self, exposure, amp): 

2198 """Detect saturated pixels and mask them using mask plane config.saturatedMaskName, in place. 

2199 

2200 Parameters 

2201 ---------- 

2202 exposure : `lsst.afw.image.Exposure` 

2203 Exposure to process. Only the amplifier DataSec is processed. 

2204 amp : `lsst.afw.table.AmpInfoCatalog` 

2205 Amplifier detector data. 

2206 

2207 See Also 

2208 -------- 

2209 lsst.ip.isr.isrFunctions.makeThresholdMask 

2210 """ 

2211 if not math.isnan(amp.getSaturation()): 

2212 maskedImage = exposure.getMaskedImage() 

2213 dataView = maskedImage.Factory(maskedImage, amp.getRawBBox()) 

2214 isrFunctions.makeThresholdMask( 

2215 maskedImage=dataView, 

2216 threshold=amp.getSaturation(), 

2217 growFootprints=0, 

2218 maskName=self.config.saturatedMaskName, 

2219 ) 

2220 

2221 def saturationInterpolation(self, exposure): 

2222 """Interpolate over saturated pixels, in place. 

2223 

2224 This method should be called after `saturationDetection`, to 

2225 ensure that the saturated pixels have been identified in the 

2226 SAT mask. It should also be called after `assembleCcd`, since 

2227 saturated regions may cross amplifier boundaries. 

2228 

2229 Parameters 

2230 ---------- 

2231 exposure : `lsst.afw.image.Exposure` 

2232 Exposure to process. 

2233 

2234 See Also 

2235 -------- 

2236 lsst.ip.isr.isrTask.saturationDetection 

2237 lsst.ip.isr.isrFunctions.interpolateFromMask 

2238 """ 

2239 isrFunctions.interpolateFromMask( 

2240 maskedImage=exposure.getMaskedImage(), 

2241 fwhm=self.config.fwhm, 

2242 growSaturatedFootprints=self.config.growSaturationFootprintSize, 

2243 maskNameList=list(self.config.saturatedMaskName), 

2244 ) 

2245 

2246 def suspectDetection(self, exposure, amp): 

2247 """Detect suspect pixels and mask them using mask plane config.suspectMaskName, in place. 

2248 

2249 Parameters 

2250 ---------- 

2251 exposure : `lsst.afw.image.Exposure` 

2252 Exposure to process. Only the amplifier DataSec is processed. 

2253 amp : `lsst.afw.table.AmpInfoCatalog` 

2254 Amplifier detector data. 

2255 

2256 See Also 

2257 -------- 

2258 lsst.ip.isr.isrFunctions.makeThresholdMask 

2259 

2260 Notes 

2261 ----- 

2262 Suspect pixels are pixels whose value is greater than amp.getSuspectLevel(). 

2263 This is intended to indicate pixels that may be affected by unknown systematics; 

2264 for example if non-linearity corrections above a certain level are unstable 

2265 then that would be a useful value for suspectLevel. A value of `nan` indicates 

2266 that no such level exists and no pixels are to be masked as suspicious. 

2267 """ 

2268 suspectLevel = amp.getSuspectLevel() 

2269 if math.isnan(suspectLevel): 

2270 return 

2271 

2272 maskedImage = exposure.getMaskedImage() 

2273 dataView = maskedImage.Factory(maskedImage, amp.getRawBBox()) 

2274 isrFunctions.makeThresholdMask( 

2275 maskedImage=dataView, 

2276 threshold=suspectLevel, 

2277 growFootprints=0, 

2278 maskName=self.config.suspectMaskName, 

2279 ) 

2280 

2281 def maskDefect(self, exposure, defectBaseList): 

2282 """Mask defects using mask plane "BAD", in place. 

2283 

2284 Parameters 

2285 ---------- 

2286 exposure : `lsst.afw.image.Exposure` 

2287 Exposure to process. 

2288 defectBaseList : `lsst.ip.isr.Defects` or `list` of 

2289 `lsst.afw.image.DefectBase`. 

2290 List of defects to mask. 

2291 

2292 Notes 

2293 ----- 

2294 Call this after CCD assembly, since defects may cross amplifier boundaries. 

2295 """ 

2296 maskedImage = exposure.getMaskedImage() 

2297 if not isinstance(defectBaseList, Defects): 

2298 # Promotes DefectBase to Defect 

2299 defectList = Defects(defectBaseList) 

2300 else: 

2301 defectList = defectBaseList 

2302 defectList.maskPixels(maskedImage, maskName="BAD") 

2303 

2304 def maskEdges(self, exposure, numEdgePixels=0, maskPlane="SUSPECT", level='DETECTOR'): 

2305 """Mask edge pixels with applicable mask plane. 

2306 

2307 Parameters 

2308 ---------- 

2309 exposure : `lsst.afw.image.Exposure` 

2310 Exposure to process. 

2311 numEdgePixels : `int`, optional 

2312 Number of edge pixels to mask. 

2313 maskPlane : `str`, optional 

2314 Mask plane name to use. 

2315 level : `str`, optional 

2316 Level at which to mask edges. 

2317 """ 

2318 maskedImage = exposure.getMaskedImage() 

2319 maskBitMask = maskedImage.getMask().getPlaneBitMask(maskPlane) 

2320 

2321 if numEdgePixels > 0: 

2322 if level == 'DETECTOR': 

2323 boxes = [maskedImage.getBBox()] 

2324 elif level == 'AMP': 

2325 boxes = [amp.getBBox() for amp in exposure.getDetector()] 

2326 

2327 for box in boxes: 

2328 # This makes a bbox numEdgeSuspect pixels smaller than the image on each side 

2329 subImage = maskedImage[box] 

2330 box.grow(-numEdgePixels) 

2331 # Mask pixels outside box 

2332 SourceDetectionTask.setEdgeBits( 

2333 subImage, 

2334 box, 

2335 maskBitMask) 

2336 

2337 def maskAndInterpolateDefects(self, exposure, defectBaseList): 

2338 """Mask and interpolate defects using mask plane "BAD", in place. 

2339 

2340 Parameters 

2341 ---------- 

2342 exposure : `lsst.afw.image.Exposure` 

2343 Exposure to process. 

2344 defectBaseList : `lsst.ip.isr.Defects` or `list` of 

2345 `lsst.afw.image.DefectBase`. 

2346 List of defects to mask and interpolate. 

2347 

2348 See Also 

2349 -------- 

2350 lsst.ip.isr.isrTask.maskDefect 

2351 """ 

2352 self.maskDefect(exposure, defectBaseList) 

2353 self.maskEdges(exposure, numEdgePixels=self.config.numEdgeSuspect, 

2354 maskPlane="SUSPECT", level=self.config.edgeMaskLevel) 

2355 isrFunctions.interpolateFromMask( 

2356 maskedImage=exposure.getMaskedImage(), 

2357 fwhm=self.config.fwhm, 

2358 growSaturatedFootprints=0, 

2359 maskNameList=["BAD"], 

2360 ) 

2361 

2362 def maskNan(self, exposure): 

2363 """Mask NaNs using mask plane "UNMASKEDNAN", in place. 

2364 

2365 Parameters 

2366 ---------- 

2367 exposure : `lsst.afw.image.Exposure` 

2368 Exposure to process. 

2369 

2370 Notes 

2371 ----- 

2372 We mask over all NaNs, including those that are masked with 

2373 other bits (because those may or may not be interpolated over 

2374 later, and we want to remove all NaNs). Despite this 

2375 behaviour, the "UNMASKEDNAN" mask plane is used to preserve 

2376 the historical name. 

2377 """ 

2378 maskedImage = exposure.getMaskedImage() 

2379 

2380 # Find and mask NaNs 

2381 maskedImage.getMask().addMaskPlane("UNMASKEDNAN") 

2382 maskVal = maskedImage.getMask().getPlaneBitMask("UNMASKEDNAN") 

2383 numNans = maskNans(maskedImage, maskVal) 

2384 self.metadata.set("NUMNANS", numNans) 

2385 if numNans > 0: 

2386 self.log.warn("There were %d unmasked NaNs.", numNans) 

2387 

2388 def maskAndInterpolateNan(self, exposure): 

2389 """"Mask and interpolate NaNs using mask plane "UNMASKEDNAN", in place. 

2390 

2391 Parameters 

2392 ---------- 

2393 exposure : `lsst.afw.image.Exposure` 

2394 Exposure to process. 

2395 

2396 See Also 

2397 -------- 

2398 lsst.ip.isr.isrTask.maskNan 

2399 """ 

2400 self.maskNan(exposure) 

2401 isrFunctions.interpolateFromMask( 

2402 maskedImage=exposure.getMaskedImage(), 

2403 fwhm=self.config.fwhm, 

2404 growSaturatedFootprints=0, 

2405 maskNameList=["UNMASKEDNAN"], 

2406 ) 

2407 

2408 def measureBackground(self, exposure, IsrQaConfig=None): 

2409 """Measure the image background in subgrids, for quality control purposes. 

2410 

2411 Parameters 

2412 ---------- 

2413 exposure : `lsst.afw.image.Exposure` 

2414 Exposure to process. 

2415 IsrQaConfig : `lsst.ip.isr.isrQa.IsrQaConfig` 

2416 Configuration object containing parameters on which background 

2417 statistics and subgrids to use. 

2418 """ 

2419 if IsrQaConfig is not None: 

2420 statsControl = afwMath.StatisticsControl(IsrQaConfig.flatness.clipSigma, 

2421 IsrQaConfig.flatness.nIter) 

2422 maskVal = exposure.getMaskedImage().getMask().getPlaneBitMask(["BAD", "SAT", "DETECTED"]) 

2423 statsControl.setAndMask(maskVal) 

2424 maskedImage = exposure.getMaskedImage() 

2425 stats = afwMath.makeStatistics(maskedImage, afwMath.MEDIAN | afwMath.STDEVCLIP, statsControl) 

2426 skyLevel = stats.getValue(afwMath.MEDIAN) 

2427 skySigma = stats.getValue(afwMath.STDEVCLIP) 

2428 self.log.info("Flattened sky level: %f +/- %f.", skyLevel, skySigma) 

2429 metadata = exposure.getMetadata() 

2430 metadata.set('SKYLEVEL', skyLevel) 

2431 metadata.set('SKYSIGMA', skySigma) 

2432 

2433 # calcluating flatlevel over the subgrids 

2434 stat = afwMath.MEANCLIP if IsrQaConfig.flatness.doClip else afwMath.MEAN 

2435 meshXHalf = int(IsrQaConfig.flatness.meshX/2.) 

2436 meshYHalf = int(IsrQaConfig.flatness.meshY/2.) 

2437 nX = int((exposure.getWidth() + meshXHalf) / IsrQaConfig.flatness.meshX) 

2438 nY = int((exposure.getHeight() + meshYHalf) / IsrQaConfig.flatness.meshY) 

2439 skyLevels = numpy.zeros((nX, nY)) 

2440 

2441 for j in range(nY): 

2442 yc = meshYHalf + j * IsrQaConfig.flatness.meshY 

2443 for i in range(nX): 

2444 xc = meshXHalf + i * IsrQaConfig.flatness.meshX 

2445 

2446 xLLC = xc - meshXHalf 

2447 yLLC = yc - meshYHalf 

2448 xURC = xc + meshXHalf - 1 

2449 yURC = yc + meshYHalf - 1 

2450 

2451 bbox = lsst.geom.Box2I(lsst.geom.Point2I(xLLC, yLLC), lsst.geom.Point2I(xURC, yURC)) 

2452 miMesh = maskedImage.Factory(exposure.getMaskedImage(), bbox, afwImage.LOCAL) 

2453 

2454 skyLevels[i, j] = afwMath.makeStatistics(miMesh, stat, statsControl).getValue() 

2455 

2456 good = numpy.where(numpy.isfinite(skyLevels)) 

2457 skyMedian = numpy.median(skyLevels[good]) 

2458 flatness = (skyLevels[good] - skyMedian) / skyMedian 

2459 flatness_rms = numpy.std(flatness) 

2460 flatness_pp = flatness.max() - flatness.min() if len(flatness) > 0 else numpy.nan 

2461 

2462 self.log.info("Measuring sky levels in %dx%d grids: %f.", nX, nY, skyMedian) 

2463 self.log.info("Sky flatness in %dx%d grids - pp: %f rms: %f.", 

2464 nX, nY, flatness_pp, flatness_rms) 

2465 

2466 metadata.set('FLATNESS_PP', float(flatness_pp)) 

2467 metadata.set('FLATNESS_RMS', float(flatness_rms)) 

2468 metadata.set('FLATNESS_NGRIDS', '%dx%d' % (nX, nY)) 

2469 metadata.set('FLATNESS_MESHX', IsrQaConfig.flatness.meshX) 

2470 metadata.set('FLATNESS_MESHY', IsrQaConfig.flatness.meshY) 

2471 

2472 def roughZeroPoint(self, exposure): 

2473 """Set an approximate magnitude zero point for the exposure. 

2474 

2475 Parameters 

2476 ---------- 

2477 exposure : `lsst.afw.image.Exposure` 

2478 Exposure to process. 

2479 """ 

2480 filterLabel = exposure.getFilterLabel() 

2481 if filterLabel in self.config.fluxMag0T1: 

2482 fluxMag0 = self.config.fluxMag0T1[filterLabel] 

2483 else: 

2484 self.log.warn("No rough magnitude zero point set for filter %s.", filterLabel) 

2485 fluxMag0 = self.config.defaultFluxMag0T1 

2486 

2487 expTime = exposure.getInfo().getVisitInfo().getExposureTime() 

2488 if not expTime > 0: # handle NaN as well as <= 0 

2489 self.log.warn("Non-positive exposure time; skipping rough zero point.") 

2490 return 

2491 

2492 self.log.info("Setting rough magnitude zero point: %f", 2.5*math.log10(fluxMag0*expTime)) 

2493 exposure.setPhotoCalib(afwImage.makePhotoCalibFromCalibZeroPoint(fluxMag0*expTime, 0.0)) 

2494 

2495 def setValidPolygonIntersect(self, ccdExposure, fpPolygon): 

2496 """Set the valid polygon as the intersection of fpPolygon and the ccd corners. 

2497 

2498 Parameters 

2499 ---------- 

2500 ccdExposure : `lsst.afw.image.Exposure` 

2501 Exposure to process. 

2502 fpPolygon : `lsst.afw.geom.Polygon` 

2503 Polygon in focal plane coordinates. 

2504 """ 

2505 # Get ccd corners in focal plane coordinates 

2506 ccd = ccdExposure.getDetector() 

2507 fpCorners = ccd.getCorners(FOCAL_PLANE) 

2508 ccdPolygon = Polygon(fpCorners) 

2509 

2510 # Get intersection of ccd corners with fpPolygon 

2511 intersect = ccdPolygon.intersectionSingle(fpPolygon) 

2512 

2513 # Transform back to pixel positions and build new polygon 

2514 ccdPoints = ccd.transform(intersect, FOCAL_PLANE, PIXELS) 

2515 validPolygon = Polygon(ccdPoints) 

2516 ccdExposure.getInfo().setValidPolygon(validPolygon) 

2517 

2518 @contextmanager 

2519 def flatContext(self, exp, flat, dark=None): 

2520 """Context manager that applies and removes flats and darks, 

2521 if the task is configured to apply them. 

2522 

2523 Parameters 

2524 ---------- 

2525 exp : `lsst.afw.image.Exposure` 

2526 Exposure to process. 

2527 flat : `lsst.afw.image.Exposure` 

2528 Flat exposure the same size as ``exp``. 

2529 dark : `lsst.afw.image.Exposure`, optional 

2530 Dark exposure the same size as ``exp``. 

2531 

2532 Yields 

2533 ------ 

2534 exp : `lsst.afw.image.Exposure` 

2535 The flat and dark corrected exposure. 

2536 """ 

2537 if self.config.doDark and dark is not None: 

2538 self.darkCorrection(exp, dark) 

2539 if self.config.doFlat: 

2540 self.flatCorrection(exp, flat) 

2541 try: 

2542 yield exp 

2543 finally: 

2544 if self.config.doFlat: 

2545 self.flatCorrection(exp, flat, invert=True) 

2546 if self.config.doDark and dark is not None: 

2547 self.darkCorrection(exp, dark, invert=True) 

2548 

2549 def debugView(self, exposure, stepname): 

2550 """Utility function to examine ISR exposure at different stages. 

2551 

2552 Parameters 

2553 ---------- 

2554 exposure : `lsst.afw.image.Exposure` 

2555 Exposure to view. 

2556 stepname : `str` 

2557 State of processing to view. 

2558 """ 

2559 frame = getDebugFrame(self._display, stepname) 

2560 if frame: 

2561 display = getDisplay(frame) 

2562 display.scale('asinh', 'zscale') 

2563 display.mtv(exposure) 

2564 prompt = "Press Enter to continue [c]... " 

2565 while True: 

2566 ans = input(prompt).lower() 

2567 if ans in ("", "c",): 

2568 break 

2569 

2570 

2571class FakeAmp(object): 

2572 """A Detector-like object that supports returning gain and saturation level 

2573 

2574 This is used when the input exposure does not have a detector. 

2575 

2576 Parameters 

2577 ---------- 

2578 exposure : `lsst.afw.image.Exposure` 

2579 Exposure to generate a fake amplifier for. 

2580 config : `lsst.ip.isr.isrTaskConfig` 

2581 Configuration to apply to the fake amplifier. 

2582 """ 

2583 

2584 def __init__(self, exposure, config): 

2585 self._bbox = exposure.getBBox(afwImage.LOCAL) 

2586 self._RawHorizontalOverscanBBox = lsst.geom.Box2I() 

2587 self._gain = config.gain 

2588 self._readNoise = config.readNoise 

2589 self._saturation = config.saturation 

2590 

2591 def getBBox(self): 

2592 return self._bbox 

2593 

2594 def getRawBBox(self): 

2595 return self._bbox 

2596 

2597 def getRawHorizontalOverscanBBox(self): 

2598 return self._RawHorizontalOverscanBBox 

2599 

2600 def getGain(self): 

2601 return self._gain 

2602 

2603 def getReadNoise(self): 

2604 return self._readNoise 

2605 

2606 def getSaturation(self): 

2607 return self._saturation 

2608 

2609 def getSuspectLevel(self): 

2610 return float("NaN") 

2611 

2612 

2613class RunIsrConfig(pexConfig.Config): 

2614 isr = pexConfig.ConfigurableField(target=IsrTask, doc="Instrument signature removal") 

2615 

2616 

2617class RunIsrTask(pipeBase.CmdLineTask): 

2618 """Task to wrap the default IsrTask to allow it to be retargeted. 

2619 

2620 The standard IsrTask can be called directly from a command line 

2621 program, but doing so removes the ability of the task to be 

2622 retargeted. As most cameras override some set of the IsrTask 

2623 methods, this would remove those data-specific methods in the 

2624 output post-ISR images. This wrapping class fixes the issue, 

2625 allowing identical post-ISR images to be generated by both the 

2626 processCcd and isrTask code. 

2627 """ 

2628 ConfigClass = RunIsrConfig 

2629 _DefaultName = "runIsr" 

2630 

2631 def __init__(self, *args, **kwargs): 

2632 super().__init__(*args, **kwargs) 

2633 self.makeSubtask("isr") 

2634 

2635 def runDataRef(self, dataRef): 

2636 """ 

2637 Parameters 

2638 ---------- 

2639 dataRef : `lsst.daf.persistence.ButlerDataRef` 

2640 data reference of the detector data to be processed 

2641 

2642 Returns 

2643 ------- 

2644 result : `pipeBase.Struct` 

2645 Result struct with component: 

2646 

2647 - exposure : `lsst.afw.image.Exposure` 

2648 Post-ISR processed exposure. 

2649 """ 

2650 return self.isr.runDataRef(dataRef)