lsst.ip.isr g5923ed5121+fb9e0e4ff8
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isrTask.py
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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
22__all__ = ["IsrTask", "IsrTaskConfig"]
23
24import math
25import numpy
26
27import lsst.geom
28import lsst.afw.image as afwImage
29import lsst.afw.math as afwMath
30import lsst.pex.config as pexConfig
31import lsst.pipe.base as pipeBase
32import lsst.pipe.base.connectionTypes as cT
33
34from contextlib import contextmanager
35from lsstDebug import getDebugFrame
36
37from lsst.afw.cameraGeom import NullLinearityType
38from lsst.afw.display import getDisplay
39from lsst.meas.algorithms.detection import SourceDetectionTask
40from lsst.utils.timer import timeMethod
41
42from . import isrFunctions
43from . import isrQa
44from . import linearize
45from .defects import Defects
46
47from .assembleCcdTask import AssembleCcdTask
48from .crosstalk import CrosstalkTask, CrosstalkCalib
49from .fringe import FringeTask
50from .isr import maskNans
51from .masking import MaskingTask
52from .overscan import OverscanCorrectionTask
53from .straylight import StrayLightTask
54from .vignette import VignetteTask
55from .ampOffset import AmpOffsetTask
56from .deferredCharge import DeferredChargeTask
57from .isrStatistics import IsrStatisticsTask
58from .ptcDataset import PhotonTransferCurveDataset
59
60
61def crosstalkSourceLookup(datasetType, registry, quantumDataId, collections):
62 """Lookup function to identify crosstalkSource entries.
63
64 This should return an empty list under most circumstances. Only
65 when inter-chip crosstalk has been identified should this be
66 populated.
67
68 Parameters
69 ----------
70 datasetType : `str`
71 Dataset to lookup.
72 registry : `lsst.daf.butler.Registry`
73 Butler registry to query.
74 quantumDataId : `lsst.daf.butler.DataCoordinate`
75 Expanded data id to transform to identify crosstalkSources. The
76 ``detector`` entry will be stripped.
77 collections : `lsst.daf.butler.CollectionSearch`
78 Collections to search through.
79
80 Returns
81 -------
82 results : `list` [`lsst.daf.butler.DatasetRef`]
83 List of datasets that match the query that will be used as
84 crosstalkSources.
85 """
86 newDataId = quantumDataId.subset(registry.dimensions.conform(["instrument", "exposure"]))
87 results = set(registry.queryDatasets(datasetType, collections=collections, dataId=newDataId,
88 findFirst=True))
89 # In some contexts, calling `.expanded()` to expand all data IDs in the
90 # query results can be a lot faster because it vectorizes lookups. But in
91 # this case, expandDataId shouldn't need to hit the database at all in the
92 # steady state, because only the detector record is unknown and those are
93 # cached in the registry.
94 records = {k: newDataId.records[k] for k in newDataId.dimensions.elements}
95 return [ref.expanded(registry.expandDataId(ref.dataId, records=records)) for ref in results]
96
97
98class IsrTaskConnections(pipeBase.PipelineTaskConnections,
99 dimensions={"instrument", "exposure", "detector"},
100 defaultTemplates={}):
101 ccdExposure = cT.Input(
102 name="raw",
103 doc="Input exposure to process.",
104 storageClass="Exposure",
105 dimensions=["instrument", "exposure", "detector"],
106 )
107 camera = cT.PrerequisiteInput(
108 name="camera",
109 storageClass="Camera",
110 doc="Input camera to construct complete exposures.",
111 dimensions=["instrument"],
112 isCalibration=True,
113 )
114
115 crosstalk = cT.PrerequisiteInput(
116 name="crosstalk",
117 doc="Input crosstalk object",
118 storageClass="CrosstalkCalib",
119 dimensions=["instrument", "detector"],
120 isCalibration=True,
121 minimum=0, # can fall back to cameraGeom
122 )
123 crosstalkSources = cT.PrerequisiteInput(
124 name="isrOverscanCorrected",
125 doc="Overscan corrected input images.",
126 storageClass="Exposure",
127 dimensions=["instrument", "exposure", "detector"],
128 deferLoad=True,
129 multiple=True,
130 lookupFunction=crosstalkSourceLookup,
131 minimum=0, # not needed for all instruments, no config to control this
132 )
133 bias = cT.PrerequisiteInput(
134 name="bias",
135 doc="Input bias calibration.",
136 storageClass="ExposureF",
137 dimensions=["instrument", "detector"],
138 isCalibration=True,
139 )
140 dark = cT.PrerequisiteInput(
141 name='dark',
142 doc="Input dark calibration.",
143 storageClass="ExposureF",
144 dimensions=["instrument", "detector"],
145 isCalibration=True,
146 )
147 flat = cT.PrerequisiteInput(
148 name="flat",
149 doc="Input flat calibration.",
150 storageClass="ExposureF",
151 dimensions=["instrument", "physical_filter", "detector"],
152 isCalibration=True,
153 )
154 ptc = cT.PrerequisiteInput(
155 name="ptc",
156 doc="Input Photon Transfer Curve dataset",
157 storageClass="PhotonTransferCurveDataset",
158 dimensions=["instrument", "detector"],
159 isCalibration=True,
160 )
161 fringes = cT.PrerequisiteInput(
162 name="fringe",
163 doc="Input fringe calibration.",
164 storageClass="ExposureF",
165 dimensions=["instrument", "physical_filter", "detector"],
166 isCalibration=True,
167 minimum=0, # only needed for some bands, even when enabled
168 )
169 strayLightData = cT.PrerequisiteInput(
170 name='yBackground',
171 doc="Input stray light calibration.",
172 storageClass="StrayLightData",
173 dimensions=["instrument", "physical_filter", "detector"],
174 deferLoad=True,
175 isCalibration=True,
176 minimum=0, # only needed for some bands, even when enabled
177 )
178 bfKernel = cT.PrerequisiteInput(
179 name='bfKernel',
180 doc="Input brighter-fatter kernel.",
181 storageClass="NumpyArray",
182 dimensions=["instrument"],
183 isCalibration=True,
184 minimum=0, # can use either bfKernel or newBFKernel
185 )
186 newBFKernel = cT.PrerequisiteInput(
187 name='brighterFatterKernel',
188 doc="Newer complete kernel + gain solutions.",
189 storageClass="BrighterFatterKernel",
190 dimensions=["instrument", "detector"],
191 isCalibration=True,
192 minimum=0, # can use either bfKernel or newBFKernel
193 )
194 defects = cT.PrerequisiteInput(
195 name='defects',
196 doc="Input defect tables.",
197 storageClass="Defects",
198 dimensions=["instrument", "detector"],
199 isCalibration=True,
200 )
201 linearizer = cT.PrerequisiteInput(
202 name='linearizer',
203 storageClass="Linearizer",
204 doc="Linearity correction calibration.",
205 dimensions=["instrument", "detector"],
206 isCalibration=True,
207 minimum=0, # can fall back to cameraGeom
208 )
209 opticsTransmission = cT.PrerequisiteInput(
210 name="transmission_optics",
211 storageClass="TransmissionCurve",
212 doc="Transmission curve due to the optics.",
213 dimensions=["instrument"],
214 isCalibration=True,
215 )
216 filterTransmission = cT.PrerequisiteInput(
217 name="transmission_filter",
218 storageClass="TransmissionCurve",
219 doc="Transmission curve due to the filter.",
220 dimensions=["instrument", "physical_filter"],
221 isCalibration=True,
222 )
223 sensorTransmission = cT.PrerequisiteInput(
224 name="transmission_sensor",
225 storageClass="TransmissionCurve",
226 doc="Transmission curve due to the sensor.",
227 dimensions=["instrument", "detector"],
228 isCalibration=True,
229 )
230 atmosphereTransmission = cT.PrerequisiteInput(
231 name="transmission_atmosphere",
232 storageClass="TransmissionCurve",
233 doc="Transmission curve due to the atmosphere.",
234 dimensions=["instrument"],
235 isCalibration=True,
236 )
237 illumMaskedImage = cT.PrerequisiteInput(
238 name="illum",
239 doc="Input illumination correction.",
240 storageClass="MaskedImageF",
241 dimensions=["instrument", "physical_filter", "detector"],
242 isCalibration=True,
243 )
244 deferredChargeCalib = cT.PrerequisiteInput(
245 name="cpCtiCalib",
246 doc="Deferred charge/CTI correction dataset.",
247 storageClass="IsrCalib",
248 dimensions=["instrument", "detector"],
249 isCalibration=True,
250 )
251
252 outputExposure = cT.Output(
253 name='postISRCCD',
254 doc="Output ISR processed exposure.",
255 storageClass="Exposure",
256 dimensions=["instrument", "exposure", "detector"],
257 )
258 preInterpExposure = cT.Output(
259 name='preInterpISRCCD',
260 doc="Output ISR processed exposure, with pixels left uninterpolated.",
261 storageClass="ExposureF",
262 dimensions=["instrument", "exposure", "detector"],
263 )
264 outputBin1Exposure = cT.Output(
265 name="postIsrBin1",
266 doc="First binned image.",
267 storageClass="ExposureF",
268 dimensions=["instrument", "exposure", "detector"],
269 )
270 outputBin2Exposure = cT.Output(
271 name="postIsrBin2",
272 doc="Second binned image.",
273 storageClass="ExposureF",
274 dimensions=["instrument", "exposure", "detector"],
275 )
276
277 outputOssThumbnail = cT.Output(
278 name="OssThumb",
279 doc="Output Overscan-subtracted thumbnail image.",
280 storageClass="Thumbnail",
281 dimensions=["instrument", "exposure", "detector"],
282 )
283 outputFlattenedThumbnail = cT.Output(
284 name="FlattenedThumb",
285 doc="Output flat-corrected thumbnail image.",
286 storageClass="Thumbnail",
287 dimensions=["instrument", "exposure", "detector"],
288 )
289 outputStatistics = cT.Output(
290 name="isrStatistics",
291 doc="Output of additional statistics table.",
292 storageClass="StructuredDataDict",
293 dimensions=["instrument", "exposure", "detector"],
294 )
295
296 def __init__(self, *, config=None):
297 super().__init__(config=config)
298
299 if config.doBias is not True:
300 self.prerequisiteInputs.remove("bias")
301 if config.doLinearize is not True:
302 self.prerequisiteInputs.remove("linearizer")
303 if config.doCrosstalk is not True:
304 self.prerequisiteInputs.remove("crosstalkSources")
305 self.prerequisiteInputs.remove("crosstalk")
306 if config.doBrighterFatter is not True:
307 self.prerequisiteInputs.remove("bfKernel")
308 self.prerequisiteInputs.remove("newBFKernel")
309 if config.doDefect is not True:
310 self.prerequisiteInputs.remove("defects")
311 if config.doDark is not True:
312 self.prerequisiteInputs.remove("dark")
313 if config.doFlat is not True:
314 self.prerequisiteInputs.remove("flat")
315 if config.doFringe is not True:
316 self.prerequisiteInputs.remove("fringes")
317 if config.doStrayLight is not True:
318 self.prerequisiteInputs.remove("strayLightData")
319 if config.usePtcGains is not True and config.usePtcReadNoise is not True:
320 self.prerequisiteInputs.remove("ptc")
321 if config.doAttachTransmissionCurve is not True:
322 self.prerequisiteInputs.remove("opticsTransmission")
323 self.prerequisiteInputs.remove("filterTransmission")
324 self.prerequisiteInputs.remove("sensorTransmission")
325 self.prerequisiteInputs.remove("atmosphereTransmission")
326 else:
327 if config.doUseOpticsTransmission is not True:
328 self.prerequisiteInputs.remove("opticsTransmission")
329 if config.doUseFilterTransmission is not True:
330 self.prerequisiteInputs.remove("filterTransmission")
331 if config.doUseSensorTransmission is not True:
332 self.prerequisiteInputs.remove("sensorTransmission")
333 if config.doUseAtmosphereTransmission is not True:
334 self.prerequisiteInputs.remove("atmosphereTransmission")
335 if config.doIlluminationCorrection is not True:
336 self.prerequisiteInputs.remove("illumMaskedImage")
337 if config.doDeferredCharge is not True:
338 self.prerequisiteInputs.remove("deferredChargeCalib")
339
340 if config.doWrite is not True:
341 self.outputs.remove("outputExposure")
342 self.outputs.remove("preInterpExposure")
343 self.outputs.remove("outputFlattenedThumbnail")
344 self.outputs.remove("outputOssThumbnail")
345 self.outputs.remove("outputStatistics")
346 self.outputs.remove("outputBin1Exposure")
347 self.outputs.remove("outputBin2Exposure")
348 else:
349 if config.doBinnedExposures is not True:
350 self.outputs.remove("outputBin1Exposure")
351 self.outputs.remove("outputBin2Exposure")
352 if config.doSaveInterpPixels is not True:
353 self.outputs.remove("preInterpExposure")
354 if config.qa.doThumbnailOss is not True:
355 self.outputs.remove("outputOssThumbnail")
356 if config.qa.doThumbnailFlattened is not True:
357 self.outputs.remove("outputFlattenedThumbnail")
358 if config.doCalculateStatistics is not True:
359 self.outputs.remove("outputStatistics")
360
361
362class IsrTaskConfig(pipeBase.PipelineTaskConfig,
363 pipelineConnections=IsrTaskConnections):
364 """Configuration parameters for IsrTask.
365
366 Items are grouped in the order in which they are executed by the task.
367 """
368 datasetType = pexConfig.Field(
369 dtype=str,
370 doc="Dataset type for input data; users will typically leave this alone, "
371 "but camera-specific ISR tasks will override it",
372 default="raw",
373 )
374
375 fallbackFilterName = pexConfig.Field(
376 dtype=str,
377 doc="Fallback default filter name for calibrations.",
378 optional=True
379 )
380 useFallbackDate = pexConfig.Field(
381 dtype=bool,
382 doc="Pass observation date when using fallback filter.",
383 default=False,
384 )
385 expectWcs = pexConfig.Field(
386 dtype=bool,
387 default=True,
388 doc="Expect input science images to have a WCS (set False for e.g. spectrographs)."
389 )
390 fwhm = pexConfig.Field(
391 dtype=float,
392 doc="FWHM of PSF in arcseconds (currently unused).",
393 default=1.0,
394 )
395 qa = pexConfig.ConfigField(
396 dtype=isrQa.IsrQaConfig,
397 doc="QA related configuration options.",
398 )
399 doHeaderProvenance = pexConfig.Field(
400 dtype=bool,
401 default=True,
402 doc="Write calibration identifiers into output exposure header?",
403 )
404
405 # Calib checking configuration:
406 doRaiseOnCalibMismatch = pexConfig.Field(
407 dtype=bool,
408 default=False,
409 doc="Should IsrTask halt if exposure and calibration header values do not match?",
410 )
411 cameraKeywordsToCompare = pexConfig.ListField(
412 dtype=str,
413 doc="List of header keywords to compare between exposure and calibrations.",
414 default=[],
415 )
416
417 # Image conversion configuration
418 doConvertIntToFloat = pexConfig.Field(
419 dtype=bool,
420 doc="Convert integer raw images to floating point values?",
421 default=True,
422 )
423
424 # Saturated pixel handling.
425 doSaturation = pexConfig.Field(
426 dtype=bool,
427 doc="Mask saturated pixels? NB: this is totally independent of the"
428 " interpolation option - this is ONLY setting the bits in the mask."
429 " To have them interpolated make sure doSaturationInterpolation=True",
430 default=True,
431 )
432 saturatedMaskName = pexConfig.Field(
433 dtype=str,
434 doc="Name of mask plane to use in saturation detection and interpolation",
435 default="SAT",
436 )
437 saturation = pexConfig.Field(
438 dtype=float,
439 doc="The saturation level to use if no Detector is present in the Exposure (ignored if NaN)",
440 default=float("NaN"),
441 )
442 growSaturationFootprintSize = pexConfig.Field(
443 dtype=int,
444 doc="Number of pixels by which to grow the saturation footprints",
445 default=1,
446 )
447
448 # Suspect pixel handling.
449 doSuspect = pexConfig.Field(
450 dtype=bool,
451 doc="Mask suspect pixels?",
452 default=False,
453 )
454 suspectMaskName = pexConfig.Field(
455 dtype=str,
456 doc="Name of mask plane to use for suspect pixels",
457 default="SUSPECT",
458 )
459 numEdgeSuspect = pexConfig.Field(
460 dtype=int,
461 doc="Number of edge pixels to be flagged as untrustworthy.",
462 default=0,
463 )
464 edgeMaskLevel = pexConfig.ChoiceField(
465 dtype=str,
466 doc="Mask edge pixels in which coordinate frame: DETECTOR or AMP?",
467 default="DETECTOR",
468 allowed={
469 'DETECTOR': 'Mask only the edges of the full detector.',
470 'AMP': 'Mask edges of each amplifier.',
471 },
472 )
473
474 # Initial masking options.
475 doSetBadRegions = pexConfig.Field(
476 dtype=bool,
477 doc="Should we set the level of all BAD patches of the chip to the chip's average value?",
478 default=True,
479 )
480 badStatistic = pexConfig.ChoiceField(
481 dtype=str,
482 doc="How to estimate the average value for BAD regions.",
483 default='MEANCLIP',
484 allowed={
485 "MEANCLIP": "Correct using the (clipped) mean of good data",
486 "MEDIAN": "Correct using the median of the good data",
487 },
488 )
489
490 # Overscan subtraction configuration.
491 doOverscan = pexConfig.Field(
492 dtype=bool,
493 doc="Do overscan subtraction?",
494 default=True,
495 )
496 overscan = pexConfig.ConfigurableField(
497 target=OverscanCorrectionTask,
498 doc="Overscan subtraction task for image segments.",
499 )
500
501 # Amplifier to CCD assembly configuration
502 doAssembleCcd = pexConfig.Field(
503 dtype=bool,
504 default=True,
505 doc="Assemble amp-level exposures into a ccd-level exposure?"
506 )
507 assembleCcd = pexConfig.ConfigurableField(
508 target=AssembleCcdTask,
509 doc="CCD assembly task",
510 )
511
512 # General calibration configuration.
513 doAssembleIsrExposures = pexConfig.Field(
514 dtype=bool,
515 default=False,
516 doc="Assemble amp-level calibration exposures into ccd-level exposure?"
517 )
518 doTrimToMatchCalib = pexConfig.Field(
519 dtype=bool,
520 default=False,
521 doc="Trim raw data to match calibration bounding boxes?"
522 )
523
524 # Bias subtraction.
525 doBias = pexConfig.Field(
526 dtype=bool,
527 doc="Apply bias frame correction?",
528 default=True,
529 )
530 biasDataProductName = pexConfig.Field(
531 dtype=str,
532 doc="Name of the bias data product",
533 default="bias",
534 )
535 doBiasBeforeOverscan = pexConfig.Field(
536 dtype=bool,
537 doc="Reverse order of overscan and bias correction.",
538 default=False
539 )
540
541 # Deferred charge correction.
542 doDeferredCharge = pexConfig.Field(
543 dtype=bool,
544 doc="Apply deferred charge correction?",
545 default=False,
546 )
547 deferredChargeCorrection = pexConfig.ConfigurableField(
548 target=DeferredChargeTask,
549 doc="Deferred charge correction task.",
550 )
551
552 # Variance construction
553 doVariance = pexConfig.Field(
554 dtype=bool,
555 doc="Calculate variance?",
556 default=True
557 )
558 gain = pexConfig.Field(
559 dtype=float,
560 doc="The gain to use if no Detector is present in the Exposure (ignored if NaN)",
561 default=float("NaN"),
562 )
563 readNoise = pexConfig.Field(
564 dtype=float,
565 doc="The read noise to use if no Detector is present in the Exposure",
566 default=0.0,
567 )
568 doEmpiricalReadNoise = pexConfig.Field(
569 dtype=bool,
570 default=False,
571 doc="Calculate empirical read noise instead of value from AmpInfo data?"
572 )
573 usePtcReadNoise = pexConfig.Field(
574 dtype=bool,
575 default=False,
576 doc="Use readnoise values from the Photon Transfer Curve?"
577 )
578 maskNegativeVariance = pexConfig.Field(
579 dtype=bool,
580 default=True,
581 doc="Mask pixels that claim a negative variance? This likely indicates a failure "
582 "in the measurement of the overscan at an edge due to the data falling off faster "
583 "than the overscan model can account for it."
584 )
585 negativeVarianceMaskName = pexConfig.Field(
586 dtype=str,
587 default="BAD",
588 doc="Mask plane to use to mark pixels with negative variance, if `maskNegativeVariance` is True.",
589 )
590 # Linearization.
591 doLinearize = pexConfig.Field(
592 dtype=bool,
593 doc="Correct for nonlinearity of the detector's response?",
594 default=True,
595 )
596
597 # Crosstalk.
598 doCrosstalk = pexConfig.Field(
599 dtype=bool,
600 doc="Apply intra-CCD crosstalk correction?",
601 default=False,
602 )
603 doCrosstalkBeforeAssemble = pexConfig.Field(
604 dtype=bool,
605 doc="Apply crosstalk correction before CCD assembly, and before trimming?",
606 default=False,
607 )
608 crosstalk = pexConfig.ConfigurableField(
609 target=CrosstalkTask,
610 doc="Intra-CCD crosstalk correction",
611 )
612
613 # Masking options.
614 doDefect = pexConfig.Field(
615 dtype=bool,
616 doc="Apply correction for CCD defects, e.g. hot pixels?",
617 default=True,
618 )
619 doNanMasking = pexConfig.Field(
620 dtype=bool,
621 doc="Mask non-finite (NAN, inf) pixels?",
622 default=True,
623 )
624 doWidenSaturationTrails = pexConfig.Field(
625 dtype=bool,
626 doc="Widen bleed trails based on their width?",
627 default=True
628 )
629
630 # Brighter-Fatter correction.
631 doBrighterFatter = pexConfig.Field(
632 dtype=bool,
633 default=False,
634 doc="Apply the brighter-fatter correction?"
635 )
636 doFluxConservingBrighterFatterCorrection = pexConfig.Field(
637 dtype=bool,
638 default=False,
639 doc="Apply the flux-conserving BFE correction by Miller et al.?"
640 )
641 brighterFatterLevel = pexConfig.ChoiceField(
642 dtype=str,
643 default="DETECTOR",
644 doc="The level at which to correct for brighter-fatter.",
645 allowed={
646 "AMP": "Every amplifier treated separately.",
647 "DETECTOR": "One kernel per detector",
648 }
649 )
650 brighterFatterMaxIter = pexConfig.Field(
651 dtype=int,
652 default=10,
653 doc="Maximum number of iterations for the brighter-fatter correction"
654 )
655 brighterFatterThreshold = pexConfig.Field(
656 dtype=float,
657 default=1000,
658 doc="Threshold used to stop iterating the brighter-fatter correction. It is the "
659 "absolute value of the difference between the current corrected image and the one "
660 "from the previous iteration summed over all the pixels."
661 )
662 brighterFatterApplyGain = pexConfig.Field(
663 dtype=bool,
664 default=True,
665 doc="Should the gain be applied when applying the brighter-fatter correction?"
666 )
667 brighterFatterMaskListToInterpolate = pexConfig.ListField(
668 dtype=str,
669 doc="List of mask planes that should be interpolated over when applying the brighter-fatter "
670 "correction.",
671 default=["SAT", "BAD", "NO_DATA", "UNMASKEDNAN"],
672 )
673 brighterFatterMaskGrowSize = pexConfig.Field(
674 dtype=int,
675 default=0,
676 doc="Number of pixels to grow the masks listed in config.brighterFatterMaskListToInterpolate "
677 "when brighter-fatter correction is applied."
678 )
679
680 # Dark subtraction.
681 doDark = pexConfig.Field(
682 dtype=bool,
683 doc="Apply dark frame correction?",
684 default=True,
685 )
686 darkDataProductName = pexConfig.Field(
687 dtype=str,
688 doc="Name of the dark data product",
689 default="dark",
690 )
691
692 # Camera-specific stray light removal.
693 doStrayLight = pexConfig.Field(
694 dtype=bool,
695 doc="Subtract stray light in the y-band (due to encoder LEDs)?",
696 default=False,
697 )
698 strayLight = pexConfig.ConfigurableField(
699 target=StrayLightTask,
700 doc="y-band stray light correction"
701 )
702
703 # Flat correction.
704 doFlat = pexConfig.Field(
705 dtype=bool,
706 doc="Apply flat field correction?",
707 default=True,
708 )
709 flatDataProductName = pexConfig.Field(
710 dtype=str,
711 doc="Name of the flat data product",
712 default="flat",
713 )
714 flatScalingType = pexConfig.ChoiceField(
715 dtype=str,
716 doc="The method for scaling the flat on the fly.",
717 default='USER',
718 allowed={
719 "USER": "Scale by flatUserScale",
720 "MEAN": "Scale by the inverse of the mean",
721 "MEDIAN": "Scale by the inverse of the median",
722 },
723 )
724 flatUserScale = pexConfig.Field(
725 dtype=float,
726 doc="If flatScalingType is 'USER' then scale flat by this amount; ignored otherwise",
727 default=1.0,
728 )
729 doTweakFlat = pexConfig.Field(
730 dtype=bool,
731 doc="Tweak flats to match observed amplifier ratios?",
732 default=False
733 )
734
735 # Amplifier normalization based on gains instead of using flats
736 # configuration.
737 doApplyGains = pexConfig.Field(
738 dtype=bool,
739 doc="Correct the amplifiers for their gains instead of applying flat correction",
740 default=False,
741 )
742 usePtcGains = pexConfig.Field(
743 dtype=bool,
744 doc="Use the gain values from the input Photon Transfer Curve?",
745 default=False,
746 )
747 normalizeGains = pexConfig.Field(
748 dtype=bool,
749 doc="Normalize all the amplifiers in each CCD to have the same median value.",
750 default=False,
751 )
752
753 # Fringe correction.
754 doFringe = pexConfig.Field(
755 dtype=bool,
756 doc="Apply fringe correction?",
757 default=True,
758 )
759 fringe = pexConfig.ConfigurableField(
760 target=FringeTask,
761 doc="Fringe subtraction task",
762 )
763 fringeAfterFlat = pexConfig.Field(
764 dtype=bool,
765 doc="Do fringe subtraction after flat-fielding?",
766 default=True,
767 )
768
769 # Amp offset correction.
770 doAmpOffset = pexConfig.Field(
771 doc="Calculate and apply amp offset corrections?",
772 dtype=bool,
773 default=False,
774 )
775 ampOffset = pexConfig.ConfigurableField(
776 doc="Amp offset correction task.",
777 target=AmpOffsetTask,
778 )
779
780 # Initial CCD-level background statistics options.
781 doMeasureBackground = pexConfig.Field(
782 dtype=bool,
783 doc="Measure the background level on the reduced image?",
784 default=False,
785 )
786
787 # Camera-specific masking configuration.
788 doCameraSpecificMasking = pexConfig.Field(
789 dtype=bool,
790 doc="Mask camera-specific bad regions?",
791 default=False,
792 )
793 masking = pexConfig.ConfigurableField(
794 target=MaskingTask,
795 doc="Masking task."
796 )
797
798 # Interpolation options.
799 doInterpolate = pexConfig.Field(
800 dtype=bool,
801 doc="Interpolate masked pixels?",
802 default=True,
803 )
804 doSaturationInterpolation = pexConfig.Field(
805 dtype=bool,
806 doc="Perform interpolation over pixels masked as saturated?"
807 " NB: This is independent of doSaturation; if that is False this plane"
808 " will likely be blank, resulting in a no-op here.",
809 default=True,
810 )
811 doNanInterpolation = pexConfig.Field(
812 dtype=bool,
813 doc="Perform interpolation over pixels masked as NaN?"
814 " NB: This is independent of doNanMasking; if that is False this plane"
815 " will likely be blank, resulting in a no-op here.",
816 default=True,
817 )
818 doNanInterpAfterFlat = pexConfig.Field(
819 dtype=bool,
820 doc=("If True, ensure we interpolate NaNs after flat-fielding, even if we "
821 "also have to interpolate them before flat-fielding."),
822 default=False,
823 )
824 maskListToInterpolate = pexConfig.ListField(
825 dtype=str,
826 doc="List of mask planes that should be interpolated.",
827 default=['SAT', 'BAD'],
828 )
829 doSaveInterpPixels = pexConfig.Field(
830 dtype=bool,
831 doc="Save a copy of the pre-interpolated pixel values?",
832 default=False,
833 )
834
835 # Default photometric calibration options.
836 fluxMag0T1 = pexConfig.DictField(
837 keytype=str,
838 itemtype=float,
839 doc="The approximate flux of a zero-magnitude object in a one-second exposure, per filter.",
840 default=dict((f, pow(10.0, 0.4*m)) for f, m in (("Unknown", 28.0),
841 ))
842 )
843 defaultFluxMag0T1 = pexConfig.Field(
844 dtype=float,
845 doc="Default value for fluxMag0T1 (for an unrecognized filter).",
846 default=pow(10.0, 0.4*28.0)
847 )
848
849 # Vignette correction configuration.
850 doVignette = pexConfig.Field(
851 dtype=bool,
852 doc=("Compute and attach the validPolygon defining the unvignetted region to the exposure "
853 "according to vignetting parameters?"),
854 default=False,
855 )
856 doMaskVignettePolygon = pexConfig.Field(
857 dtype=bool,
858 doc=("Add a mask bit for pixels within the vignetted region. Ignored if doVignette "
859 "is False"),
860 default=True,
861 )
862 vignetteValue = pexConfig.Field(
863 dtype=float,
864 doc="Value to replace image array pixels with in the vignetted region? Ignored if None.",
865 optional=True,
866 default=None,
867 )
868 vignette = pexConfig.ConfigurableField(
869 target=VignetteTask,
870 doc="Vignetting task.",
871 )
872
873 # Transmission curve configuration.
874 doAttachTransmissionCurve = pexConfig.Field(
875 dtype=bool,
876 default=False,
877 doc="Construct and attach a wavelength-dependent throughput curve for this CCD image?"
878 )
879 doUseOpticsTransmission = pexConfig.Field(
880 dtype=bool,
881 default=True,
882 doc="Load and use transmission_optics (if doAttachTransmissionCurve is True)?"
883 )
884 doUseFilterTransmission = pexConfig.Field(
885 dtype=bool,
886 default=True,
887 doc="Load and use transmission_filter (if doAttachTransmissionCurve is True)?"
888 )
889 doUseSensorTransmission = pexConfig.Field(
890 dtype=bool,
891 default=True,
892 doc="Load and use transmission_sensor (if doAttachTransmissionCurve is True)?"
893 )
894 doUseAtmosphereTransmission = pexConfig.Field(
895 dtype=bool,
896 default=True,
897 doc="Load and use transmission_atmosphere (if doAttachTransmissionCurve is True)?"
898 )
899
900 # Illumination correction.
901 doIlluminationCorrection = pexConfig.Field(
902 dtype=bool,
903 default=False,
904 doc="Perform illumination correction?"
905 )
906 illuminationCorrectionDataProductName = pexConfig.Field(
907 dtype=str,
908 doc="Name of the illumination correction data product.",
909 default="illumcor",
910 )
911 illumScale = pexConfig.Field(
912 dtype=float,
913 doc="Scale factor for the illumination correction.",
914 default=1.0,
915 )
916 illumFilters = pexConfig.ListField(
917 dtype=str,
918 default=[],
919 doc="Only perform illumination correction for these filters."
920 )
921
922 # Calculate image quality statistics?
923 doStandardStatistics = pexConfig.Field(
924 dtype=bool,
925 doc="Should standard image quality statistics be calculated?",
926 default=True,
927 )
928 # Calculate additional statistics?
929 doCalculateStatistics = pexConfig.Field(
930 dtype=bool,
931 doc="Should additional ISR statistics be calculated?",
932 default=False,
933 )
934 isrStats = pexConfig.ConfigurableField(
935 target=IsrStatisticsTask,
936 doc="Task to calculate additional statistics.",
937 )
938
939 # Make binned images?
940 doBinnedExposures = pexConfig.Field(
941 dtype=bool,
942 doc="Should binned exposures be calculated?",
943 default=False,
944 )
945 binFactor1 = pexConfig.Field(
946 dtype=int,
947 doc="Binning factor for first binned exposure. This is intended for a finely binned output.",
948 default=8,
949 check=lambda x: x > 1,
950 )
951 binFactor2 = pexConfig.Field(
952 dtype=int,
953 doc="Binning factor for second binned exposure. This is intended for a coarsely binned output.",
954 default=64,
955 check=lambda x: x > 1,
956 )
957
958 # Write the outputs to disk. If ISR is run as a subtask, this may not
959 # be needed.
960 doWrite = pexConfig.Field(
961 dtype=bool,
962 doc="Persist postISRCCD?",
963 default=True,
964 )
965
966 def validate(self):
967 super().validate()
968 if self.doFlat and self.doApplyGains:
969 raise ValueError("You may not specify both doFlat and doApplyGains")
971 raise ValueError("You may not specify both doBiasBeforeOverscan and doTrimToMatchCalib")
976 if self.doNanInterpolation and "UNMASKEDNAN" not in self.maskListToInterpolate:
977 self.maskListToInterpolate.append("UNMASKEDNAN")
978 if self.doCalculateStatistics and self.isrStats.doCtiStatistics:
979 if self.doApplyGains != self.isrStats.doApplyGainsForCtiStatistics:
980 raise ValueError("doApplyGains must match isrStats.applyGainForCtiStatistics.")
981
982
983class IsrTask(pipeBase.PipelineTask):
984 """Apply common instrument signature correction algorithms to a raw frame.
985
986 The process for correcting imaging data is very similar from
987 camera to camera. This task provides a vanilla implementation of
988 doing these corrections, including the ability to turn certain
989 corrections off if they are not needed. The inputs to the primary
990 method, `run()`, are a raw exposure to be corrected and the
991 calibration data products. The raw input is a single chip sized
992 mosaic of all amps including overscans and other non-science
993 pixels.
994
995 The __init__ method sets up the subtasks for ISR processing, using
996 the defaults from `lsst.ip.isr`.
997
998 Parameters
999 ----------
1000 args : `list`
1001 Positional arguments passed to the Task constructor.
1002 None used at this time.
1003 kwargs : `dict`, optional
1004 Keyword arguments passed on to the Task constructor.
1005 None used at this time.
1006 """
1007 ConfigClass = IsrTaskConfig
1008 _DefaultName = "isr"
1009
1010 def __init__(self, **kwargs):
1011 super().__init__(**kwargs)
1012 self.makeSubtask("assembleCcd")
1013 self.makeSubtask("crosstalk")
1014 self.makeSubtask("strayLight")
1015 self.makeSubtask("fringe")
1016 self.makeSubtask("masking")
1017 self.makeSubtask("overscan")
1018 self.makeSubtask("vignette")
1019 self.makeSubtask("ampOffset")
1020 self.makeSubtask("deferredChargeCorrection")
1021 self.makeSubtask("isrStats")
1022
1023 def runQuantum(self, butlerQC, inputRefs, outputRefs):
1024 inputs = butlerQC.get(inputRefs)
1025
1026 try:
1027 inputs['detectorNum'] = inputRefs.ccdExposure.dataId['detector']
1028 except Exception as e:
1029 raise ValueError("Failure to find valid detectorNum value for Dataset %s: %s." %
1030 (inputRefs, e))
1031
1032 detector = inputs['ccdExposure'].getDetector()
1033
1034 # This is use for header provenance.
1035 additionalInputDates = {}
1036
1037 if self.config.doCrosstalk is True:
1038 # Crosstalk sources need to be defined by the pipeline
1039 # yaml if they exist.
1040 if 'crosstalk' in inputs and inputs['crosstalk'] is not None:
1041 if not isinstance(inputs['crosstalk'], CrosstalkCalib):
1042 inputs['crosstalk'] = CrosstalkCalib.fromTable(inputs['crosstalk'])
1043 else:
1044 coeffVector = (self.config.crosstalk.crosstalkValues
1045 if self.config.crosstalk.useConfigCoefficients else None)
1046 crosstalkCalib = CrosstalkCalib().fromDetector(detector, coeffVector=coeffVector)
1047 inputs['crosstalk'] = crosstalkCalib
1048 if inputs['crosstalk'].interChip and len(inputs['crosstalk'].interChip) > 0:
1049 if 'crosstalkSources' not in inputs:
1050 self.log.warning("No crosstalkSources found for chip with interChip terms!")
1051
1052 if self.doLinearize(detector) is True:
1053 if 'linearizer' in inputs:
1054 if isinstance(inputs['linearizer'], dict):
1055 linearizer = linearize.Linearizer(detector=detector, log=self.log)
1056 linearizer.fromYaml(inputs['linearizer'])
1057 self.log.warning("Dictionary linearizers will be deprecated in DM-28741.")
1058 elif isinstance(inputs['linearizer'], numpy.ndarray):
1059 linearizer = linearize.Linearizer(table=inputs.get('linearizer', None),
1060 detector=detector,
1061 log=self.log)
1062 self.log.warning("Bare lookup table linearizers will be deprecated in DM-28741.")
1063 else:
1064 linearizer = inputs['linearizer']
1065 self.log.info("Loading linearizer from the Butler.")
1066 linearizer.log = self.log
1067 inputs['linearizer'] = linearizer
1068 else:
1069 inputs['linearizer'] = linearize.Linearizer(detector=detector, log=self.log)
1070 self.log.info("Constructing linearizer from cameraGeom information.")
1071
1072 if self.config.doDefect is True:
1073 if "defects" in inputs and inputs['defects'] is not None:
1074 # defects is loaded as a BaseCatalog with columns
1075 # x0, y0, width, height. Masking expects a list of defects
1076 # defined by their bounding box
1077 if not isinstance(inputs["defects"], Defects):
1078 inputs["defects"] = Defects.fromTable(inputs["defects"])
1079
1080 # Load the correct style of brighter-fatter kernel, and repack
1081 # the information as a numpy array.
1082 brighterFatterSource = None
1083 if self.config.doBrighterFatter:
1084 brighterFatterKernel = inputs.pop('newBFKernel', None)
1085 if brighterFatterKernel is None:
1086 # This type of kernel must be in (y, x) index
1087 # ordering, as it used directly as the .array
1088 # component of the afwImage kernel.
1089 brighterFatterKernel = inputs.get('bfKernel', None)
1090 brighterFatterSource = 'bfKernel'
1091 additionalInputDates[brighterFatterSource] = self.extractCalibDate(brighterFatterKernel)
1092
1093 if brighterFatterKernel is None:
1094 # This was requested by the config, but none were found.
1095 raise RuntimeError("No brighter-fatter kernel was supplied.")
1096 elif not isinstance(brighterFatterKernel, numpy.ndarray):
1097 # This is a ISR calib kernel. These kernels are
1098 # generated in (x, y) index ordering, and need to be
1099 # transposed to be used directly as the .array
1100 # component of the afwImage kernel. This is done
1101 # explicitly below when setting the ``bfKernel``
1102 # input.
1103 brighterFatterSource = 'newBFKernel'
1104 additionalInputDates[brighterFatterSource] = self.extractCalibDate(brighterFatterKernel)
1105
1106 detName = detector.getName()
1107 level = brighterFatterKernel.level
1108
1109 # This is expected to be a dictionary of amp-wise gains.
1110 inputs['bfGains'] = brighterFatterKernel.gain
1111 if self.config.brighterFatterLevel == 'DETECTOR':
1112 kernel = None
1113 if level == 'DETECTOR':
1114 if detName in brighterFatterKernel.detKernels:
1115 kernel = brighterFatterKernel.detKernels[detName]
1116 else:
1117 raise RuntimeError("Failed to extract kernel from new-style BF kernel.")
1118 elif level == 'AMP':
1119 self.log.warning("Making DETECTOR level kernel from AMP based brighter "
1120 "fatter kernels.")
1121 brighterFatterKernel.makeDetectorKernelFromAmpwiseKernels(detName)
1122 kernel = brighterFatterKernel.detKernels[detName]
1123 if kernel is None:
1124 raise RuntimeError("Could not identify brighter-fatter kernel!")
1125 # Do the one single transpose here so the kernel
1126 # can be directly loaded into the afwImage .array
1127 # component.
1128 inputs['bfKernel'] = numpy.transpose(kernel)
1129 elif self.config.brighterFatterLevel == 'AMP':
1130 raise NotImplementedError("Per-amplifier brighter-fatter correction not implemented")
1131
1132 if self.config.doFringe is True and self.fringe.checkFilter(inputs['ccdExposure']):
1133 expId = inputs['ccdExposure'].info.id
1134 inputs['fringes'] = self.fringe.loadFringes(inputs['fringes'],
1135 expId=expId,
1136 assembler=self.assembleCcd
1137 if self.config.doAssembleIsrExposures else None)
1138 else:
1139 inputs['fringes'] = pipeBase.Struct(fringes=None)
1140
1141 if self.config.doStrayLight is True and self.strayLight.checkFilter(inputs['ccdExposure']):
1142 if 'strayLightData' not in inputs:
1143 inputs['strayLightData'] = None
1144
1145 if self.config.doHeaderProvenance:
1146 # Add calibration provenanace info to header.
1147 exposureMetadata = inputs['ccdExposure'].getMetadata()
1148
1149 # These inputs change name during this step. These should
1150 # have matching entries in the additionalInputDates dict.
1151 additionalInputs = []
1152 if self.config.doBrighterFatter:
1153 additionalInputs.append(brighterFatterSource)
1154
1155 for inputName in sorted(list(inputs.keys()) + additionalInputs):
1156 reference = getattr(inputRefs, inputName, None)
1157 if reference is not None and hasattr(reference, "run"):
1158 runKey = f"LSST CALIB RUN {inputName.upper()}"
1159 runValue = reference.run
1160 idKey = f"LSST CALIB UUID {inputName.upper()}"
1161 idValue = str(reference.id)
1162 dateKey = f"LSST CALIB DATE {inputName.upper()}"
1163
1164 if inputName in additionalInputDates:
1165 dateValue = additionalInputDates[inputName]
1166 else:
1167 dateValue = self.extractCalibDate(inputs[inputName])
1168
1169 exposureMetadata[runKey] = runValue
1170 exposureMetadata[idKey] = idValue
1171 exposureMetadata[dateKey] = dateValue
1172
1173 outputs = self.run(**inputs)
1174 butlerQC.put(outputs, outputRefs)
1175
1176 @timeMethod
1177 def run(self, ccdExposure, *, camera=None, bias=None, linearizer=None,
1178 crosstalk=None, crosstalkSources=None,
1179 dark=None, flat=None, ptc=None, bfKernel=None, bfGains=None, defects=None,
1180 fringes=pipeBase.Struct(fringes=None), opticsTransmission=None, filterTransmission=None,
1181 sensorTransmission=None, atmosphereTransmission=None,
1182 detectorNum=None, strayLightData=None, illumMaskedImage=None,
1183 deferredChargeCalib=None,
1184 ):
1185 """Perform instrument signature removal on an exposure.
1186
1187 Steps included in the ISR processing, in order performed, are:
1188
1189 - saturation and suspect pixel masking
1190 - overscan subtraction
1191 - CCD assembly of individual amplifiers
1192 - bias subtraction
1193 - variance image construction
1194 - linearization of non-linear response
1195 - crosstalk masking
1196 - brighter-fatter correction
1197 - dark subtraction
1198 - fringe correction
1199 - stray light subtraction
1200 - flat correction
1201 - masking of known defects and camera specific features
1202 - vignette calculation
1203 - appending transmission curve and distortion model
1204
1205 Parameters
1206 ----------
1207 ccdExposure : `lsst.afw.image.Exposure`
1208 The raw exposure that is to be run through ISR. The
1209 exposure is modified by this method.
1210 camera : `lsst.afw.cameraGeom.Camera`, optional
1211 The camera geometry for this exposure. Required if
1212 one or more of ``ccdExposure``, ``bias``, ``dark``, or
1213 ``flat`` does not have an associated detector.
1214 bias : `lsst.afw.image.Exposure`, optional
1215 Bias calibration frame.
1216 linearizer : `lsst.ip.isr.linearize.LinearizeBase`, optional
1217 Functor for linearization.
1218 crosstalk : `lsst.ip.isr.crosstalk.CrosstalkCalib`, optional
1219 Calibration for crosstalk.
1220 crosstalkSources : `list`, optional
1221 List of possible crosstalk sources.
1222 dark : `lsst.afw.image.Exposure`, optional
1223 Dark calibration frame.
1224 flat : `lsst.afw.image.Exposure`, optional
1225 Flat calibration frame.
1226 ptc : `lsst.ip.isr.PhotonTransferCurveDataset`, optional
1227 Photon transfer curve dataset, with, e.g., gains
1228 and read noise.
1229 bfKernel : `numpy.ndarray`, optional
1230 Brighter-fatter kernel.
1231 bfGains : `dict` of `float`, optional
1232 Gains used to override the detector's nominal gains for the
1233 brighter-fatter correction. A dict keyed by amplifier name for
1234 the detector in question.
1235 defects : `lsst.ip.isr.Defects`, optional
1236 List of defects.
1237 fringes : `lsst.pipe.base.Struct`, optional
1238 Struct containing the fringe correction data, with
1239 elements:
1240
1241 ``fringes``
1242 fringe calibration frame (`lsst.afw.image.Exposure`)
1243 ``seed``
1244 random seed derived from the ``ccdExposureId`` for random
1245 number generator (`numpy.uint32`)
1246 opticsTransmission: `lsst.afw.image.TransmissionCurve`, optional
1247 A ``TransmissionCurve`` that represents the throughput of the,
1248 optics, to be evaluated in focal-plane coordinates.
1249 filterTransmission : `lsst.afw.image.TransmissionCurve`
1250 A ``TransmissionCurve`` that represents the throughput of the
1251 filter itself, to be evaluated in focal-plane coordinates.
1252 sensorTransmission : `lsst.afw.image.TransmissionCurve`
1253 A ``TransmissionCurve`` that represents the throughput of the
1254 sensor itself, to be evaluated in post-assembly trimmed detector
1255 coordinates.
1256 atmosphereTransmission : `lsst.afw.image.TransmissionCurve`
1257 A ``TransmissionCurve`` that represents the throughput of the
1258 atmosphere, assumed to be spatially constant.
1259 detectorNum : `int`, optional
1260 The integer number for the detector to process.
1261 strayLightData : `object`, optional
1262 Opaque object containing calibration information for stray-light
1263 correction. If `None`, no correction will be performed.
1264 illumMaskedImage : `lsst.afw.image.MaskedImage`, optional
1265 Illumination correction image.
1266
1267 Returns
1268 -------
1269 result : `lsst.pipe.base.Struct`
1270 Result struct with component:
1271
1272 ``exposure``
1273 The fully ISR corrected exposure.
1274 (`lsst.afw.image.Exposure`)
1275 ``outputExposure``
1276 An alias for ``exposure``. (`lsst.afw.image.Exposure`)
1277 ``ossThumb``
1278 Thumbnail image of the exposure after overscan subtraction.
1279 (`numpy.ndarray`)
1280 ``flattenedThumb``
1281 Thumbnail image of the exposure after flat-field correction.
1282 (`numpy.ndarray`)
1283 ``outputStatistics``
1284 Values of the additional statistics calculated.
1285
1286 Raises
1287 ------
1288 RuntimeError
1289 Raised if a configuration option is set to `True`, but the
1290 required calibration data has not been specified.
1291
1292 Notes
1293 -----
1294 The current processed exposure can be viewed by setting the
1295 appropriate `lsstDebug` entries in the ``debug.display``
1296 dictionary. The names of these entries correspond to some of
1297 the `IsrTaskConfig` Boolean options, with the value denoting the
1298 frame to use. The exposure is shown inside the matching
1299 option check and after the processing of that step has
1300 finished. The steps with debug points are:
1301
1302 * doAssembleCcd
1303 * doBias
1304 * doCrosstalk
1305 * doBrighterFatter
1306 * doDark
1307 * doFringe
1308 * doStrayLight
1309 * doFlat
1310
1311 In addition, setting the ``postISRCCD`` entry displays the
1312 exposure after all ISR processing has finished.
1313 """
1314
1315 ccdExposure = self.ensureExposure(ccdExposure, camera, detectorNum)
1316 bias = self.ensureExposure(bias, camera, detectorNum)
1317 dark = self.ensureExposure(dark, camera, detectorNum)
1318 flat = self.ensureExposure(flat, camera, detectorNum)
1319
1320 ccd = ccdExposure.getDetector()
1321 filterLabel = ccdExposure.getFilter()
1322 physicalFilter = isrFunctions.getPhysicalFilter(filterLabel, self.log)
1323
1324 if not ccd:
1325 assert not self.config.doAssembleCcd, "You need a Detector to run assembleCcd."
1326 ccd = [FakeAmp(ccdExposure, self.config)]
1327
1328 # Validate Input
1329 if self.config.doBias and bias is None:
1330 raise RuntimeError("Must supply a bias exposure if config.doBias=True.")
1331 if self.doLinearize(ccd) and linearizer is None:
1332 raise RuntimeError("Must supply a linearizer if config.doLinearize=True for this detector.")
1333 if self.config.doBrighterFatter and bfKernel is None:
1334 raise RuntimeError("Must supply a kernel if config.doBrighterFatter=True.")
1335 if self.config.doDark and dark is None:
1336 raise RuntimeError("Must supply a dark exposure if config.doDark=True.")
1337 if self.config.doFlat and flat is None:
1338 raise RuntimeError("Must supply a flat exposure if config.doFlat=True.")
1339 if self.config.doDefect and defects is None:
1340 raise RuntimeError("Must supply defects if config.doDefect=True.")
1341 if (self.config.doFringe and physicalFilter in self.fringe.config.filters
1342 and fringes.fringes is None):
1343 # The `fringes` object needs to be a pipeBase.Struct, as
1344 # we use it as a `dict` for the parameters of
1345 # `FringeTask.run()`. The `fringes.fringes` `list` may
1346 # not be `None` if `doFringe=True`. Otherwise, raise.
1347 raise RuntimeError("Must supply fringe exposure as a pipeBase.Struct.")
1348 if (self.config.doIlluminationCorrection and physicalFilter in self.config.illumFilters
1349 and illumMaskedImage is None):
1350 raise RuntimeError("Must supply an illumcor if config.doIlluminationCorrection=True.")
1351 if (self.config.doDeferredCharge and deferredChargeCalib is None):
1352 raise RuntimeError("Must supply a deferred charge calibration if config.doDeferredCharge=True.")
1353 if (self.config.usePtcGains and ptc is None):
1354 raise RuntimeError("No ptcDataset provided to use PTC gains.")
1355 if (self.config.usePtcReadNoise and ptc is None):
1356 raise RuntimeError("No ptcDataset provided to use PTC read noise.")
1357
1358 # Validate that the inputs match the exposure configuration.
1359 exposureMetadata = ccdExposure.getMetadata()
1360 if self.config.doBias:
1361 self.compareCameraKeywords(exposureMetadata, bias, "bias")
1362 if self.config.doBrighterFatter:
1363 self.compareCameraKeywords(exposureMetadata, bfKernel, "brighter-fatter")
1364 if self.config.doCrosstalk:
1365 self.compareCameraKeywords(exposureMetadata, crosstalk, "crosstalk")
1366 if self.config.doDark:
1367 self.compareCameraKeywords(exposureMetadata, dark, "dark")
1368 if self.config.doDefect:
1369 self.compareCameraKeywords(exposureMetadata, defects, "defects")
1370 if self.config.doDeferredCharge:
1371 self.compareCameraKeywords(exposureMetadata, deferredChargeCalib, "CTI")
1372 if self.config.doFlat:
1373 self.compareCameraKeywords(exposureMetadata, flat, "flat")
1374 if (self.config.doFringe and physicalFilter in self.fringe.config.filters):
1375 self.compareCameraKeywords(exposureMetadata, fringes.fringes, "fringe")
1376 if (self.config.doIlluminationCorrection and physicalFilter in self.config.illumFilters):
1377 self.compareCameraKeywords(exposureMetadata, illumMaskedImage, "illumination")
1378 if self.doLinearize(ccd):
1379 self.compareCameraKeywords(exposureMetadata, linearizer, "linearizer")
1380 if self.config.usePtcGains or self.config.usePtcReadNoise:
1381 self.compareCameraKeywords(exposureMetadata, ptc, "PTC")
1382 if self.config.doStrayLight:
1383 self.compareCameraKeywords(exposureMetadata, strayLightData, "straylight")
1384
1385 # Start in ADU. Update units to electrons when gain is applied:
1386 # updateVariance, applyGains
1387 # Check if needed during/after BFE correction, CTI correction.
1388 exposureMetadata["LSST ISR UNITS"] = "ADU"
1389
1390 # Begin ISR processing.
1391 if self.config.doConvertIntToFloat:
1392 self.log.info("Converting exposure to floating point values.")
1393 ccdExposure = self.convertIntToFloat(ccdExposure)
1394
1395 if self.config.doBias and self.config.doBiasBeforeOverscan:
1396 self.log.info("Applying bias correction.")
1397 isrFunctions.biasCorrection(ccdExposure.getMaskedImage(), bias.getMaskedImage(),
1398 trimToFit=self.config.doTrimToMatchCalib)
1399 self.debugView(ccdExposure, "doBias")
1400
1401 # Amplifier level processing.
1402 overscans = []
1403
1404 if self.config.doOverscan and self.config.overscan.doParallelOverscan:
1405 # This will attempt to mask bleed pixels across all amplifiers.
1406 self.overscan.maskParallelOverscan(ccdExposure, ccd)
1407
1408 for amp in ccd:
1409 # if ccdExposure is one amp,
1410 # check for coverage to prevent performing ops multiple times
1411 if ccdExposure.getBBox().contains(amp.getBBox()):
1412 # Check for fully masked bad amplifiers,
1413 # and generate masks for SUSPECT and SATURATED values.
1414 badAmp = self.maskAmplifier(ccdExposure, amp, defects)
1415
1416 if self.config.doOverscan and not badAmp:
1417 # Overscan correction on amp-by-amp basis.
1418 overscanResults = self.overscanCorrection(ccdExposure, amp)
1419 self.log.debug("Corrected overscan for amplifier %s.", amp.getName())
1420 if overscanResults is not None and \
1421 self.config.qa is not None and self.config.qa.saveStats is True:
1422 if isinstance(overscanResults.overscanMean, float):
1423 # Only serial overscan was run
1424 mean = overscanResults.overscanMean
1425 sigma = overscanResults.overscanSigma
1426 residMean = overscanResults.residualMean
1427 residSigma = overscanResults.residualSigma
1428 else:
1429 # Both serial and parallel overscan were
1430 # run. Only report serial here.
1431 mean = overscanResults.overscanMean[0]
1432 sigma = overscanResults.overscanSigma[0]
1433 residMean = overscanResults.residualMean[0]
1434 residSigma = overscanResults.residualSigma[0]
1435
1436 self.metadata[f"FIT MEDIAN {amp.getName()}"] = mean
1437 self.metadata[f"FIT STDEV {amp.getName()}"] = sigma
1438 self.log.debug(" Overscan stats for amplifer %s: %f +/- %f",
1439 amp.getName(), mean, sigma)
1440
1441 self.metadata[f"RESIDUAL MEDIAN {amp.getName()}"] = residMean
1442 self.metadata[f"RESIDUAL STDEV {amp.getName()}"] = residSigma
1443 self.log.debug(" Overscan stats for amplifer %s after correction: %f +/- %f",
1444 amp.getName(), residMean, residSigma)
1445
1446 ccdExposure.getMetadata().set('OVERSCAN', "Overscan corrected")
1447 else:
1448 if badAmp:
1449 self.log.warning("Amplifier %s is bad.", amp.getName())
1450 overscanResults = None
1451
1452 overscans.append(overscanResults if overscanResults is not None else None)
1453 else:
1454 self.log.info("Skipped OSCAN for %s.", amp.getName())
1455
1456 # Define an effective PTC that will contain the gain and readout
1457 # noise to be used throughout the ISR task.
1458 ptc = self.defineEffectivePtc(ptc, ccd, bfGains, overscans, exposureMetadata)
1459
1460 if self.config.doDeferredCharge:
1461 self.log.info("Applying deferred charge/CTI correction.")
1462 self.deferredChargeCorrection.run(ccdExposure, deferredChargeCalib)
1463 self.debugView(ccdExposure, "doDeferredCharge")
1464
1465 if self.config.doCrosstalk and self.config.doCrosstalkBeforeAssemble:
1466 self.log.info("Applying crosstalk correction.")
1467 self.crosstalk.run(ccdExposure, crosstalk=crosstalk,
1468 crosstalkSources=crosstalkSources, camera=camera)
1469 self.debugView(ccdExposure, "doCrosstalk")
1470
1471 if self.config.doAssembleCcd:
1472 self.log.info("Assembling CCD from amplifiers.")
1473 ccdExposure = self.assembleCcd.assembleCcd(ccdExposure)
1474
1475 if self.config.expectWcs and not ccdExposure.getWcs():
1476 self.log.warning("No WCS found in input exposure.")
1477 self.debugView(ccdExposure, "doAssembleCcd")
1478
1479 ossThumb = None
1480 if self.config.qa.doThumbnailOss:
1481 ossThumb = isrQa.makeThumbnail(ccdExposure, isrQaConfig=self.config.qa)
1482
1483 if self.config.doBias and not self.config.doBiasBeforeOverscan:
1484 self.log.info("Applying bias correction.")
1485 isrFunctions.biasCorrection(ccdExposure.getMaskedImage(), bias.getMaskedImage(),
1486 trimToFit=self.config.doTrimToMatchCalib)
1487 self.debugView(ccdExposure, "doBias")
1488
1489 if self.config.doVariance:
1490 for amp in ccd:
1491 if ccdExposure.getBBox().contains(amp.getBBox()):
1492 self.log.debug("Constructing variance map for amplifer %s.", amp.getName())
1493 ampExposure = ccdExposure.Factory(ccdExposure, amp.getBBox())
1494 self.updateVariance(ampExposure, amp, ptc)
1495
1496 if self.config.qa is not None and self.config.qa.saveStats is True:
1497 qaStats = afwMath.makeStatistics(ampExposure.getVariance(),
1498 afwMath.MEDIAN | afwMath.STDEVCLIP)
1499 self.metadata[f"ISR VARIANCE {amp.getName()} MEDIAN"] = \
1500 qaStats.getValue(afwMath.MEDIAN)
1501 self.metadata[f"ISR VARIANCE {amp.getName()} STDEV"] = \
1502 qaStats.getValue(afwMath.STDEVCLIP)
1503 self.log.debug(" Variance stats for amplifer %s: %f +/- %f.",
1504 amp.getName(), qaStats.getValue(afwMath.MEDIAN),
1505 qaStats.getValue(afwMath.STDEVCLIP))
1506 if self.config.maskNegativeVariance:
1507 self.maskNegativeVariance(ccdExposure)
1508
1509 if self.doLinearize(ccd):
1510 self.log.info("Applying linearizer.")
1511 linearizer.applyLinearity(image=ccdExposure.getMaskedImage().getImage(),
1512 detector=ccd, log=self.log)
1513
1514 if self.config.doCrosstalk and not self.config.doCrosstalkBeforeAssemble:
1515 self.log.info("Applying crosstalk correction.")
1516 self.crosstalk.run(ccdExposure, crosstalk=crosstalk,
1517 crosstalkSources=crosstalkSources, isTrimmed=True)
1518 self.debugView(ccdExposure, "doCrosstalk")
1519
1520 # Masking block. Optionally mask known defects, NAN/inf pixels,
1521 # widen trails, and do anything else the camera needs. Saturated and
1522 # suspect pixels have already been masked.
1523 if self.config.doDefect:
1524 self.log.info("Masking defects.")
1525 self.maskDefect(ccdExposure, defects)
1526
1527 if self.config.numEdgeSuspect > 0:
1528 self.log.info("Masking edges as SUSPECT.")
1529 self.maskEdges(ccdExposure, numEdgePixels=self.config.numEdgeSuspect,
1530 maskPlane="SUSPECT", level=self.config.edgeMaskLevel)
1531
1532 if self.config.doNanMasking:
1533 self.log.info("Masking non-finite (NAN, inf) value pixels.")
1534 self.maskNan(ccdExposure)
1535
1536 if self.config.doWidenSaturationTrails:
1537 self.log.info("Widening saturation trails.")
1538 isrFunctions.widenSaturationTrails(ccdExposure.getMaskedImage().getMask())
1539
1540 if self.config.doCameraSpecificMasking:
1541 self.log.info("Masking regions for camera specific reasons.")
1542 self.masking.run(ccdExposure)
1543
1544 if self.config.doBrighterFatter:
1545 # We need to apply flats and darks before we can interpolate, and
1546 # we need to interpolate before we do B-F, but we do B-F without
1547 # the flats and darks applied so we can work in units of electrons
1548 # or holes. This context manager applies and then removes the darks
1549 # and flats.
1550 #
1551 # We also do not want to interpolate values here, so operate on
1552 # temporary images so we can apply only the BF-correction and roll
1553 # back the interpolation.
1554 interpExp = ccdExposure.clone()
1555 with self.flatContext(interpExp, flat, dark):
1556 isrFunctions.interpolateFromMask(
1557 maskedImage=interpExp.getMaskedImage(),
1558 fwhm=self.config.fwhm,
1559 growSaturatedFootprints=self.config.growSaturationFootprintSize,
1560 maskNameList=list(self.config.brighterFatterMaskListToInterpolate)
1561 )
1562 bfExp = interpExp.clone()
1563
1564 self.log.info("Applying brighter-fatter correction using kernel type %s / gains %s.",
1565 type(bfKernel), type(bfGains))
1566 if self.config.doFluxConservingBrighterFatterCorrection:
1567 bfResults = isrFunctions.fluxConservingBrighterFatterCorrection(
1568 bfExp,
1569 bfKernel,
1570 self.config.brighterFatterMaxIter,
1571 self.config.brighterFatterThreshold,
1572 self.config.brighterFatterApplyGain,
1573 bfGains
1574 )
1575 else:
1576 bfResults = isrFunctions.brighterFatterCorrection(
1577 bfExp,
1578 bfKernel,
1579 self.config.brighterFatterMaxIter,
1580 self.config.brighterFatterThreshold,
1581 self.config.brighterFatterApplyGain,
1582 bfGains
1583 )
1584 if bfResults[1] == self.config.brighterFatterMaxIter - 1:
1585 self.log.warning("Brighter-fatter correction did not converge, final difference %f.",
1586 bfResults[0])
1587 else:
1588 self.log.info("Finished brighter-fatter correction in %d iterations.",
1589 bfResults[1])
1590 image = ccdExposure.getMaskedImage().getImage()
1591 bfCorr = bfExp.getMaskedImage().getImage()
1592 bfCorr -= interpExp.getMaskedImage().getImage()
1593 image += bfCorr
1594
1595 # Applying the brighter-fatter correction applies a
1596 # convolution to the science image. At the edges this
1597 # convolution may not have sufficient valid pixels to
1598 # produce a valid correction. Mark pixels within the size
1599 # of the brighter-fatter kernel as EDGE to warn of this
1600 # fact.
1601 self.log.info("Ensuring image edges are masked as EDGE to the brighter-fatter kernel size.")
1602 self.maskEdges(ccdExposure, numEdgePixels=numpy.max(bfKernel.shape) // 2,
1603 maskPlane="EDGE")
1604
1605 if self.config.brighterFatterMaskGrowSize > 0:
1606 self.log.info("Growing masks to account for brighter-fatter kernel convolution.")
1607 for maskPlane in self.config.brighterFatterMaskListToInterpolate:
1608 isrFunctions.growMasks(ccdExposure.getMask(),
1609 radius=self.config.brighterFatterMaskGrowSize,
1610 maskNameList=maskPlane,
1611 maskValue=maskPlane)
1612
1613 self.debugView(ccdExposure, "doBrighterFatter")
1614
1615 if self.config.doDark:
1616 self.log.info("Applying dark correction.")
1617 self.darkCorrection(ccdExposure, dark)
1618 self.debugView(ccdExposure, "doDark")
1619
1620 if self.config.doFringe and not self.config.fringeAfterFlat:
1621 self.log.info("Applying fringe correction before flat.")
1622 self.fringe.run(ccdExposure, **fringes.getDict())
1623 self.debugView(ccdExposure, "doFringe")
1624
1625 if self.config.doStrayLight and self.strayLight.check(ccdExposure):
1626 self.log.info("Checking strayLight correction.")
1627 self.strayLight.run(ccdExposure, strayLightData)
1628 self.debugView(ccdExposure, "doStrayLight")
1629
1630 if self.config.doFlat:
1631 self.log.info("Applying flat correction.")
1632 self.flatCorrection(ccdExposure, flat)
1633 self.debugView(ccdExposure, "doFlat")
1634
1635 if self.config.doApplyGains:
1636 self.log.info("Applying gain correction instead of flat.")
1637 isrFunctions.applyGains(ccdExposure, self.config.normalizeGains,
1638 ptcGains=ptc.gain)
1639 exposureMetadata["LSST ISR UNITS"] = "electrons"
1640
1641 if self.config.doFringe and self.config.fringeAfterFlat:
1642 self.log.info("Applying fringe correction after flat.")
1643 self.fringe.run(ccdExposure, **fringes.getDict())
1644
1645 if self.config.doVignette:
1646 if self.config.doMaskVignettePolygon:
1647 self.log.info("Constructing, attaching, and masking vignette polygon.")
1648 else:
1649 self.log.info("Constructing and attaching vignette polygon.")
1650 self.vignettePolygon = self.vignette.run(
1651 exposure=ccdExposure, doUpdateMask=self.config.doMaskVignettePolygon,
1652 vignetteValue=self.config.vignetteValue, log=self.log)
1653
1654 if self.config.doAttachTransmissionCurve:
1655 self.log.info("Adding transmission curves.")
1656 isrFunctions.attachTransmissionCurve(ccdExposure, opticsTransmission=opticsTransmission,
1657 filterTransmission=filterTransmission,
1658 sensorTransmission=sensorTransmission,
1659 atmosphereTransmission=atmosphereTransmission)
1660
1661 flattenedThumb = None
1662 if self.config.qa.doThumbnailFlattened:
1663 flattenedThumb = isrQa.makeThumbnail(ccdExposure, isrQaConfig=self.config.qa)
1664
1665 if self.config.doIlluminationCorrection and physicalFilter in self.config.illumFilters:
1666 self.log.info("Performing illumination correction.")
1667 isrFunctions.illuminationCorrection(ccdExposure.getMaskedImage(),
1668 illumMaskedImage, illumScale=self.config.illumScale,
1669 trimToFit=self.config.doTrimToMatchCalib)
1670
1671 preInterpExp = None
1672 if self.config.doSaveInterpPixels:
1673 preInterpExp = ccdExposure.clone()
1674
1675 # Reset and interpolate bad pixels.
1676 #
1677 # Large contiguous bad regions (which should have the BAD mask
1678 # bit set) should have their values set to the image median.
1679 # This group should include defects and bad amplifiers. As the
1680 # area covered by these defects are large, there's little
1681 # reason to expect that interpolation would provide a more
1682 # useful value.
1683 #
1684 # Smaller defects can be safely interpolated after the larger
1685 # regions have had their pixel values reset. This ensures
1686 # that the remaining defects adjacent to bad amplifiers (as an
1687 # example) do not attempt to interpolate extreme values.
1688 if self.config.doSetBadRegions:
1689 badPixelCount, badPixelValue = isrFunctions.setBadRegions(ccdExposure)
1690 if badPixelCount > 0:
1691 self.log.info("Set %d BAD pixels to %f.", badPixelCount, badPixelValue)
1692
1693 if self.config.doInterpolate:
1694 self.log.info("Interpolating masked pixels.")
1695 isrFunctions.interpolateFromMask(
1696 maskedImage=ccdExposure.getMaskedImage(),
1697 fwhm=self.config.fwhm,
1698 growSaturatedFootprints=self.config.growSaturationFootprintSize,
1699 maskNameList=list(self.config.maskListToInterpolate)
1700 )
1701
1702 self.roughZeroPoint(ccdExposure)
1703
1704 # correct for amp offsets within the CCD
1705 if self.config.doAmpOffset:
1706 self.log.info("Correcting amp offsets.")
1707 self.ampOffset.run(ccdExposure)
1708
1709 if self.config.doMeasureBackground:
1710 self.log.info("Measuring background level.")
1711 self.measureBackground(ccdExposure, self.config.qa)
1712
1713 if self.config.qa is not None and self.config.qa.saveStats is True:
1714 for amp in ccd:
1715 ampExposure = ccdExposure.Factory(ccdExposure, amp.getBBox())
1716 qaStats = afwMath.makeStatistics(ampExposure.getImage(),
1717 afwMath.MEDIAN | afwMath.STDEVCLIP)
1718 self.metadata[f"ISR BACKGROUND {amp.getName()} MEDIAN"] = qaStats.getValue(afwMath.MEDIAN)
1719 self.metadata[f"ISR BACKGROUND {amp.getName()} STDEV"] = \
1720 qaStats.getValue(afwMath.STDEVCLIP)
1721 self.log.debug(" Background stats for amplifer %s: %f +/- %f",
1722 amp.getName(), qaStats.getValue(afwMath.MEDIAN),
1723 qaStats.getValue(afwMath.STDEVCLIP))
1724
1725 # Calculate standard image quality statistics
1726 if self.config.doStandardStatistics:
1727 metadata = ccdExposure.getMetadata()
1728 for amp in ccd:
1729 ampExposure = ccdExposure.Factory(ccdExposure, amp.getBBox())
1730 ampName = amp.getName()
1731 metadata[f"LSST ISR MASK SAT {ampName}"] = isrFunctions.countMaskedPixels(
1732 ampExposure.getMaskedImage(),
1733 [self.config.saturatedMaskName]
1734 )
1735 metadata[f"LSST ISR MASK BAD {ampName}"] = isrFunctions.countMaskedPixels(
1736 ampExposure.getMaskedImage(),
1737 ["BAD"]
1738 )
1739 qaStats = afwMath.makeStatistics(ampExposure.getImage(),
1740 afwMath.MEAN | afwMath.MEDIAN | afwMath.STDEVCLIP)
1741
1742 metadata[f"LSST ISR FINAL MEAN {ampName}"] = qaStats.getValue(afwMath.MEAN)
1743 metadata[f"LSST ISR FINAL MEDIAN {ampName}"] = qaStats.getValue(afwMath.MEDIAN)
1744 metadata[f"LSST ISR FINAL STDEV {ampName}"] = qaStats.getValue(afwMath.STDEVCLIP)
1745
1746 k1 = f"LSST ISR FINAL MEDIAN {ampName}"
1747 k2 = f"LSST ISR OVERSCAN SERIAL MEDIAN {ampName}"
1748 if self.config.doOverscan and k1 in metadata and k2 in metadata:
1749 metadata[f"LSST ISR LEVEL {ampName}"] = metadata[k1] - metadata[k2]
1750 else:
1751 metadata[f"LSST ISR LEVEL {ampName}"] = numpy.nan
1752
1753 # calculate additional statistics.
1754 outputStatistics = None
1755 if self.config.doCalculateStatistics:
1756 outputStatistics = self.isrStats.run(ccdExposure, overscanResults=overscans,
1757 bias=bias, dark=dark, flat=flat, ptc=ptc).results
1758
1759 # do any binning.
1760 outputBin1Exposure = None
1761 outputBin2Exposure = None
1762 if self.config.doBinnedExposures:
1763 outputBin1Exposure, outputBin2Exposure = self.makeBinnedImages(ccdExposure)
1764
1765 self.debugView(ccdExposure, "postISRCCD")
1766
1767 return pipeBase.Struct(
1768 exposure=ccdExposure,
1769 ossThumb=ossThumb,
1770 flattenedThumb=flattenedThumb,
1771
1772 outputBin1Exposure=outputBin1Exposure,
1773 outputBin2Exposure=outputBin2Exposure,
1774
1775 preInterpExposure=preInterpExp,
1776 outputExposure=ccdExposure,
1777 outputOssThumbnail=ossThumb,
1778 outputFlattenedThumbnail=flattenedThumb,
1779 outputStatistics=outputStatistics,
1780 )
1781
1782 def defineEffectivePtc(self, ptcDataset, detector, bfGains, overScans, metadata):
1783 """Define an effective Photon Transfer Curve dataset
1784 with nominal gains and noise.
1785
1786 Parameters
1787 ----------
1788 ptcDataset : `lsst.ip.isr.PhotonTransferCurveDataset`
1789 Input Photon Transfer Curve dataset.
1790 detector : `lsst.afw.cameraGeom.Detector`
1791 Detector object.
1792 bfGains : `dict`
1793 Gains from running the brighter-fatter code.
1794 A dict keyed by amplifier name for the detector
1795 in question.
1796 ovserScans : `list` [`lsst.pipe.base.Struct`]
1797 List of overscanResults structures
1798 metadata : `lsst.daf.base.PropertyList`
1799 Exposure metadata to update gain and noise provenance.
1800
1801 Returns
1802 -------
1803 effectivePtc : `lsst.ip.isr.PhotonTransferCurveDataset`
1804 PTC dataset containing gains and readout noise
1805 values to be used throughout
1806 Instrument Signature Removal.
1807 """
1808 amps = detector.getAmplifiers()
1809 ampNames = [amp.getName() for amp in amps]
1810 detName = detector.getName()
1811 effectivePtc = PhotonTransferCurveDataset(ampNames, 'EFFECTIVE_PTC', 1)
1812 boolGainMismatch = False
1813 doWarningPtcValidation = True
1814
1815 for amp, overscanResults in zip(amps, overScans):
1816 ampName = amp.getName()
1817 # Gain:
1818 # Try first with the PTC gains.
1819 gainProvenanceString = "amp"
1820 if self.config.usePtcGains:
1821 gain = ptcDataset.gain[ampName]
1822 gainProvenanceString = "ptc"
1823 self.log.debug("Using gain from Photon Transfer Curve.")
1824 else:
1825 # Try then with the amplifier gain.
1826 # We already have a detector at this point. If there was no
1827 # detector to begin with, one would have been created with
1828 # self.config.gain and self.config.noise. Same comment
1829 # applies for the noise block below.
1830 gain = amp.getGain()
1831
1832 # Check if the gain up to this point differs from the
1833 # gain in bfGains. If so, raise or warn, accordingly.
1834 if not boolGainMismatch and bfGains is not None and ampName in bfGains:
1835 bfGain = bfGains[ampName]
1836 if not math.isclose(gain, bfGain, rel_tol=1e-4):
1837 if self.config.doRaiseOnCalibMismatch:
1838 raise RuntimeError("Gain mismatch for det %s amp %s: "
1839 "(gain (%s): %s, bfGain: %s)",
1840 detName, ampName, gainProvenanceString,
1841 gain, bfGain)
1842 else:
1843 self.log.warning("Gain mismatch for det %s amp %s: "
1844 "(gain (%s): %s, bfGain: %s)",
1845 detName, ampName, gainProvenanceString,
1846 gain, bfGain)
1847 boolGainMismatch = True
1848
1849 # Noise:
1850 # Try first with the empirical noise from the overscan.
1851 noiseProvenanceString = "amp"
1852 if self.config.doEmpiricalReadNoise and overscanResults is not None:
1853 noiseProvenanceString = "serial overscan"
1854 if isinstance(overscanResults.residualSigma, float):
1855 # Only serial overscan was run
1856 noise = overscanResults.residualSigma
1857 else:
1858 # Both serial and parallel overscan were
1859 # run. Only report noise from serial here.
1860 noise = overscanResults.residualSigma[0]
1861 elif self.config.usePtcReadNoise:
1862 # Try then with the PTC noise.
1863 noise = ptcDataset.noise[amp.getName()]
1864 noiseProvenanceString = "ptc"
1865 self.log.debug("Using noise from Photon Transfer Curve.")
1866 else:
1867 # Finally, try with the amplifier noise.
1868 # We already have a detector at this point. If there
1869 # was no detector to begin with, one would have
1870 # been created with self.config.gain and
1871 # self.config.noise.
1872 noise = amp.getReadNoise()
1873
1874 if math.isnan(gain):
1875 gain = 1.0
1876 self.log.warning("Gain for amp %s set to NAN! Updating to"
1877 " 1.0 to generate Poisson variance.", ampName)
1878 elif gain <= 0:
1879 patchedGain = 1.0
1880 self.log.warning("Gain for amp %s == %g <= 0; setting to %f.",
1881 ampName, gain, patchedGain)
1882 gain = patchedGain
1883
1884 effectivePtc.gain[ampName] = gain
1885 effectivePtc.noise[ampName] = noise
1886 # Make sure noise,turnoff, and gain make sense
1887 effectivePtc.validateGainNoiseTurnoffValues(ampName, doWarn=doWarningPtcValidation)
1888 doWarningPtcValidation = False
1889
1890 metadata[f"LSST GAIN {amp.getName()}"] = effectivePtc.gain[ampName]
1891 metadata[f"LSST READNOISE {amp.getName()}"] = effectivePtc.noise[ampName]
1892
1893 self.log.info("Det: %s - Noise provenance: %s, Gain provenance: %s",
1894 detName,
1895 noiseProvenanceString,
1896 gainProvenanceString)
1897 metadata["LSST ISR GAIN SOURCE"] = gainProvenanceString
1898 metadata["LSST ISR NOISE SOURCE"] = noiseProvenanceString
1899
1900 return effectivePtc
1901
1902 def ensureExposure(self, inputExp, camera=None, detectorNum=None):
1903 """Ensure that the data returned by Butler is a fully constructed exp.
1904
1905 ISR requires exposure-level image data for historical reasons, so if we
1906 did not recieve that from Butler, construct it from what we have,
1907 modifying the input in place.
1908
1909 Parameters
1910 ----------
1911 inputExp : `lsst.afw.image` image-type.
1912 The input data structure obtained from Butler.
1913 Can be `lsst.afw.image.Exposure`,
1914 `lsst.afw.image.DecoratedImageU`,
1915 or `lsst.afw.image.ImageF`
1916 camera : `lsst.afw.cameraGeom.camera`, optional
1917 The camera associated with the image. Used to find the appropriate
1918 detector if detector is not already set.
1919 detectorNum : `int`, optional
1920 The detector in the camera to attach, if the detector is not
1921 already set.
1922
1923 Returns
1924 -------
1925 inputExp : `lsst.afw.image.Exposure`
1926 The re-constructed exposure, with appropriate detector parameters.
1927
1928 Raises
1929 ------
1930 TypeError
1931 Raised if the input data cannot be used to construct an exposure.
1932 """
1933 if isinstance(inputExp, afwImage.DecoratedImageU):
1934 inputExp = afwImage.makeExposure(afwImage.makeMaskedImage(inputExp))
1935 elif isinstance(inputExp, afwImage.ImageF):
1936 inputExp = afwImage.makeExposure(afwImage.makeMaskedImage(inputExp))
1937 elif isinstance(inputExp, afwImage.MaskedImageF):
1938 inputExp = afwImage.makeExposure(inputExp)
1939 elif isinstance(inputExp, afwImage.Exposure):
1940 pass
1941 elif inputExp is None:
1942 # Assume this will be caught by the setup if it is a problem.
1943 return inputExp
1944 else:
1945 raise TypeError("Input Exposure is not known type in isrTask.ensureExposure: %s." %
1946 (type(inputExp), ))
1947
1948 if inputExp.getDetector() is None:
1949 if camera is None or detectorNum is None:
1950 raise RuntimeError('Must supply both a camera and detector number when using exposures '
1951 'without a detector set.')
1952 inputExp.setDetector(camera[detectorNum])
1953
1954 return inputExp
1955
1956 @staticmethod
1958 """Extract common calibration metadata values that will be written to
1959 output header.
1960
1961 Parameters
1962 ----------
1963 calib : `lsst.afw.image.Exposure` or `lsst.ip.isr.IsrCalib`
1964 Calibration to pull date information from.
1965
1966 Returns
1967 -------
1968 dateString : `str`
1969 Calibration creation date string to add to header.
1970 """
1971 if hasattr(calib, "getMetadata"):
1972 if 'CALIB_CREATION_DATE' in calib.getMetadata():
1973 return " ".join((calib.getMetadata().get("CALIB_CREATION_DATE", "Unknown"),
1974 calib.getMetadata().get("CALIB_CREATION_TIME", "Unknown")))
1975 else:
1976 return " ".join((calib.getMetadata().get("CALIB_CREATE_DATE", "Unknown"),
1977 calib.getMetadata().get("CALIB_CREATE_TIME", "Unknown")))
1978 else:
1979 return "Unknown Unknown"
1980
1981 def compareCameraKeywords(self, exposureMetadata, calib, calibName):
1982 """Compare header keywords to confirm camera states match.
1983
1984 Parameters
1985 ----------
1986 exposureMetadata : `lsst.daf.base.PropertySet`
1987 Header for the exposure being processed.
1988 calib : `lsst.afw.image.Exposure` or `lsst.ip.isr.IsrCalib`
1989 Calibration to be applied.
1990 calibName : `str`
1991 Calib type for log message.
1992 """
1993 try:
1994 calibMetadata = calib.getMetadata()
1995 except AttributeError:
1996 return
1997 for keyword in self.config.cameraKeywordsToCompare:
1998 if keyword in exposureMetadata and keyword in calibMetadata:
1999 if exposureMetadata[keyword] != calibMetadata[keyword]:
2000 if self.config.doRaiseOnCalibMismatch:
2001 raise RuntimeError("Sequencer mismatch for %s [%s]: exposure: %s calib: %s",
2002 calibName, keyword,
2003 exposureMetadata[keyword], calibMetadata[keyword])
2004 else:
2005 self.log.warning("Sequencer mismatch for %s [%s]: exposure: %s calib: %s",
2006 calibName, keyword,
2007 exposureMetadata[keyword], calibMetadata[keyword])
2008 else:
2009 self.log.debug("Sequencer keyword %s not found.", keyword)
2010
2011 def convertIntToFloat(self, exposure):
2012 """Convert exposure image from uint16 to float.
2013
2014 If the exposure does not need to be converted, the input is
2015 immediately returned. For exposures that are converted to use
2016 floating point pixels, the variance is set to unity and the
2017 mask to zero.
2018
2019 Parameters
2020 ----------
2021 exposure : `lsst.afw.image.Exposure`
2022 The raw exposure to be converted.
2023
2024 Returns
2025 -------
2026 newexposure : `lsst.afw.image.Exposure`
2027 The input ``exposure``, converted to floating point pixels.
2028
2029 Raises
2030 ------
2031 RuntimeError
2032 Raised if the exposure type cannot be converted to float.
2033
2034 """
2035 if isinstance(exposure, afwImage.ExposureF):
2036 # Nothing to be done
2037 self.log.debug("Exposure already of type float.")
2038 return exposure
2039 if not hasattr(exposure, "convertF"):
2040 raise RuntimeError("Unable to convert exposure (%s) to float." % type(exposure))
2041
2042 newexposure = exposure.convertF()
2043 newexposure.variance[:] = 1
2044 newexposure.mask[:] = 0x0
2045
2046 return newexposure
2047
2048 def maskAmplifier(self, ccdExposure, amp, defects):
2049 """Identify bad amplifiers, saturated and suspect pixels.
2050
2051 Parameters
2052 ----------
2053 ccdExposure : `lsst.afw.image.Exposure`
2054 Input exposure to be masked.
2055 amp : `lsst.afw.cameraGeom.Amplifier`
2056 Catalog of parameters defining the amplifier on this
2057 exposure to mask.
2058 defects : `lsst.ip.isr.Defects`
2059 List of defects. Used to determine if the entire
2060 amplifier is bad.
2061
2062 Returns
2063 -------
2064 badAmp : `Bool`
2065 If this is true, the entire amplifier area is covered by
2066 defects and unusable.
2067
2068 """
2069 maskedImage = ccdExposure.getMaskedImage()
2070
2071 badAmp = False
2072
2073 # Check if entire amp region is defined as a defect
2074 # NB: need to use amp.getBBox() for correct comparison with current
2075 # defects definition.
2076 if defects is not None:
2077 badAmp = bool(sum([v.getBBox().contains(amp.getBBox()) for v in defects]))
2078
2079 # In the case of a bad amp, we will set mask to "BAD"
2080 # (here use amp.getRawBBox() for correct association with pixels in
2081 # current ccdExposure).
2082 if badAmp:
2083 dataView = afwImage.MaskedImageF(maskedImage, amp.getRawBBox(),
2084 afwImage.PARENT)
2085 maskView = dataView.getMask()
2086 maskView |= maskView.getPlaneBitMask("BAD")
2087 del maskView
2088 return badAmp
2089
2090 # Mask remaining defects after assembleCcd() to allow for defects that
2091 # cross amplifier boundaries. Saturation and suspect pixels can be
2092 # masked now, though.
2093 limits = dict()
2094 if self.config.doSaturation and not badAmp:
2095 limits.update({self.config.saturatedMaskName: amp.getSaturation()})
2096 if self.config.doSuspect and not badAmp:
2097 limits.update({self.config.suspectMaskName: amp.getSuspectLevel()})
2098 if math.isfinite(self.config.saturation):
2099 limits.update({self.config.saturatedMaskName: self.config.saturation})
2100
2101 for maskName, maskThreshold in limits.items():
2102 if not math.isnan(maskThreshold):
2103 dataView = maskedImage.Factory(maskedImage, amp.getRawBBox())
2104 isrFunctions.makeThresholdMask(
2105 maskedImage=dataView,
2106 threshold=maskThreshold,
2107 growFootprints=0,
2108 maskName=maskName
2109 )
2110
2111 # Determine if we've fully masked this amplifier with SUSPECT and
2112 # SAT pixels.
2113 maskView = afwImage.Mask(maskedImage.getMask(), amp.getRawDataBBox(),
2114 afwImage.PARENT)
2115 maskVal = maskView.getPlaneBitMask([self.config.saturatedMaskName,
2116 self.config.suspectMaskName])
2117 if numpy.all(maskView.getArray() & maskVal > 0):
2118 badAmp = True
2119 maskView |= maskView.getPlaneBitMask("BAD")
2120
2121 return badAmp
2122
2123 def overscanCorrection(self, ccdExposure, amp):
2124 """Apply overscan correction in place.
2125
2126 This method does initial pixel rejection of the overscan
2127 region. The overscan can also be optionally segmented to
2128 allow for discontinuous overscan responses to be fit
2129 separately. The actual overscan subtraction is performed by
2130 the `lsst.ip.isr.overscan.OverscanTask`, which is called here
2131 after the amplifier is preprocessed.
2132
2133 Parameters
2134 ----------
2135 ccdExposure : `lsst.afw.image.Exposure`
2136 Exposure to have overscan correction performed.
2137 amp : `lsst.afw.cameraGeom.Amplifer`
2138 The amplifier to consider while correcting the overscan.
2139
2140 Returns
2141 -------
2142 overscanResults : `lsst.pipe.base.Struct`
2143 Result struct with components:
2144
2145 ``imageFit``
2146 Value or fit subtracted from the amplifier image data.
2147 (scalar or `lsst.afw.image.Image`)
2148 ``overscanFit``
2149 Value or fit subtracted from the overscan image data.
2150 (scalar or `lsst.afw.image.Image`)
2151 ``overscanImage``
2152 Image of the overscan region with the overscan
2153 correction applied. This quantity is used to estimate
2154 the amplifier read noise empirically.
2155 (`lsst.afw.image.Image`)
2156 ``edgeMask``
2157 Mask of the suspect pixels. (`lsst.afw.image.Mask`)
2158 ``overscanMean``
2159 Median overscan fit value. (`float`)
2160 ``overscanSigma``
2161 Clipped standard deviation of the overscan after
2162 correction. (`float`)
2163
2164 Raises
2165 ------
2166 RuntimeError
2167 Raised if the ``amp`` does not contain raw pixel information.
2168
2169 See Also
2170 --------
2171 lsst.ip.isr.overscan.OverscanTask
2172 """
2173 if amp.getRawHorizontalOverscanBBox().isEmpty():
2174 self.log.info("ISR_OSCAN: No overscan region. Not performing overscan correction.")
2175 return None
2176
2177 # Perform overscan correction on subregions.
2178 overscanResults = self.overscan.run(ccdExposure, amp)
2179
2180 metadata = ccdExposure.getMetadata()
2181 ampName = amp.getName()
2182
2183 keyBase = "LSST ISR OVERSCAN"
2184 # Updated quantities
2185 if isinstance(overscanResults.overscanMean, float):
2186 # Serial overscan correction only:
2187 metadata[f"{keyBase} SERIAL MEAN {ampName}"] = overscanResults.overscanMean
2188 metadata[f"{keyBase} SERIAL MEDIAN {ampName}"] = overscanResults.overscanMedian
2189 metadata[f"{keyBase} SERIAL STDEV {ampName}"] = overscanResults.overscanSigma
2190
2191 metadata[f"{keyBase} RESIDUAL SERIAL MEAN {ampName}"] = overscanResults.residualMean
2192 metadata[f"{keyBase} RESIDUAL SERIAL MEDIAN {ampName}"] = overscanResults.residualMedian
2193 metadata[f"{keyBase} RESIDUAL SERIAL STDEV {ampName}"] = overscanResults.residualSigma
2194 elif isinstance(overscanResults.overscanMean, tuple):
2195 # Both serial and parallel overscan have run:
2196 metadata[f"{keyBase} SERIAL MEAN {ampName}"] = overscanResults.overscanMean[0]
2197 metadata[f"{keyBase} SERIAL MEDIAN {ampName}"] = overscanResults.overscanMedian[0]
2198 metadata[f"{keyBase} SERIAL STDEV {ampName}"] = overscanResults.overscanSigma[0]
2199
2200 metadata[f"{keyBase} PARALLEL MEAN {ampName}"] = overscanResults.overscanMean[1]
2201 metadata[f"{keyBase} PARALLEL MEDIAN {ampName}"] = overscanResults.overscanMedian[1]
2202 metadata[f"{keyBase} PARALLEL STDEV {ampName}"] = overscanResults.overscanSigma[1]
2203
2204 metadata[f"{keyBase} RESIDUAL SERIAL MEAN {ampName}"] = overscanResults.residualMean[0]
2205 metadata[f"{keyBase} RESIDUAL SERIAL MEDIAN {ampName}"] = overscanResults.residualMedian[0]
2206 metadata[f"{keyBase} RESIDUAL SERIAL STDEV {ampName}"] = overscanResults.residualSigma[0]
2207
2208 metadata[f"{keyBase} RESIDUAL PARALLEL MEAN {ampName}"] = overscanResults.residualMean[1]
2209 metadata[f"{keyBase} RESIDUAL PARALLEL MEDIAN {ampName}"] = overscanResults.residualMedian[1]
2210 metadata[f"{keyBase} RESIDUAL PARALLEL STDEV {ampName}"] = overscanResults.residualSigma[1]
2211 else:
2212 self.log.warning("Unexpected type for overscan values; none added to header.")
2213
2214 return overscanResults
2215
2216 def updateVariance(self, ampExposure, amp, ptcDataset):
2217 """Set the variance plane using the gain and read noise
2218
2219 The read noise is calculated from the ``overscanImage`` if the
2220 ``doEmpiricalReadNoise`` option is set in the configuration; otherwise
2221 the value from the amplifier data is used.
2222
2223 Parameters
2224 ----------
2225 ampExposure : `lsst.afw.image.Exposure`
2226 Exposure to process.
2227 amp : `lsst.afw.cameraGeom.Amplifier` or `FakeAmp`
2228 Amplifier detector data.
2229 ptcDataset : `lsst.ip.isr.PhotonTransferCurveDataset`
2230 Effective PTC dataset containing the gains and read noise.
2231
2232 See also
2233 --------
2234 lsst.ip.isr.isrFunctions.updateVariance
2235 """
2236 ampName = amp.getName()
2237 # At this point, the effective PTC should have
2238 # gain and noise values.
2239 gain = ptcDataset.gain[ampName]
2240 readNoise = ptcDataset.noise[ampName]
2241
2242 isrFunctions.updateVariance(
2243 maskedImage=ampExposure.getMaskedImage(),
2244 gain=gain,
2245 readNoise=readNoise,
2246 )
2247
2248 def maskNegativeVariance(self, exposure):
2249 """Identify and mask pixels with negative variance values.
2250
2251 Parameters
2252 ----------
2253 exposure : `lsst.afw.image.Exposure`
2254 Exposure to process.
2255
2256 See Also
2257 --------
2258 lsst.ip.isr.isrFunctions.updateVariance
2259 """
2260 maskPlane = exposure.getMask().getPlaneBitMask(self.config.negativeVarianceMaskName)
2261 bad = numpy.where(exposure.getVariance().getArray() <= 0.0)
2262 exposure.mask.array[bad] |= maskPlane
2263
2264 def darkCorrection(self, exposure, darkExposure, invert=False):
2265 """Apply dark correction in place.
2266
2267 Parameters
2268 ----------
2269 exposure : `lsst.afw.image.Exposure`
2270 Exposure to process.
2271 darkExposure : `lsst.afw.image.Exposure`
2272 Dark exposure of the same size as ``exposure``.
2273 invert : `Bool`, optional
2274 If True, re-add the dark to an already corrected image.
2275
2276 Raises
2277 ------
2278 RuntimeError
2279 Raised if either ``exposure`` or ``darkExposure`` do not
2280 have their dark time defined.
2281
2282 See Also
2283 --------
2284 lsst.ip.isr.isrFunctions.darkCorrection
2285 """
2286 expScale = exposure.getInfo().getVisitInfo().getDarkTime()
2287 if math.isnan(expScale):
2288 raise RuntimeError("Exposure darktime is NAN.")
2289 if darkExposure.getInfo().getVisitInfo() is not None \
2290 and not math.isnan(darkExposure.getInfo().getVisitInfo().getDarkTime()):
2291 darkScale = darkExposure.getInfo().getVisitInfo().getDarkTime()
2292 else:
2293 # DM-17444: darkExposure.getInfo.getVisitInfo() is None
2294 # so getDarkTime() does not exist.
2295 self.log.warning("darkExposure.getInfo().getVisitInfo() does not exist. Using darkScale = 1.0.")
2296 darkScale = 1.0
2297
2298 isrFunctions.darkCorrection(
2299 maskedImage=exposure.getMaskedImage(),
2300 darkMaskedImage=darkExposure.getMaskedImage(),
2301 expScale=expScale,
2302 darkScale=darkScale,
2303 invert=invert,
2304 trimToFit=self.config.doTrimToMatchCalib
2305 )
2306
2307 def doLinearize(self, detector):
2308 """Check if linearization is needed for the detector cameraGeom.
2309
2310 Checks config.doLinearize and the linearity type of the first
2311 amplifier.
2312
2313 Parameters
2314 ----------
2315 detector : `lsst.afw.cameraGeom.Detector`
2316 Detector to get linearity type from.
2317
2318 Returns
2319 -------
2320 doLinearize : `Bool`
2321 If True, linearization should be performed.
2322 """
2323 return self.config.doLinearize and \
2324 detector.getAmplifiers()[0].getLinearityType() != NullLinearityType
2325
2326 def flatCorrection(self, exposure, flatExposure, invert=False):
2327 """Apply flat correction in place.
2328
2329 Parameters
2330 ----------
2331 exposure : `lsst.afw.image.Exposure`
2332 Exposure to process.
2333 flatExposure : `lsst.afw.image.Exposure`
2334 Flat exposure of the same size as ``exposure``.
2335 invert : `Bool`, optional
2336 If True, unflatten an already flattened image.
2337
2338 See Also
2339 --------
2340 lsst.ip.isr.isrFunctions.flatCorrection
2341 """
2342 isrFunctions.flatCorrection(
2343 maskedImage=exposure.getMaskedImage(),
2344 flatMaskedImage=flatExposure.getMaskedImage(),
2345 scalingType=self.config.flatScalingType,
2346 userScale=self.config.flatUserScale,
2347 invert=invert,
2348 trimToFit=self.config.doTrimToMatchCalib
2349 )
2350
2351 def saturationDetection(self, exposure, amp):
2352 """Detect and mask saturated pixels in config.saturatedMaskName.
2353
2354 Parameters
2355 ----------
2356 exposure : `lsst.afw.image.Exposure`
2357 Exposure to process. Only the amplifier DataSec is processed.
2358 amp : `lsst.afw.cameraGeom.Amplifier`
2359 Amplifier detector data.
2360
2361 See Also
2362 --------
2363 lsst.ip.isr.isrFunctions.makeThresholdMask
2364 """
2365 if not math.isnan(amp.getSaturation()):
2366 maskedImage = exposure.getMaskedImage()
2367 dataView = maskedImage.Factory(maskedImage, amp.getRawBBox())
2368 isrFunctions.makeThresholdMask(
2369 maskedImage=dataView,
2370 threshold=amp.getSaturation(),
2371 growFootprints=0,
2372 maskName=self.config.saturatedMaskName,
2373 )
2374
2375 def saturationInterpolation(self, exposure):
2376 """Interpolate over saturated pixels, in place.
2377
2378 This method should be called after `saturationDetection`, to
2379 ensure that the saturated pixels have been identified in the
2380 SAT mask. It should also be called after `assembleCcd`, since
2381 saturated regions may cross amplifier boundaries.
2382
2383 Parameters
2384 ----------
2385 exposure : `lsst.afw.image.Exposure`
2386 Exposure to process.
2387
2388 See Also
2389 --------
2390 lsst.ip.isr.isrTask.saturationDetection
2391 lsst.ip.isr.isrFunctions.interpolateFromMask
2392 """
2393 isrFunctions.interpolateFromMask(
2394 maskedImage=exposure.getMaskedImage(),
2395 fwhm=self.config.fwhm,
2396 growSaturatedFootprints=self.config.growSaturationFootprintSize,
2397 maskNameList=list(self.config.saturatedMaskName),
2398 )
2399
2400 def suspectDetection(self, exposure, amp):
2401 """Detect and mask suspect pixels in config.suspectMaskName.
2402
2403 Parameters
2404 ----------
2405 exposure : `lsst.afw.image.Exposure`
2406 Exposure to process. Only the amplifier DataSec is processed.
2407 amp : `lsst.afw.cameraGeom.Amplifier`
2408 Amplifier detector data.
2409
2410 See Also
2411 --------
2412 lsst.ip.isr.isrFunctions.makeThresholdMask
2413
2414 Notes
2415 -----
2416 Suspect pixels are pixels whose value is greater than
2417 amp.getSuspectLevel(). This is intended to indicate pixels that may be
2418 affected by unknown systematics; for example if non-linearity
2419 corrections above a certain level are unstable then that would be a
2420 useful value for suspectLevel. A value of `nan` indicates that no such
2421 level exists and no pixels are to be masked as suspicious.
2422 """
2423 suspectLevel = amp.getSuspectLevel()
2424 if math.isnan(suspectLevel):
2425 return
2426
2427 maskedImage = exposure.getMaskedImage()
2428 dataView = maskedImage.Factory(maskedImage, amp.getRawBBox())
2429 isrFunctions.makeThresholdMask(
2430 maskedImage=dataView,
2431 threshold=suspectLevel,
2432 growFootprints=0,
2433 maskName=self.config.suspectMaskName,
2434 )
2435
2436 def maskDefect(self, exposure, defectBaseList):
2437 """Mask defects using mask plane "BAD", in place.
2438
2439 Parameters
2440 ----------
2441 exposure : `lsst.afw.image.Exposure`
2442 Exposure to process.
2443 defectBaseList : defect-type
2444 List of defects to mask. Can be of type `lsst.ip.isr.Defects`
2445 or `list` of `lsst.afw.image.DefectBase`.
2446
2447 Notes
2448 -----
2449 Call this after CCD assembly, since defects may cross amplifier
2450 boundaries.
2451 """
2452 maskedImage = exposure.getMaskedImage()
2453 if not isinstance(defectBaseList, Defects):
2454 # Promotes DefectBase to Defect
2455 defectList = Defects(defectBaseList)
2456 else:
2457 defectList = defectBaseList
2458 defectList.maskPixels(maskedImage, maskName="BAD")
2459
2460 def maskEdges(self, exposure, numEdgePixels=0, maskPlane="SUSPECT", level='DETECTOR'):
2461 """Mask edge pixels with applicable mask plane.
2462
2463 Parameters
2464 ----------
2465 exposure : `lsst.afw.image.Exposure`
2466 Exposure to process.
2467 numEdgePixels : `int`, optional
2468 Number of edge pixels to mask.
2469 maskPlane : `str`, optional
2470 Mask plane name to use.
2471 level : `str`, optional
2472 Level at which to mask edges.
2473 """
2474 maskedImage = exposure.getMaskedImage()
2475 maskBitMask = maskedImage.getMask().getPlaneBitMask(maskPlane)
2476
2477 if numEdgePixels > 0:
2478 if level == 'DETECTOR':
2479 boxes = [maskedImage.getBBox()]
2480 elif level == 'AMP':
2481 boxes = [amp.getBBox() for amp in exposure.getDetector()]
2482
2483 for box in boxes:
2484 # This makes a bbox numEdgeSuspect pixels smaller than the
2485 # image on each side
2486 subImage = maskedImage[box]
2487 box.grow(-numEdgePixels)
2488 # Mask pixels outside box
2489 SourceDetectionTask.setEdgeBits(
2490 subImage,
2491 box,
2492 maskBitMask)
2493
2494 def maskAndInterpolateDefects(self, exposure, defectBaseList):
2495 """Mask and interpolate defects using mask plane "BAD", in place.
2496
2497 Parameters
2498 ----------
2499 exposure : `lsst.afw.image.Exposure`
2500 Exposure to process.
2501 defectBaseList : defects-like
2502 List of defects to mask and interpolate. Can be
2503 `lsst.ip.isr.Defects` or `list` of `lsst.afw.image.DefectBase`.
2504
2505 See Also
2506 --------
2507 lsst.ip.isr.isrTask.maskDefect
2508 """
2509 self.maskDefect(exposure, defectBaseList)
2510 self.maskEdges(exposure, numEdgePixels=self.config.numEdgeSuspect,
2511 maskPlane="SUSPECT", level=self.config.edgeMaskLevel)
2512 isrFunctions.interpolateFromMask(
2513 maskedImage=exposure.getMaskedImage(),
2514 fwhm=self.config.fwhm,
2515 growSaturatedFootprints=0,
2516 maskNameList=["BAD"],
2517 )
2518
2519 def maskNan(self, exposure):
2520 """Mask NaNs using mask plane "UNMASKEDNAN", in place.
2521
2522 Parameters
2523 ----------
2524 exposure : `lsst.afw.image.Exposure`
2525 Exposure to process.
2526
2527 Notes
2528 -----
2529 We mask over all non-finite values (NaN, inf), including those
2530 that are masked with other bits (because those may or may not be
2531 interpolated over later, and we want to remove all NaN/infs).
2532 Despite this behaviour, the "UNMASKEDNAN" mask plane is used to
2533 preserve the historical name.
2534 """
2535 maskedImage = exposure.getMaskedImage()
2536
2537 # Find and mask NaNs
2538 maskedImage.getMask().addMaskPlane("UNMASKEDNAN")
2539 maskVal = maskedImage.getMask().getPlaneBitMask("UNMASKEDNAN")
2540 numNans = maskNans(maskedImage, maskVal)
2541 self.metadata["NUMNANS"] = numNans
2542 if numNans > 0:
2543 self.log.warning("There were %d unmasked NaNs.", numNans)
2544
2545 def maskAndInterpolateNan(self, exposure):
2546 """"Mask and interpolate NaN/infs using mask plane "UNMASKEDNAN",
2547 in place.
2548
2549 Parameters
2550 ----------
2551 exposure : `lsst.afw.image.Exposure`
2552 Exposure to process.
2553
2554 See Also
2555 --------
2556 lsst.ip.isr.isrTask.maskNan
2557 """
2558 self.maskNan(exposure)
2559 isrFunctions.interpolateFromMask(
2560 maskedImage=exposure.getMaskedImage(),
2561 fwhm=self.config.fwhm,
2562 growSaturatedFootprints=0,
2563 maskNameList=["UNMASKEDNAN"],
2564 )
2565
2566 def measureBackground(self, exposure, IsrQaConfig=None):
2567 """Measure the image background in subgrids, for quality control.
2568
2569 Parameters
2570 ----------
2571 exposure : `lsst.afw.image.Exposure`
2572 Exposure to process.
2573 IsrQaConfig : `lsst.ip.isr.isrQa.IsrQaConfig`
2574 Configuration object containing parameters on which background
2575 statistics and subgrids to use.
2576 """
2577 if IsrQaConfig is not None:
2578 statsControl = afwMath.StatisticsControl(IsrQaConfig.flatness.clipSigma,
2579 IsrQaConfig.flatness.nIter)
2580 maskVal = exposure.getMaskedImage().getMask().getPlaneBitMask(["BAD", "SAT", "DETECTED"])
2581 statsControl.setAndMask(maskVal)
2582 maskedImage = exposure.getMaskedImage()
2583 stats = afwMath.makeStatistics(maskedImage, afwMath.MEDIAN | afwMath.STDEVCLIP, statsControl)
2584 skyLevel = stats.getValue(afwMath.MEDIAN)
2585 skySigma = stats.getValue(afwMath.STDEVCLIP)
2586 self.log.info("Flattened sky level: %f +/- %f.", skyLevel, skySigma)
2587 metadata = exposure.getMetadata()
2588 metadata["SKYLEVEL"] = skyLevel
2589 metadata["SKYSIGMA"] = skySigma
2590
2591 # calcluating flatlevel over the subgrids
2592 stat = afwMath.MEANCLIP if IsrQaConfig.flatness.doClip else afwMath.MEAN
2593 meshXHalf = int(IsrQaConfig.flatness.meshX/2.)
2594 meshYHalf = int(IsrQaConfig.flatness.meshY/2.)
2595 nX = int((exposure.getWidth() + meshXHalf) / IsrQaConfig.flatness.meshX)
2596 nY = int((exposure.getHeight() + meshYHalf) / IsrQaConfig.flatness.meshY)
2597 skyLevels = numpy.zeros((nX, nY))
2598
2599 for j in range(nY):
2600 yc = meshYHalf + j * IsrQaConfig.flatness.meshY
2601 for i in range(nX):
2602 xc = meshXHalf + i * IsrQaConfig.flatness.meshX
2603
2604 xLLC = xc - meshXHalf
2605 yLLC = yc - meshYHalf
2606 xURC = xc + meshXHalf - 1
2607 yURC = yc + meshYHalf - 1
2608
2609 bbox = lsst.geom.Box2I(lsst.geom.Point2I(xLLC, yLLC), lsst.geom.Point2I(xURC, yURC))
2610 miMesh = maskedImage.Factory(exposure.getMaskedImage(), bbox, afwImage.LOCAL)
2611
2612 skyLevels[i, j] = afwMath.makeStatistics(miMesh, stat, statsControl).getValue()
2613
2614 good = numpy.where(numpy.isfinite(skyLevels))
2615 skyMedian = numpy.median(skyLevels[good])
2616 flatness = (skyLevels[good] - skyMedian) / skyMedian
2617 flatness_rms = numpy.std(flatness)
2618 flatness_pp = flatness.max() - flatness.min() if len(flatness) > 0 else numpy.nan
2619
2620 self.log.info("Measuring sky levels in %dx%d grids: %f.", nX, nY, skyMedian)
2621 self.log.info("Sky flatness in %dx%d grids - pp: %f rms: %f.",
2622 nX, nY, flatness_pp, flatness_rms)
2623
2624 metadata["FLATNESS_PP"] = float(flatness_pp)
2625 metadata["FLATNESS_RMS"] = float(flatness_rms)
2626 metadata["FLATNESS_NGRIDS"] = '%dx%d' % (nX, nY)
2627 metadata["FLATNESS_MESHX"] = IsrQaConfig.flatness.meshX
2628 metadata["FLATNESS_MESHY"] = IsrQaConfig.flatness.meshY
2629
2630 def roughZeroPoint(self, exposure):
2631 """Set an approximate magnitude zero point for the exposure.
2632
2633 Parameters
2634 ----------
2635 exposure : `lsst.afw.image.Exposure`
2636 Exposure to process.
2637 """
2638 filterLabel = exposure.getFilter()
2639 physicalFilter = isrFunctions.getPhysicalFilter(filterLabel, self.log)
2640
2641 if physicalFilter in self.config.fluxMag0T1:
2642 fluxMag0 = self.config.fluxMag0T1[physicalFilter]
2643 else:
2644 self.log.warning("No rough magnitude zero point defined for filter %s.", physicalFilter)
2645 fluxMag0 = self.config.defaultFluxMag0T1
2646
2647 expTime = exposure.getInfo().getVisitInfo().getExposureTime()
2648 if not expTime > 0: # handle NaN as well as <= 0
2649 self.log.warning("Non-positive exposure time; skipping rough zero point.")
2650 return
2651
2652 self.log.info("Setting rough magnitude zero point for filter %s: %f",
2653 physicalFilter, 2.5*math.log10(fluxMag0*expTime))
2654 exposure.setPhotoCalib(afwImage.makePhotoCalibFromCalibZeroPoint(fluxMag0*expTime, 0.0))
2655
2656 @contextmanager
2657 def flatContext(self, exp, flat, dark=None):
2658 """Context manager that applies and removes flats and darks,
2659 if the task is configured to apply them.
2660
2661 Parameters
2662 ----------
2663 exp : `lsst.afw.image.Exposure`
2664 Exposure to process.
2665 flat : `lsst.afw.image.Exposure`
2666 Flat exposure the same size as ``exp``.
2667 dark : `lsst.afw.image.Exposure`, optional
2668 Dark exposure the same size as ``exp``.
2669
2670 Yields
2671 ------
2672 exp : `lsst.afw.image.Exposure`
2673 The flat and dark corrected exposure.
2674 """
2675 if self.config.doDark and dark is not None:
2676 self.darkCorrection(exp, dark)
2677 if self.config.doFlat:
2678 self.flatCorrection(exp, flat)
2679 try:
2680 yield exp
2681 finally:
2682 if self.config.doFlat:
2683 self.flatCorrection(exp, flat, invert=True)
2684 if self.config.doDark and dark is not None:
2685 self.darkCorrection(exp, dark, invert=True)
2686
2687 def makeBinnedImages(self, exposure):
2688 """Make visualizeVisit style binned exposures.
2689
2690 Parameters
2691 ----------
2692 exposure : `lsst.afw.image.Exposure`
2693 Exposure to bin.
2694
2695 Returns
2696 -------
2697 bin1 : `lsst.afw.image.Exposure`
2698 Binned exposure using binFactor1.
2699 bin2 : `lsst.afw.image.Exposure`
2700 Binned exposure using binFactor2.
2701 """
2702 mi = exposure.getMaskedImage()
2703
2704 bin1 = afwMath.binImage(mi, self.config.binFactor1)
2705 bin2 = afwMath.binImage(mi, self.config.binFactor2)
2706
2707 return bin1, bin2
2708
2709 def debugView(self, exposure, stepname):
2710 """Utility function to examine ISR exposure at different stages.
2711
2712 Parameters
2713 ----------
2714 exposure : `lsst.afw.image.Exposure`
2715 Exposure to view.
2716 stepname : `str`
2717 State of processing to view.
2718 """
2719 frame = getDebugFrame(self._display, stepname)
2720 if frame:
2721 display = getDisplay(frame)
2722 display.scale('asinh', 'zscale')
2723 display.mtv(exposure)
2724 prompt = "Press Enter to continue [c]... "
2725 while True:
2726 ans = input(prompt).lower()
2727 if ans in ("", "c",):
2728 break
2729
2730
2731class FakeAmp(object):
2732 """A Detector-like object that supports returning gain and saturation level
2733
2734 This is used when the input exposure does not have a detector.
2735
2736 Parameters
2737 ----------
2738 exposure : `lsst.afw.image.Exposure`
2739 Exposure to generate a fake amplifier for.
2740 config : `lsst.ip.isr.isrTaskConfig`
2741 Configuration to apply to the fake amplifier.
2742 """
2743
2744 def __init__(self, exposure, config):
2745 self._bbox = exposure.getBBox(afwImage.LOCAL)
2747 self._gain = config.gain
2748 self._readNoise = config.readNoise
2749 self._saturation = config.saturation
2750
2751 def getBBox(self):
2752 return self._bbox
2753
2754 def getRawBBox(self):
2755 return self._bbox
2756
2760 def getGain(self):
2761 return self._gain
2762
2763 def getReadNoise(self):
2764 return self._readNoise
2765
2766 def getSaturation(self):
2767 return self._saturation
2768
2770 return float("NaN")
__init__(self, exposure, config)
Definition isrTask.py:2744
run(self, ccdExposure, *camera=None, bias=None, linearizer=None, crosstalk=None, crosstalkSources=None, dark=None, flat=None, ptc=None, bfKernel=None, bfGains=None, defects=None, fringes=pipeBase.Struct(fringes=None), opticsTransmission=None, filterTransmission=None, sensorTransmission=None, atmosphereTransmission=None, detectorNum=None, strayLightData=None, illumMaskedImage=None, deferredChargeCalib=None)
Definition isrTask.py:1184
maskEdges(self, exposure, numEdgePixels=0, maskPlane="SUSPECT", level='DETECTOR')
Definition isrTask.py:2460
updateVariance(self, ampExposure, amp, ptcDataset)
Definition isrTask.py:2216
maskAndInterpolateDefects(self, exposure, defectBaseList)
Definition isrTask.py:2494
roughZeroPoint(self, exposure)
Definition isrTask.py:2630
defineEffectivePtc(self, ptcDataset, detector, bfGains, overScans, metadata)
Definition isrTask.py:1782
runQuantum(self, butlerQC, inputRefs, outputRefs)
Definition isrTask.py:1023
ensureExposure(self, inputExp, camera=None, detectorNum=None)
Definition isrTask.py:1902
maskDefect(self, exposure, defectBaseList)
Definition isrTask.py:2436
convertIntToFloat(self, exposure)
Definition isrTask.py:2011
maskAndInterpolateNan(self, exposure)
Definition isrTask.py:2545
flatCorrection(self, exposure, flatExposure, invert=False)
Definition isrTask.py:2326
debugView(self, exposure, stepname)
Definition isrTask.py:2709
measureBackground(self, exposure, IsrQaConfig=None)
Definition isrTask.py:2566
compareCameraKeywords(self, exposureMetadata, calib, calibName)
Definition isrTask.py:1981
maskAmplifier(self, ccdExposure, amp, defects)
Definition isrTask.py:2048
saturationInterpolation(self, exposure)
Definition isrTask.py:2375
saturationDetection(self, exposure, amp)
Definition isrTask.py:2351
doLinearize(self, detector)
Definition isrTask.py:2307
darkCorrection(self, exposure, darkExposure, invert=False)
Definition isrTask.py:2264
suspectDetection(self, exposure, amp)
Definition isrTask.py:2400
overscanCorrection(self, ccdExposure, amp)
Definition isrTask.py:2123
makeBinnedImages(self, exposure)
Definition isrTask.py:2687
flatContext(self, exp, flat, dark=None)
Definition isrTask.py:2657
maskNegativeVariance(self, exposure)
Definition isrTask.py:2248
crosstalkSourceLookup(datasetType, registry, quantumDataId, collections)
Definition isrTask.py:61