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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/>.
22import math
23import numpy
25import lsst.geom
26import lsst.afw.image as afwImage
27import lsst.afw.math as afwMath
28import lsst.pex.config as pexConfig
29import lsst.pipe.base as pipeBase
30import lsst.pipe.base.connectionTypes as cT
32from contextlib import contextmanager
33from lsstDebug import getDebugFrame
35from lsst.afw.cameraGeom import (PIXELS, FOCAL_PLANE, NullLinearityType,
36 ReadoutCorner)
37from lsst.afw.display import getDisplay
38from lsst.afw.geom import Polygon
39from lsst.daf.persistence import ButlerDataRef
40from lsst.daf.persistence.butler import NoResults
41from lsst.meas.algorithms.detection import SourceDetectionTask
42from lsst.meas.algorithms import Defects
44from . import isrFunctions
45from . import isrQa
46from . import linearize
48from .assembleCcdTask import AssembleCcdTask
49from .crosstalk import CrosstalkTask, CrosstalkCalib
50from .fringe import FringeTask
51from .isr import maskNans
52from .masking import MaskingTask
53from .overscan import OverscanCorrectionTask
54from .straylight import StrayLightTask
55from .vignette import VignetteTask
56from lsst.daf.butler import DimensionGraph
59__all__ = ["IsrTask", "IsrTaskConfig", "RunIsrTask", "RunIsrConfig"]
62def crosstalkSourceLookup(datasetType, registry, quantumDataId, collections):
63 """Lookup function to identify crosstalkSource entries.
65 This should return an empty list under most circumstances. Only
66 when inter-chip crosstalk has been identified should this be
67 populated.
69 This will be unused until DM-25348 resolves the quantum graph
70 generation issue.
72 Parameters
73 ----------
74 datasetType : `str`
75 Dataset to lookup.
76 registry : `lsst.daf.butler.Registry`
77 Butler registry to query.
78 quantumDataId : `lsst.daf.butler.ExpandedDataCoordinate`
79 Data id to transform to identify crosstalkSources. The
80 ``detector`` entry will be stripped.
81 collections : `lsst.daf.butler.CollectionSearch`
82 Collections to search through.
84 Returns
85 -------
86 results : `list` [`lsst.daf.butler.DatasetRef`]
87 List of datasets that match the query that will be used as
88 crosstalkSources.
89 """
90 newDataId = quantumDataId.subset(DimensionGraph(registry.dimensions, names=["instrument", "exposure"]))
91 results = list(registry.queryDatasets(datasetType,
92 collections=collections,
93 dataId=newDataId,
94 findFirst=True,
95 ).expanded())
96 return results
99class IsrTaskConnections(pipeBase.PipelineTaskConnections,
100 dimensions={"instrument", "exposure", "detector"},
101 defaultTemplates={}):
102 ccdExposure = cT.Input(
103 name="raw",
104 doc="Input exposure to process.",
105 storageClass="Exposure",
106 dimensions=["instrument", "exposure", "detector"],
107 )
108 camera = cT.PrerequisiteInput(
109 name="camera",
110 storageClass="Camera",
111 doc="Input camera to construct complete exposures.",
112 dimensions=["instrument"],
113 isCalibration=True,
114 )
116 crosstalk = cT.PrerequisiteInput(
117 name="crosstalk",
118 doc="Input crosstalk object",
119 storageClass="CrosstalkCalib",
120 dimensions=["instrument", "detector"],
121 isCalibration=True,
122 )
123 # TODO: DM-25348. This does not work yet to correctly load
124 # possible crosstalk sources.
125 crosstalkSources = cT.PrerequisiteInput(
126 name="isrOverscanCorrected",
127 doc="Overscan corrected input images.",
128 storageClass="Exposure",
129 dimensions=["instrument", "exposure", "detector"],
130 deferLoad=True,
131 multiple=True,
132 lookupFunction=crosstalkSourceLookup,
133 )
134 bias = cT.PrerequisiteInput(
135 name="bias",
136 doc="Input bias calibration.",
137 storageClass="ExposureF",
138 dimensions=["instrument", "detector"],
139 isCalibration=True,
140 )
141 dark = cT.PrerequisiteInput(
142 name='dark',
143 doc="Input dark calibration.",
144 storageClass="ExposureF",
145 dimensions=["instrument", "detector"],
146 isCalibration=True,
147 )
148 flat = cT.PrerequisiteInput(
149 name="flat",
150 doc="Input flat calibration.",
151 storageClass="ExposureF",
152 dimensions=["instrument", "physical_filter", "detector"],
153 isCalibration=True,
154 )
155 fringes = cT.PrerequisiteInput(
156 name="fringe",
157 doc="Input fringe calibration.",
158 storageClass="ExposureF",
159 dimensions=["instrument", "physical_filter", "detector"],
160 isCalibration=True,
161 )
162 strayLightData = cT.PrerequisiteInput(
163 name='yBackground',
164 doc="Input stray light calibration.",
165 storageClass="StrayLightData",
166 dimensions=["instrument", "physical_filter", "detector"],
167 isCalibration=True,
168 )
169 bfKernel = cT.PrerequisiteInput(
170 name='bfKernel',
171 doc="Input brighter-fatter kernel.",
172 storageClass="NumpyArray",
173 dimensions=["instrument"],
174 isCalibration=True,
175 )
176 newBFKernel = cT.PrerequisiteInput(
177 name='brighterFatterKernel',
178 doc="Newer complete kernel + gain solutions.",
179 storageClass="BrighterFatterKernel",
180 dimensions=["instrument", "detector"],
181 isCalibration=True,
182 )
183 defects = cT.PrerequisiteInput(
184 name='defects',
185 doc="Input defect tables.",
186 storageClass="Defects",
187 dimensions=["instrument", "detector"],
188 isCalibration=True,
189 )
190 opticsTransmission = cT.PrerequisiteInput(
191 name="transmission_optics",
192 storageClass="TransmissionCurve",
193 doc="Transmission curve due to the optics.",
194 dimensions=["instrument"],
195 isCalibration=True,
196 )
197 filterTransmission = cT.PrerequisiteInput(
198 name="transmission_filter",
199 storageClass="TransmissionCurve",
200 doc="Transmission curve due to the filter.",
201 dimensions=["instrument", "physical_filter"],
202 isCalibration=True,
203 )
204 sensorTransmission = cT.PrerequisiteInput(
205 name="transmission_sensor",
206 storageClass="TransmissionCurve",
207 doc="Transmission curve due to the sensor.",
208 dimensions=["instrument", "detector"],
209 isCalibration=True,
210 )
211 atmosphereTransmission = cT.PrerequisiteInput(
212 name="transmission_atmosphere",
213 storageClass="TransmissionCurve",
214 doc="Transmission curve due to the atmosphere.",
215 dimensions=["instrument"],
216 isCalibration=True,
217 )
218 illumMaskedImage = cT.PrerequisiteInput(
219 name="illum",
220 doc="Input illumination correction.",
221 storageClass="MaskedImageF",
222 dimensions=["instrument", "physical_filter", "detector"],
223 isCalibration=True,
224 )
226 outputExposure = cT.Output(
227 name='postISRCCD',
228 doc="Output ISR processed exposure.",
229 storageClass="Exposure",
230 dimensions=["instrument", "exposure", "detector"],
231 )
232 preInterpExposure = cT.Output(
233 name='preInterpISRCCD',
234 doc="Output ISR processed exposure, with pixels left uninterpolated.",
235 storageClass="ExposureF",
236 dimensions=["instrument", "exposure", "detector"],
237 )
238 outputOssThumbnail = cT.Output(
239 name="OssThumb",
240 doc="Output Overscan-subtracted thumbnail image.",
241 storageClass="Thumbnail",
242 dimensions=["instrument", "exposure", "detector"],
243 )
244 outputFlattenedThumbnail = cT.Output(
245 name="FlattenedThumb",
246 doc="Output flat-corrected thumbnail image.",
247 storageClass="Thumbnail",
248 dimensions=["instrument", "exposure", "detector"],
249 )
251 def __init__(self, *, config=None):
252 super().__init__(config=config)
254 if config.doBias is not True:
255 self.prerequisiteInputs.discard("bias")
256 if config.doLinearize is not True:
257 self.prerequisiteInputs.discard("linearizer")
258 if config.doCrosstalk is not True:
259 self.inputs.discard("crosstalkSources")
260 self.prerequisiteInputs.discard("crosstalk")
261 if config.doBrighterFatter is not True:
262 self.prerequisiteInputs.discard("bfKernel")
263 self.prerequisiteInputs.discard("newBFKernel")
264 if config.doDefect is not True:
265 self.prerequisiteInputs.discard("defects")
266 if config.doDark is not True:
267 self.prerequisiteInputs.discard("dark")
268 if config.doFlat is not True:
269 self.prerequisiteInputs.discard("flat")
270 if config.doAttachTransmissionCurve is not True:
271 self.prerequisiteInputs.discard("opticsTransmission")
272 self.prerequisiteInputs.discard("filterTransmission")
273 self.prerequisiteInputs.discard("sensorTransmission")
274 self.prerequisiteInputs.discard("atmosphereTransmission")
275 if config.doUseOpticsTransmission is not True:
276 self.prerequisiteInputs.discard("opticsTransmission")
277 if config.doUseFilterTransmission is not True:
278 self.prerequisiteInputs.discard("filterTransmission")
279 if config.doUseSensorTransmission is not True:
280 self.prerequisiteInputs.discard("sensorTransmission")
281 if config.doUseAtmosphereTransmission is not True:
282 self.prerequisiteInputs.discard("atmosphereTransmission")
283 if config.doIlluminationCorrection is not True:
284 self.prerequisiteInputs.discard("illumMaskedImage")
286 if config.doWrite is not True:
287 self.outputs.discard("outputExposure")
288 self.outputs.discard("preInterpExposure")
289 self.outputs.discard("outputFlattenedThumbnail")
290 self.outputs.discard("outputOssThumbnail")
291 if config.doSaveInterpPixels is not True:
292 self.outputs.discard("preInterpExposure")
293 if config.qa.doThumbnailOss is not True:
294 self.outputs.discard("outputOssThumbnail")
295 if config.qa.doThumbnailFlattened is not True:
296 self.outputs.discard("outputFlattenedThumbnail")
299class IsrTaskConfig(pipeBase.PipelineTaskConfig,
300 pipelineConnections=IsrTaskConnections):
301 """Configuration parameters for IsrTask.
303 Items are grouped in the order in which they are executed by the task.
304 """
305 datasetType = pexConfig.Field(
306 dtype=str,
307 doc="Dataset type for input data; users will typically leave this alone, "
308 "but camera-specific ISR tasks will override it",
309 default="raw",
310 )
312 fallbackFilterName = pexConfig.Field(
313 dtype=str,
314 doc="Fallback default filter name for calibrations.",
315 optional=True
316 )
317 useFallbackDate = pexConfig.Field(
318 dtype=bool,
319 doc="Pass observation date when using fallback filter.",
320 default=False,
321 )
322 expectWcs = pexConfig.Field(
323 dtype=bool,
324 default=True,
325 doc="Expect input science images to have a WCS (set False for e.g. spectrographs)."
326 )
327 fwhm = pexConfig.Field(
328 dtype=float,
329 doc="FWHM of PSF in arcseconds.",
330 default=1.0,
331 )
332 qa = pexConfig.ConfigField(
333 dtype=isrQa.IsrQaConfig,
334 doc="QA related configuration options.",
335 )
337 # Image conversion configuration
338 doConvertIntToFloat = pexConfig.Field(
339 dtype=bool,
340 doc="Convert integer raw images to floating point values?",
341 default=True,
342 )
344 # Saturated pixel handling.
345 doSaturation = pexConfig.Field(
346 dtype=bool,
347 doc="Mask saturated pixels? NB: this is totally independent of the"
348 " interpolation option - this is ONLY setting the bits in the mask."
349 " To have them interpolated make sure doSaturationInterpolation=True",
350 default=True,
351 )
352 saturatedMaskName = pexConfig.Field(
353 dtype=str,
354 doc="Name of mask plane to use in saturation detection and interpolation",
355 default="SAT",
356 )
357 saturation = pexConfig.Field(
358 dtype=float,
359 doc="The saturation level to use if no Detector is present in the Exposure (ignored if NaN)",
360 default=float("NaN"),
361 )
362 growSaturationFootprintSize = pexConfig.Field(
363 dtype=int,
364 doc="Number of pixels by which to grow the saturation footprints",
365 default=1,
366 )
368 # Suspect pixel handling.
369 doSuspect = pexConfig.Field(
370 dtype=bool,
371 doc="Mask suspect pixels?",
372 default=False,
373 )
374 suspectMaskName = pexConfig.Field(
375 dtype=str,
376 doc="Name of mask plane to use for suspect pixels",
377 default="SUSPECT",
378 )
379 numEdgeSuspect = pexConfig.Field(
380 dtype=int,
381 doc="Number of edge pixels to be flagged as untrustworthy.",
382 default=0,
383 )
384 edgeMaskLevel = pexConfig.ChoiceField(
385 dtype=str,
386 doc="Mask edge pixels in which coordinate frame: DETECTOR or AMP?",
387 default="DETECTOR",
388 allowed={
389 'DETECTOR': 'Mask only the edges of the full detector.',
390 'AMP': 'Mask edges of each amplifier.',
391 },
392 )
394 # Initial masking options.
395 doSetBadRegions = pexConfig.Field(
396 dtype=bool,
397 doc="Should we set the level of all BAD patches of the chip to the chip's average value?",
398 default=True,
399 )
400 badStatistic = pexConfig.ChoiceField(
401 dtype=str,
402 doc="How to estimate the average value for BAD regions.",
403 default='MEANCLIP',
404 allowed={
405 "MEANCLIP": "Correct using the (clipped) mean of good data",
406 "MEDIAN": "Correct using the median of the good data",
407 },
408 )
410 # Overscan subtraction configuration.
411 doOverscan = pexConfig.Field(
412 dtype=bool,
413 doc="Do overscan subtraction?",
414 default=True,
415 )
416 overscan = pexConfig.ConfigurableField(
417 target=OverscanCorrectionTask,
418 doc="Overscan subtraction task for image segments.",
419 )
421 overscanFitType = pexConfig.ChoiceField(
422 dtype=str,
423 doc="The method for fitting the overscan bias level.",
424 default='MEDIAN',
425 allowed={
426 "POLY": "Fit ordinary polynomial to the longest axis of the overscan region",
427 "CHEB": "Fit Chebyshev polynomial to the longest axis of the overscan region",
428 "LEG": "Fit Legendre polynomial to the longest axis of the overscan region",
429 "NATURAL_SPLINE": "Fit natural spline to the longest axis of the overscan region",
430 "CUBIC_SPLINE": "Fit cubic spline to the longest axis of the overscan region",
431 "AKIMA_SPLINE": "Fit Akima spline to the longest axis of the overscan region",
432 "MEAN": "Correct using the mean of the overscan region",
433 "MEANCLIP": "Correct using a clipped mean of the overscan region",
434 "MEDIAN": "Correct using the median of the overscan region",
435 "MEDIAN_PER_ROW": "Correct using the median per row of the overscan region",
436 },
437 deprecated=("Please configure overscan via the OverscanCorrectionConfig interface."
438 " This option will no longer be used, and will be removed after v20.")
439 )
440 overscanOrder = pexConfig.Field(
441 dtype=int,
442 doc=("Order of polynomial or to fit if overscan fit type is a polynomial, "
443 "or number of spline knots if overscan fit type is a spline."),
444 default=1,
445 deprecated=("Please configure overscan via the OverscanCorrectionConfig interface."
446 " This option will no longer be used, and will be removed after v20.")
447 )
448 overscanNumSigmaClip = pexConfig.Field(
449 dtype=float,
450 doc="Rejection threshold (sigma) for collapsing overscan before fit",
451 default=3.0,
452 deprecated=("Please configure overscan via the OverscanCorrectionConfig interface."
453 " This option will no longer be used, and will be removed after v20.")
454 )
455 overscanIsInt = pexConfig.Field(
456 dtype=bool,
457 doc="Treat overscan as an integer image for purposes of overscan.FitType=MEDIAN"
458 " and overscan.FitType=MEDIAN_PER_ROW.",
459 default=True,
460 deprecated=("Please configure overscan via the OverscanCorrectionConfig interface."
461 " This option will no longer be used, and will be removed after v20.")
462 )
463 # These options do not get deprecated, as they define how we slice up the image data.
464 overscanNumLeadingColumnsToSkip = pexConfig.Field(
465 dtype=int,
466 doc="Number of columns to skip in overscan, i.e. those closest to amplifier",
467 default=0,
468 )
469 overscanNumTrailingColumnsToSkip = pexConfig.Field(
470 dtype=int,
471 doc="Number of columns to skip in overscan, i.e. those farthest from amplifier",
472 default=0,
473 )
474 overscanMaxDev = pexConfig.Field( 474 ↛ exitline 474 didn't jump to the function exit
475 dtype=float,
476 doc="Maximum deviation from the median for overscan",
477 default=1000.0, check=lambda x: x > 0
478 )
479 overscanBiasJump = pexConfig.Field(
480 dtype=bool,
481 doc="Fit the overscan in a piecewise-fashion to correct for bias jumps?",
482 default=False,
483 )
484 overscanBiasJumpKeyword = pexConfig.Field(
485 dtype=str,
486 doc="Header keyword containing information about devices.",
487 default="NO_SUCH_KEY",
488 )
489 overscanBiasJumpDevices = pexConfig.ListField(
490 dtype=str,
491 doc="List of devices that need piecewise overscan correction.",
492 default=(),
493 )
494 overscanBiasJumpLocation = pexConfig.Field(
495 dtype=int,
496 doc="Location of bias jump along y-axis.",
497 default=0,
498 )
500 # Amplifier to CCD assembly configuration
501 doAssembleCcd = pexConfig.Field(
502 dtype=bool,
503 default=True,
504 doc="Assemble amp-level exposures into a ccd-level exposure?"
505 )
506 assembleCcd = pexConfig.ConfigurableField(
507 target=AssembleCcdTask,
508 doc="CCD assembly task",
509 )
511 # General calibration configuration.
512 doAssembleIsrExposures = pexConfig.Field(
513 dtype=bool,
514 default=False,
515 doc="Assemble amp-level calibration exposures into ccd-level exposure?"
516 )
517 doTrimToMatchCalib = pexConfig.Field(
518 dtype=bool,
519 default=False,
520 doc="Trim raw data to match calibration bounding boxes?"
521 )
523 # Bias subtraction.
524 doBias = pexConfig.Field(
525 dtype=bool,
526 doc="Apply bias frame correction?",
527 default=True,
528 )
529 biasDataProductName = pexConfig.Field(
530 dtype=str,
531 doc="Name of the bias data product",
532 default="bias",
533 )
534 doBiasBeforeOverscan = pexConfig.Field(
535 dtype=bool,
536 doc="Reverse order of overscan and bias correction.",
537 default=False
538 )
540 # Variance construction
541 doVariance = pexConfig.Field(
542 dtype=bool,
543 doc="Calculate variance?",
544 default=True
545 )
546 gain = pexConfig.Field(
547 dtype=float,
548 doc="The gain to use if no Detector is present in the Exposure (ignored if NaN)",
549 default=float("NaN"),
550 )
551 readNoise = pexConfig.Field(
552 dtype=float,
553 doc="The read noise to use if no Detector is present in the Exposure",
554 default=0.0,
555 )
556 doEmpiricalReadNoise = pexConfig.Field(
557 dtype=bool,
558 default=False,
559 doc="Calculate empirical read noise instead of value from AmpInfo data?"
560 )
562 # Linearization.
563 doLinearize = pexConfig.Field(
564 dtype=bool,
565 doc="Correct for nonlinearity of the detector's response?",
566 default=True,
567 )
569 # Crosstalk.
570 doCrosstalk = pexConfig.Field(
571 dtype=bool,
572 doc="Apply intra-CCD crosstalk correction?",
573 default=False,
574 )
575 doCrosstalkBeforeAssemble = pexConfig.Field(
576 dtype=bool,
577 doc="Apply crosstalk correction before CCD assembly, and before trimming?",
578 default=False,
579 )
580 crosstalk = pexConfig.ConfigurableField(
581 target=CrosstalkTask,
582 doc="Intra-CCD crosstalk correction",
583 )
585 # Masking options.
586 doDefect = pexConfig.Field(
587 dtype=bool,
588 doc="Apply correction for CCD defects, e.g. hot pixels?",
589 default=True,
590 )
591 doNanMasking = pexConfig.Field(
592 dtype=bool,
593 doc="Mask NAN pixels?",
594 default=True,
595 )
596 doWidenSaturationTrails = pexConfig.Field(
597 dtype=bool,
598 doc="Widen bleed trails based on their width?",
599 default=True
600 )
602 # Brighter-Fatter correction.
603 doBrighterFatter = pexConfig.Field(
604 dtype=bool,
605 default=False,
606 doc="Apply the brighter fatter correction"
607 )
608 brighterFatterLevel = pexConfig.ChoiceField(
609 dtype=str,
610 default="DETECTOR",
611 doc="The level at which to correct for brighter-fatter.",
612 allowed={
613 "AMP": "Every amplifier treated separately.",
614 "DETECTOR": "One kernel per detector",
615 }
616 )
617 brighterFatterMaxIter = pexConfig.Field(
618 dtype=int,
619 default=10,
620 doc="Maximum number of iterations for the brighter fatter correction"
621 )
622 brighterFatterThreshold = pexConfig.Field(
623 dtype=float,
624 default=1000,
625 doc="Threshold used to stop iterating the brighter fatter correction. It is the "
626 " absolute value of the difference between the current corrected image and the one"
627 " from the previous iteration summed over all the pixels."
628 )
629 brighterFatterApplyGain = pexConfig.Field(
630 dtype=bool,
631 default=True,
632 doc="Should the gain be applied when applying the brighter fatter correction?"
633 )
634 brighterFatterMaskGrowSize = pexConfig.Field(
635 dtype=int,
636 default=0,
637 doc="Number of pixels to grow the masks listed in config.maskListToInterpolate "
638 " when brighter-fatter correction is applied."
639 )
641 # Dark subtraction.
642 doDark = pexConfig.Field(
643 dtype=bool,
644 doc="Apply dark frame correction?",
645 default=True,
646 )
647 darkDataProductName = pexConfig.Field(
648 dtype=str,
649 doc="Name of the dark data product",
650 default="dark",
651 )
653 # Camera-specific stray light removal.
654 doStrayLight = pexConfig.Field(
655 dtype=bool,
656 doc="Subtract stray light in the y-band (due to encoder LEDs)?",
657 default=False,
658 )
659 strayLight = pexConfig.ConfigurableField(
660 target=StrayLightTask,
661 doc="y-band stray light correction"
662 )
664 # Flat correction.
665 doFlat = pexConfig.Field(
666 dtype=bool,
667 doc="Apply flat field correction?",
668 default=True,
669 )
670 flatDataProductName = pexConfig.Field(
671 dtype=str,
672 doc="Name of the flat data product",
673 default="flat",
674 )
675 flatScalingType = pexConfig.ChoiceField(
676 dtype=str,
677 doc="The method for scaling the flat on the fly.",
678 default='USER',
679 allowed={
680 "USER": "Scale by flatUserScale",
681 "MEAN": "Scale by the inverse of the mean",
682 "MEDIAN": "Scale by the inverse of the median",
683 },
684 )
685 flatUserScale = pexConfig.Field(
686 dtype=float,
687 doc="If flatScalingType is 'USER' then scale flat by this amount; ignored otherwise",
688 default=1.0,
689 )
690 doTweakFlat = pexConfig.Field(
691 dtype=bool,
692 doc="Tweak flats to match observed amplifier ratios?",
693 default=False
694 )
696 # Amplifier normalization based on gains instead of using flats configuration.
697 doApplyGains = pexConfig.Field(
698 dtype=bool,
699 doc="Correct the amplifiers for their gains instead of applying flat correction",
700 default=False,
701 )
702 normalizeGains = pexConfig.Field(
703 dtype=bool,
704 doc="Normalize all the amplifiers in each CCD to have the same median value.",
705 default=False,
706 )
708 # Fringe correction.
709 doFringe = pexConfig.Field(
710 dtype=bool,
711 doc="Apply fringe correction?",
712 default=True,
713 )
714 fringe = pexConfig.ConfigurableField(
715 target=FringeTask,
716 doc="Fringe subtraction task",
717 )
718 fringeAfterFlat = pexConfig.Field(
719 dtype=bool,
720 doc="Do fringe subtraction after flat-fielding?",
721 default=True,
722 )
724 # Initial CCD-level background statistics options.
725 doMeasureBackground = pexConfig.Field(
726 dtype=bool,
727 doc="Measure the background level on the reduced image?",
728 default=False,
729 )
731 # Camera-specific masking configuration.
732 doCameraSpecificMasking = pexConfig.Field(
733 dtype=bool,
734 doc="Mask camera-specific bad regions?",
735 default=False,
736 )
737 masking = pexConfig.ConfigurableField(
738 target=MaskingTask,
739 doc="Masking task."
740 )
742 # Interpolation options.
744 doInterpolate = pexConfig.Field(
745 dtype=bool,
746 doc="Interpolate masked pixels?",
747 default=True,
748 )
749 doSaturationInterpolation = pexConfig.Field(
750 dtype=bool,
751 doc="Perform interpolation over pixels masked as saturated?"
752 " NB: This is independent of doSaturation; if that is False this plane"
753 " will likely be blank, resulting in a no-op here.",
754 default=True,
755 )
756 doNanInterpolation = pexConfig.Field(
757 dtype=bool,
758 doc="Perform interpolation over pixels masked as NaN?"
759 " NB: This is independent of doNanMasking; if that is False this plane"
760 " will likely be blank, resulting in a no-op here.",
761 default=True,
762 )
763 doNanInterpAfterFlat = pexConfig.Field(
764 dtype=bool,
765 doc=("If True, ensure we interpolate NaNs after flat-fielding, even if we "
766 "also have to interpolate them before flat-fielding."),
767 default=False,
768 )
769 maskListToInterpolate = pexConfig.ListField(
770 dtype=str,
771 doc="List of mask planes that should be interpolated.",
772 default=['SAT', 'BAD', 'UNMASKEDNAN'],
773 )
774 doSaveInterpPixels = pexConfig.Field(
775 dtype=bool,
776 doc="Save a copy of the pre-interpolated pixel values?",
777 default=False,
778 )
780 # Default photometric calibration options.
781 fluxMag0T1 = pexConfig.DictField(
782 keytype=str,
783 itemtype=float,
784 doc="The approximate flux of a zero-magnitude object in a one-second exposure, per filter.",
785 default=dict((f, pow(10.0, 0.4*m)) for f, m in (("Unknown", 28.0),
786 ))
787 )
788 defaultFluxMag0T1 = pexConfig.Field(
789 dtype=float,
790 doc="Default value for fluxMag0T1 (for an unrecognized filter).",
791 default=pow(10.0, 0.4*28.0)
792 )
794 # Vignette correction configuration.
795 doVignette = pexConfig.Field(
796 dtype=bool,
797 doc="Apply vignetting parameters?",
798 default=False,
799 )
800 vignette = pexConfig.ConfigurableField(
801 target=VignetteTask,
802 doc="Vignetting task.",
803 )
805 # Transmission curve configuration.
806 doAttachTransmissionCurve = pexConfig.Field(
807 dtype=bool,
808 default=False,
809 doc="Construct and attach a wavelength-dependent throughput curve for this CCD image?"
810 )
811 doUseOpticsTransmission = pexConfig.Field(
812 dtype=bool,
813 default=True,
814 doc="Load and use transmission_optics (if doAttachTransmissionCurve is True)?"
815 )
816 doUseFilterTransmission = pexConfig.Field(
817 dtype=bool,
818 default=True,
819 doc="Load and use transmission_filter (if doAttachTransmissionCurve is True)?"
820 )
821 doUseSensorTransmission = pexConfig.Field(
822 dtype=bool,
823 default=True,
824 doc="Load and use transmission_sensor (if doAttachTransmissionCurve is True)?"
825 )
826 doUseAtmosphereTransmission = pexConfig.Field(
827 dtype=bool,
828 default=True,
829 doc="Load and use transmission_atmosphere (if doAttachTransmissionCurve is True)?"
830 )
832 # Illumination correction.
833 doIlluminationCorrection = pexConfig.Field(
834 dtype=bool,
835 default=False,
836 doc="Perform illumination correction?"
837 )
838 illuminationCorrectionDataProductName = pexConfig.Field(
839 dtype=str,
840 doc="Name of the illumination correction data product.",
841 default="illumcor",
842 )
843 illumScale = pexConfig.Field(
844 dtype=float,
845 doc="Scale factor for the illumination correction.",
846 default=1.0,
847 )
848 illumFilters = pexConfig.ListField(
849 dtype=str,
850 default=[],
851 doc="Only perform illumination correction for these filters."
852 )
854 # Write the outputs to disk. If ISR is run as a subtask, this may not be needed.
855 doWrite = pexConfig.Field(
856 dtype=bool,
857 doc="Persist postISRCCD?",
858 default=True,
859 )
861 def validate(self):
862 super().validate()
863 if self.doFlat and self.doApplyGains:
864 raise ValueError("You may not specify both doFlat and doApplyGains")
865 if self.doBiasBeforeOverscan and self.doTrimToMatchCalib:
866 raise ValueError("You may not specify both doBiasBeforeOverscan and doTrimToMatchCalib")
867 if self.doSaturationInterpolation and self.saturatedMaskName not in self.maskListToInterpolate:
868 self.maskListToInterpolate.append(self.saturatedMaskName)
869 if not self.doSaturationInterpolation and self.saturatedMaskName in self.maskListToInterpolate:
870 self.maskListToInterpolate.remove(self.saturatedMaskName)
871 if self.doNanInterpolation and "UNMASKEDNAN" not in self.maskListToInterpolate:
872 self.maskListToInterpolate.append("UNMASKEDNAN")
875class IsrTask(pipeBase.PipelineTask, pipeBase.CmdLineTask):
876 """Apply common instrument signature correction algorithms to a raw frame.
878 The process for correcting imaging data is very similar from
879 camera to camera. This task provides a vanilla implementation of
880 doing these corrections, including the ability to turn certain
881 corrections off if they are not needed. The inputs to the primary
882 method, `run()`, are a raw exposure to be corrected and the
883 calibration data products. The raw input is a single chip sized
884 mosaic of all amps including overscans and other non-science
885 pixels. The method `runDataRef()` identifies and defines the
886 calibration data products, and is intended for use by a
887 `lsst.pipe.base.cmdLineTask.CmdLineTask` and takes as input only a
888 `daf.persistence.butlerSubset.ButlerDataRef`. This task may be
889 subclassed for different camera, although the most camera specific
890 methods have been split into subtasks that can be redirected
891 appropriately.
893 The __init__ method sets up the subtasks for ISR processing, using
894 the defaults from `lsst.ip.isr`.
896 Parameters
897 ----------
898 args : `list`
899 Positional arguments passed to the Task constructor. None used at this time.
900 kwargs : `dict`, optional
901 Keyword arguments passed on to the Task constructor. None used at this time.
902 """
903 ConfigClass = IsrTaskConfig
904 _DefaultName = "isr"
906 def __init__(self, **kwargs):
907 super().__init__(**kwargs)
908 self.makeSubtask("assembleCcd")
909 self.makeSubtask("crosstalk")
910 self.makeSubtask("strayLight")
911 self.makeSubtask("fringe")
912 self.makeSubtask("masking")
913 self.makeSubtask("overscan")
914 self.makeSubtask("vignette")
916 def runQuantum(self, butlerQC, inputRefs, outputRefs):
917 inputs = butlerQC.get(inputRefs)
919 try:
920 inputs['detectorNum'] = inputRefs.ccdExposure.dataId['detector']
921 except Exception as e:
922 raise ValueError("Failure to find valid detectorNum value for Dataset %s: %s." %
923 (inputRefs, e))
925 inputs['isGen3'] = True
927 detector = inputs['ccdExposure'].getDetector()
929 if self.config.doCrosstalk is True:
930 # Crosstalk sources need to be defined by the pipeline
931 # yaml if they exist.
932 if 'crosstalk' in inputs and inputs['crosstalk'] is not None:
933 if not isinstance(inputs['crosstalk'], CrosstalkCalib):
934 inputs['crosstalk'] = CrosstalkCalib.fromTable(inputs['crosstalk'])
935 else:
936 coeffVector = (self.config.crosstalk.crosstalkValues
937 if self.config.crosstalk.useConfigCoefficients else None)
938 crosstalkCalib = CrosstalkCalib().fromDetector(detector, coeffVector=coeffVector)
939 inputs['crosstalk'] = crosstalkCalib
940 if inputs['crosstalk'].interChip and len(inputs['crosstalk'].interChip) > 0:
941 if 'crosstalkSources' not in inputs:
942 self.log.warn("No crosstalkSources found for chip with interChip terms!")
944 if self.doLinearize(detector) is True:
945 if 'linearizer' in inputs and isinstance(inputs['linearizer'], dict):
946 linearizer = linearize.Linearizer(detector=detector, log=self.log)
947 linearizer.fromYaml(inputs['linearizer'])
948 else:
949 linearizer = linearize.Linearizer(table=inputs.get('linearizer', None), detector=detector,
950 log=self.log)
951 inputs['linearizer'] = linearizer
953 if self.config.doDefect is True:
954 if "defects" in inputs and inputs['defects'] is not None:
955 # defects is loaded as a BaseCatalog with columns x0, y0, width, height.
956 # masking expects a list of defects defined by their bounding box
957 if not isinstance(inputs["defects"], Defects):
958 inputs["defects"] = Defects.fromTable(inputs["defects"])
960 # Load the correct style of brighter fatter kernel, and repack
961 # the information as a numpy array.
962 if self.config.doBrighterFatter:
963 brighterFatterKernel = inputs.pop('newBFKernel', None)
964 if brighterFatterKernel is None:
965 brighterFatterKernel = inputs.get('bfKernel', None)
967 if brighterFatterKernel is not None and not isinstance(brighterFatterKernel, numpy.ndarray):
968 detId = detector.getId()
969 inputs['bfGains'] = brighterFatterKernel.gain
970 # If the kernel is not an ndarray, it's the cp_pipe version
971 # so extract the kernel for this detector, or raise an error
972 if self.config.brighterFatterLevel == 'DETECTOR':
973 if brighterFatterKernel.detectorKernel:
974 inputs['bfKernel'] = brighterFatterKernel.detectorKernel[detId]
975 elif brighterFatterKernel.detectorKernelFromAmpKernels:
976 inputs['bfKernel'] = brighterFatterKernel.detectorKernelFromAmpKernels[detId]
977 else:
978 raise RuntimeError("Failed to extract kernel from new-style BF kernel.")
979 else:
980 # TODO DM-15631 for implementing this
981 raise NotImplementedError("Per-amplifier brighter-fatter correction not implemented")
983 if self.config.doFringe is True and self.fringe.checkFilter(inputs['ccdExposure']):
984 expId = inputs['ccdExposure'].getInfo().getVisitInfo().getExposureId()
985 inputs['fringes'] = self.fringe.loadFringes(inputs['fringes'],
986 expId=expId,
987 assembler=self.assembleCcd
988 if self.config.doAssembleIsrExposures else None)
989 else:
990 inputs['fringes'] = pipeBase.Struct(fringes=None)
992 if self.config.doStrayLight is True and self.strayLight.checkFilter(inputs['ccdExposure']):
993 if 'strayLightData' not in inputs:
994 inputs['strayLightData'] = None
996 outputs = self.run(**inputs)
997 butlerQC.put(outputs, outputRefs)
999 def readIsrData(self, dataRef, rawExposure):
1000 """Retrieve necessary frames for instrument signature removal.
1002 Pre-fetching all required ISR data products limits the IO
1003 required by the ISR. Any conflict between the calibration data
1004 available and that needed for ISR is also detected prior to
1005 doing processing, allowing it to fail quickly.
1007 Parameters
1008 ----------
1009 dataRef : `daf.persistence.butlerSubset.ButlerDataRef`
1010 Butler reference of the detector data to be processed
1011 rawExposure : `afw.image.Exposure`
1012 The raw exposure that will later be corrected with the
1013 retrieved calibration data; should not be modified in this
1014 method.
1016 Returns
1017 -------
1018 result : `lsst.pipe.base.Struct`
1019 Result struct with components (which may be `None`):
1020 - ``bias``: bias calibration frame (`afw.image.Exposure`)
1021 - ``linearizer``: functor for linearization (`ip.isr.linearize.LinearizeBase`)
1022 - ``crosstalkSources``: list of possible crosstalk sources (`list`)
1023 - ``dark``: dark calibration frame (`afw.image.Exposure`)
1024 - ``flat``: flat calibration frame (`afw.image.Exposure`)
1025 - ``bfKernel``: Brighter-Fatter kernel (`numpy.ndarray`)
1026 - ``defects``: list of defects (`lsst.meas.algorithms.Defects`)
1027 - ``fringes``: `lsst.pipe.base.Struct` with components:
1028 - ``fringes``: fringe calibration frame (`afw.image.Exposure`)
1029 - ``seed``: random seed derived from the ccdExposureId for random
1030 number generator (`uint32`).
1031 - ``opticsTransmission``: `lsst.afw.image.TransmissionCurve`
1032 A ``TransmissionCurve`` that represents the throughput of the optics,
1033 to be evaluated in focal-plane coordinates.
1034 - ``filterTransmission`` : `lsst.afw.image.TransmissionCurve`
1035 A ``TransmissionCurve`` that represents the throughput of the filter
1036 itself, to be evaluated in focal-plane coordinates.
1037 - ``sensorTransmission`` : `lsst.afw.image.TransmissionCurve`
1038 A ``TransmissionCurve`` that represents the throughput of the sensor
1039 itself, to be evaluated in post-assembly trimmed detector coordinates.
1040 - ``atmosphereTransmission`` : `lsst.afw.image.TransmissionCurve`
1041 A ``TransmissionCurve`` that represents the throughput of the
1042 atmosphere, assumed to be spatially constant.
1043 - ``strayLightData`` : `object`
1044 An opaque object containing calibration information for
1045 stray-light correction. If `None`, no correction will be
1046 performed.
1047 - ``illumMaskedImage`` : illumination correction image (`lsst.afw.image.MaskedImage`)
1049 Raises
1050 ------
1051 NotImplementedError :
1052 Raised if a per-amplifier brighter-fatter kernel is requested by the configuration.
1053 """
1054 try:
1055 dateObs = rawExposure.getInfo().getVisitInfo().getDate()
1056 dateObs = dateObs.toPython().isoformat()
1057 except RuntimeError:
1058 self.log.warn("Unable to identify dateObs for rawExposure.")
1059 dateObs = None
1061 ccd = rawExposure.getDetector()
1062 filterName = afwImage.Filter(rawExposure.getFilter().getId()).getName() # Canonical name for filter
1063 rawExposure.mask.addMaskPlane("UNMASKEDNAN") # needed to match pre DM-15862 processing.
1064 biasExposure = (self.getIsrExposure(dataRef, self.config.biasDataProductName)
1065 if self.config.doBias else None)
1066 # immediate=True required for functors and linearizers are functors; see ticket DM-6515
1067 linearizer = (dataRef.get("linearizer", immediate=True)
1068 if self.doLinearize(ccd) else None)
1069 if linearizer is not None and not isinstance(linearizer, numpy.ndarray):
1070 linearizer.log = self.log
1071 if isinstance(linearizer, numpy.ndarray):
1072 linearizer = linearize.Linearizer(table=linearizer, detector=ccd)
1074 crosstalkCalib = None
1075 if self.config.doCrosstalk:
1076 try:
1077 crosstalkCalib = dataRef.get("crosstalk", immediate=True)
1078 except NoResults:
1079 coeffVector = (self.config.crosstalk.crosstalkValues
1080 if self.config.crosstalk.useConfigCoefficients else None)
1081 crosstalkCalib = CrosstalkCalib().fromDetector(ccd, coeffVector=coeffVector)
1082 crosstalkSources = (self.crosstalk.prepCrosstalk(dataRef, crosstalkCalib)
1083 if self.config.doCrosstalk else None)
1085 darkExposure = (self.getIsrExposure(dataRef, self.config.darkDataProductName)
1086 if self.config.doDark else None)
1087 flatExposure = (self.getIsrExposure(dataRef, self.config.flatDataProductName,
1088 dateObs=dateObs)
1089 if self.config.doFlat else None)
1091 brighterFatterKernel = None
1092 brighterFatterGains = None
1093 if self.config.doBrighterFatter is True:
1094 try:
1095 # Use the new-style cp_pipe version of the kernel if it exists
1096 # If using a new-style kernel, always use the self-consistent
1097 # gains, i.e. the ones inside the kernel object itself
1098 brighterFatterKernel = dataRef.get("brighterFatterKernel")
1099 brighterFatterGains = brighterFatterKernel.gain
1100 self.log.info("New style bright-fatter kernel (brighterFatterKernel) loaded")
1101 except NoResults:
1102 try: # Fall back to the old-style numpy-ndarray style kernel if necessary.
1103 brighterFatterKernel = dataRef.get("bfKernel")
1104 self.log.info("Old style bright-fatter kernel (np.array) loaded")
1105 except NoResults:
1106 brighterFatterKernel = None
1107 if brighterFatterKernel is not None and not isinstance(brighterFatterKernel, numpy.ndarray):
1108 # If the kernel is not an ndarray, it's the cp_pipe version
1109 # so extract the kernel for this detector, or raise an error
1110 if self.config.brighterFatterLevel == 'DETECTOR':
1111 if brighterFatterKernel.detectorKernel:
1112 brighterFatterKernel = brighterFatterKernel.detectorKernel[ccd.getId()]
1113 elif brighterFatterKernel.detectorKernelFromAmpKernels:
1114 brighterFatterKernel = brighterFatterKernel.detectorKernelFromAmpKernels[ccd.getId()]
1115 else:
1116 raise RuntimeError("Failed to extract kernel from new-style BF kernel.")
1117 else:
1118 # TODO DM-15631 for implementing this
1119 raise NotImplementedError("Per-amplifier brighter-fatter correction not implemented")
1121 defectList = (dataRef.get("defects")
1122 if self.config.doDefect else None)
1123 fringeStruct = (self.fringe.readFringes(dataRef, assembler=self.assembleCcd
1124 if self.config.doAssembleIsrExposures else None)
1125 if self.config.doFringe and self.fringe.checkFilter(rawExposure)
1126 else pipeBase.Struct(fringes=None))
1128 if self.config.doAttachTransmissionCurve:
1129 opticsTransmission = (dataRef.get("transmission_optics")
1130 if self.config.doUseOpticsTransmission else None)
1131 filterTransmission = (dataRef.get("transmission_filter")
1132 if self.config.doUseFilterTransmission else None)
1133 sensorTransmission = (dataRef.get("transmission_sensor")
1134 if self.config.doUseSensorTransmission else None)
1135 atmosphereTransmission = (dataRef.get("transmission_atmosphere")
1136 if self.config.doUseAtmosphereTransmission else None)
1137 else:
1138 opticsTransmission = None
1139 filterTransmission = None
1140 sensorTransmission = None
1141 atmosphereTransmission = None
1143 if self.config.doStrayLight:
1144 strayLightData = self.strayLight.readIsrData(dataRef, rawExposure)
1145 else:
1146 strayLightData = None
1148 illumMaskedImage = (self.getIsrExposure(dataRef,
1149 self.config.illuminationCorrectionDataProductName).getMaskedImage()
1150 if (self.config.doIlluminationCorrection
1151 and filterName in self.config.illumFilters)
1152 else None)
1154 # Struct should include only kwargs to run()
1155 return pipeBase.Struct(bias=biasExposure,
1156 linearizer=linearizer,
1157 crosstalk=crosstalkCalib,
1158 crosstalkSources=crosstalkSources,
1159 dark=darkExposure,
1160 flat=flatExposure,
1161 bfKernel=brighterFatterKernel,
1162 bfGains=brighterFatterGains,
1163 defects=defectList,
1164 fringes=fringeStruct,
1165 opticsTransmission=opticsTransmission,
1166 filterTransmission=filterTransmission,
1167 sensorTransmission=sensorTransmission,
1168 atmosphereTransmission=atmosphereTransmission,
1169 strayLightData=strayLightData,
1170 illumMaskedImage=illumMaskedImage
1171 )
1173 @pipeBase.timeMethod
1174 def run(self, ccdExposure, camera=None, bias=None, linearizer=None,
1175 crosstalk=None, crosstalkSources=None,
1176 dark=None, flat=None, bfKernel=None, bfGains=None, defects=None,
1177 fringes=pipeBase.Struct(fringes=None), opticsTransmission=None, filterTransmission=None,
1178 sensorTransmission=None, atmosphereTransmission=None,
1179 detectorNum=None, strayLightData=None, illumMaskedImage=None,
1180 isGen3=False,
1181 ):
1182 """Perform instrument signature removal on an exposure.
1184 Steps included in the ISR processing, in order performed, are:
1185 - saturation and suspect pixel masking
1186 - overscan subtraction
1187 - CCD assembly of individual amplifiers
1188 - bias subtraction
1189 - variance image construction
1190 - linearization of non-linear response
1191 - crosstalk masking
1192 - brighter-fatter correction
1193 - dark subtraction
1194 - fringe correction
1195 - stray light subtraction
1196 - flat correction
1197 - masking of known defects and camera specific features
1198 - vignette calculation
1199 - appending transmission curve and distortion model
1201 Parameters
1202 ----------
1203 ccdExposure : `lsst.afw.image.Exposure`
1204 The raw exposure that is to be run through ISR. The
1205 exposure is modified by this method.
1206 camera : `lsst.afw.cameraGeom.Camera`, optional
1207 The camera geometry for this exposure. Required if ``isGen3`` is
1208 `True` and one or more of ``ccdExposure``, ``bias``, ``dark``, or
1209 ``flat`` does not have an associated detector.
1210 bias : `lsst.afw.image.Exposure`, optional
1211 Bias calibration frame.
1212 linearizer : `lsst.ip.isr.linearize.LinearizeBase`, optional
1213 Functor for linearization.
1214 crosstalk : `lsst.ip.isr.crosstalk.CrosstalkCalib`, optional
1215 Calibration for crosstalk.
1216 crosstalkSources : `list`, optional
1217 List of possible crosstalk sources.
1218 dark : `lsst.afw.image.Exposure`, optional
1219 Dark calibration frame.
1220 flat : `lsst.afw.image.Exposure`, optional
1221 Flat calibration frame.
1222 bfKernel : `numpy.ndarray`, optional
1223 Brighter-fatter kernel.
1224 bfGains : `dict` of `float`, optional
1225 Gains used to override the detector's nominal gains for the
1226 brighter-fatter correction. A dict keyed by amplifier name for
1227 the detector in question.
1228 defects : `lsst.meas.algorithms.Defects`, optional
1229 List of defects.
1230 fringes : `lsst.pipe.base.Struct`, optional
1231 Struct containing the fringe correction data, with
1232 elements:
1233 - ``fringes``: fringe calibration frame (`afw.image.Exposure`)
1234 - ``seed``: random seed derived from the ccdExposureId for random
1235 number generator (`uint32`)
1236 opticsTransmission: `lsst.afw.image.TransmissionCurve`, optional
1237 A ``TransmissionCurve`` that represents the throughput of the optics,
1238 to be evaluated in focal-plane coordinates.
1239 filterTransmission : `lsst.afw.image.TransmissionCurve`
1240 A ``TransmissionCurve`` that represents the throughput of the filter
1241 itself, to be evaluated in focal-plane coordinates.
1242 sensorTransmission : `lsst.afw.image.TransmissionCurve`
1243 A ``TransmissionCurve`` that represents the throughput of the sensor
1244 itself, to be evaluated in post-assembly trimmed detector coordinates.
1245 atmosphereTransmission : `lsst.afw.image.TransmissionCurve`
1246 A ``TransmissionCurve`` that represents the throughput of the
1247 atmosphere, assumed to be spatially constant.
1248 detectorNum : `int`, optional
1249 The integer number for the detector to process.
1250 isGen3 : bool, optional
1251 Flag this call to run() as using the Gen3 butler environment.
1252 strayLightData : `object`, optional
1253 Opaque object containing calibration information for stray-light
1254 correction. If `None`, no correction will be performed.
1255 illumMaskedImage : `lsst.afw.image.MaskedImage`, optional
1256 Illumination correction image.
1258 Returns
1259 -------
1260 result : `lsst.pipe.base.Struct`
1261 Result struct with component:
1262 - ``exposure`` : `afw.image.Exposure`
1263 The fully ISR corrected exposure.
1264 - ``outputExposure`` : `afw.image.Exposure`
1265 An alias for `exposure`
1266 - ``ossThumb`` : `numpy.ndarray`
1267 Thumbnail image of the exposure after overscan subtraction.
1268 - ``flattenedThumb`` : `numpy.ndarray`
1269 Thumbnail image of the exposure after flat-field correction.
1271 Raises
1272 ------
1273 RuntimeError
1274 Raised if a configuration option is set to True, but the
1275 required calibration data has not been specified.
1277 Notes
1278 -----
1279 The current processed exposure can be viewed by setting the
1280 appropriate lsstDebug entries in the `debug.display`
1281 dictionary. The names of these entries correspond to some of
1282 the IsrTaskConfig Boolean options, with the value denoting the
1283 frame to use. The exposure is shown inside the matching
1284 option check and after the processing of that step has
1285 finished. The steps with debug points are:
1287 doAssembleCcd
1288 doBias
1289 doCrosstalk
1290 doBrighterFatter
1291 doDark
1292 doFringe
1293 doStrayLight
1294 doFlat
1296 In addition, setting the "postISRCCD" entry displays the
1297 exposure after all ISR processing has finished.
1299 """
1301 if isGen3 is True:
1302 # Gen3 currently cannot automatically do configuration overrides.
1303 # DM-15257 looks to discuss this issue.
1304 # Configure input exposures;
1305 if detectorNum is None:
1306 raise RuntimeError("Must supply the detectorNum if running as Gen3.")
1308 ccdExposure = self.ensureExposure(ccdExposure, camera, detectorNum)
1309 bias = self.ensureExposure(bias, camera, detectorNum)
1310 dark = self.ensureExposure(dark, camera, detectorNum)
1311 flat = self.ensureExposure(flat, camera, detectorNum)
1312 else:
1313 if isinstance(ccdExposure, ButlerDataRef):
1314 return self.runDataRef(ccdExposure)
1316 ccd = ccdExposure.getDetector()
1317 filterName = afwImage.Filter(ccdExposure.getFilter().getId()).getName() # Canonical name for filter
1319 if not ccd:
1320 assert not self.config.doAssembleCcd, "You need a Detector to run assembleCcd."
1321 ccd = [FakeAmp(ccdExposure, self.config)]
1323 # Validate Input
1324 if self.config.doBias and bias is None:
1325 raise RuntimeError("Must supply a bias exposure if config.doBias=True.")
1326 if self.doLinearize(ccd) and linearizer is None:
1327 raise RuntimeError("Must supply a linearizer if config.doLinearize=True for this detector.")
1328 if self.config.doBrighterFatter and bfKernel is None:
1329 raise RuntimeError("Must supply a kernel if config.doBrighterFatter=True.")
1330 if self.config.doDark and dark is None:
1331 raise RuntimeError("Must supply a dark exposure if config.doDark=True.")
1332 if self.config.doFlat and flat is None:
1333 raise RuntimeError("Must supply a flat exposure if config.doFlat=True.")
1334 if self.config.doDefect and defects is None:
1335 raise RuntimeError("Must supply defects if config.doDefect=True.")
1336 if (self.config.doFringe and filterName in self.fringe.config.filters
1337 and fringes.fringes is None):
1338 # The `fringes` object needs to be a pipeBase.Struct, as
1339 # we use it as a `dict` for the parameters of
1340 # `FringeTask.run()`. The `fringes.fringes` `list` may
1341 # not be `None` if `doFringe=True`. Otherwise, raise.
1342 raise RuntimeError("Must supply fringe exposure as a pipeBase.Struct.")
1343 if (self.config.doIlluminationCorrection and filterName in self.config.illumFilters
1344 and illumMaskedImage is None):
1345 raise RuntimeError("Must supply an illumcor if config.doIlluminationCorrection=True.")
1347 # Begin ISR processing.
1348 if self.config.doConvertIntToFloat:
1349 self.log.info("Converting exposure to floating point values.")
1350 ccdExposure = self.convertIntToFloat(ccdExposure)
1352 if self.config.doBias and self.config.doBiasBeforeOverscan:
1353 self.log.info("Applying bias correction.")
1354 isrFunctions.biasCorrection(ccdExposure.getMaskedImage(), bias.getMaskedImage(),
1355 trimToFit=self.config.doTrimToMatchCalib)
1356 self.debugView(ccdExposure, "doBias")
1358 # Amplifier level processing.
1359 overscans = []
1360 for amp in ccd:
1361 # if ccdExposure is one amp, check for coverage to prevent performing ops multiple times
1362 if ccdExposure.getBBox().contains(amp.getBBox()):
1363 # Check for fully masked bad amplifiers, and generate masks for SUSPECT and SATURATED values.
1364 badAmp = self.maskAmplifier(ccdExposure, amp, defects)
1366 if self.config.doOverscan and not badAmp:
1367 # Overscan correction on amp-by-amp basis.
1368 overscanResults = self.overscanCorrection(ccdExposure, amp)
1369 self.log.debug("Corrected overscan for amplifier %s.", amp.getName())
1370 if overscanResults is not None and \
1371 self.config.qa is not None and self.config.qa.saveStats is True:
1372 if isinstance(overscanResults.overscanFit, float):
1373 qaMedian = overscanResults.overscanFit
1374 qaStdev = float("NaN")
1375 else:
1376 qaStats = afwMath.makeStatistics(overscanResults.overscanFit,
1377 afwMath.MEDIAN | afwMath.STDEVCLIP)
1378 qaMedian = qaStats.getValue(afwMath.MEDIAN)
1379 qaStdev = qaStats.getValue(afwMath.STDEVCLIP)
1381 self.metadata.set(f"ISR OSCAN {amp.getName()} MEDIAN", qaMedian)
1382 self.metadata.set(f"ISR OSCAN {amp.getName()} STDEV", qaStdev)
1383 self.log.debug(" Overscan stats for amplifer %s: %f +/- %f",
1384 amp.getName(), qaMedian, qaStdev)
1385 ccdExposure.getMetadata().set('OVERSCAN', "Overscan corrected")
1386 else:
1387 if badAmp:
1388 self.log.warn("Amplifier %s is bad.", amp.getName())
1389 overscanResults = None
1391 overscans.append(overscanResults if overscanResults is not None else None)
1392 else:
1393 self.log.info("Skipped OSCAN for %s.", amp.getName())
1395 if self.config.doCrosstalk and self.config.doCrosstalkBeforeAssemble:
1396 self.log.info("Applying crosstalk correction.")
1397 self.crosstalk.run(ccdExposure, crosstalk=crosstalk,
1398 crosstalkSources=crosstalkSources)
1399 self.debugView(ccdExposure, "doCrosstalk")
1401 if self.config.doAssembleCcd:
1402 self.log.info("Assembling CCD from amplifiers.")
1403 ccdExposure = self.assembleCcd.assembleCcd(ccdExposure)
1405 if self.config.expectWcs and not ccdExposure.getWcs():
1406 self.log.warn("No WCS found in input exposure.")
1407 self.debugView(ccdExposure, "doAssembleCcd")
1409 ossThumb = None
1410 if self.config.qa.doThumbnailOss:
1411 ossThumb = isrQa.makeThumbnail(ccdExposure, isrQaConfig=self.config.qa)
1413 if self.config.doBias and not self.config.doBiasBeforeOverscan:
1414 self.log.info("Applying bias correction.")
1415 isrFunctions.biasCorrection(ccdExposure.getMaskedImage(), bias.getMaskedImage(),
1416 trimToFit=self.config.doTrimToMatchCalib)
1417 self.debugView(ccdExposure, "doBias")
1419 if self.config.doVariance:
1420 for amp, overscanResults in zip(ccd, overscans):
1421 if ccdExposure.getBBox().contains(amp.getBBox()):
1422 self.log.debug("Constructing variance map for amplifer %s.", amp.getName())
1423 ampExposure = ccdExposure.Factory(ccdExposure, amp.getBBox())
1424 if overscanResults is not None:
1425 self.updateVariance(ampExposure, amp,
1426 overscanImage=overscanResults.overscanImage)
1427 else:
1428 self.updateVariance(ampExposure, amp,
1429 overscanImage=None)
1430 if self.config.qa is not None and self.config.qa.saveStats is True:
1431 qaStats = afwMath.makeStatistics(ampExposure.getVariance(),
1432 afwMath.MEDIAN | afwMath.STDEVCLIP)
1433 self.metadata.set(f"ISR VARIANCE {amp.getName()} MEDIAN",
1434 qaStats.getValue(afwMath.MEDIAN))
1435 self.metadata.set(f"ISR VARIANCE {amp.getName()} STDEV",
1436 qaStats.getValue(afwMath.STDEVCLIP))
1437 self.log.debug(" Variance stats for amplifer %s: %f +/- %f.",
1438 amp.getName(), qaStats.getValue(afwMath.MEDIAN),
1439 qaStats.getValue(afwMath.STDEVCLIP))
1441 if self.doLinearize(ccd):
1442 self.log.info("Applying linearizer.")
1443 linearizer.applyLinearity(image=ccdExposure.getMaskedImage().getImage(),
1444 detector=ccd, log=self.log)
1446 if self.config.doCrosstalk and not self.config.doCrosstalkBeforeAssemble:
1447 self.log.info("Applying crosstalk correction.")
1448 self.crosstalk.run(ccdExposure, crosstalk=crosstalk,
1449 crosstalkSources=crosstalkSources, isTrimmed=True)
1450 self.debugView(ccdExposure, "doCrosstalk")
1452 # Masking block. Optionally mask known defects, NAN pixels, widen trails, and do
1453 # anything else the camera needs. Saturated and suspect pixels have already been masked.
1454 if self.config.doDefect:
1455 self.log.info("Masking defects.")
1456 self.maskDefect(ccdExposure, defects)
1458 if self.config.numEdgeSuspect > 0:
1459 self.log.info("Masking edges as SUSPECT.")
1460 self.maskEdges(ccdExposure, numEdgePixels=self.config.numEdgeSuspect,
1461 maskPlane="SUSPECT", level=self.config.edgeMaskLevel)
1463 if self.config.doNanMasking:
1464 self.log.info("Masking NAN value pixels.")
1465 self.maskNan(ccdExposure)
1467 if self.config.doWidenSaturationTrails:
1468 self.log.info("Widening saturation trails.")
1469 isrFunctions.widenSaturationTrails(ccdExposure.getMaskedImage().getMask())
1471 if self.config.doCameraSpecificMasking:
1472 self.log.info("Masking regions for camera specific reasons.")
1473 self.masking.run(ccdExposure)
1475 if self.config.doBrighterFatter:
1476 # We need to apply flats and darks before we can interpolate, and we
1477 # need to interpolate before we do B-F, but we do B-F without the
1478 # flats and darks applied so we can work in units of electrons or holes.
1479 # This context manager applies and then removes the darks and flats.
1480 #
1481 # We also do not want to interpolate values here, so operate on temporary
1482 # images so we can apply only the BF-correction and roll back the
1483 # interpolation.
1484 interpExp = ccdExposure.clone()
1485 with self.flatContext(interpExp, flat, dark):
1486 isrFunctions.interpolateFromMask(
1487 maskedImage=interpExp.getMaskedImage(),
1488 fwhm=self.config.fwhm,
1489 growSaturatedFootprints=self.config.growSaturationFootprintSize,
1490 maskNameList=self.config.maskListToInterpolate
1491 )
1492 bfExp = interpExp.clone()
1494 self.log.info("Applying brighter fatter correction using kernel type %s / gains %s.",
1495 type(bfKernel), type(bfGains))
1496 bfResults = isrFunctions.brighterFatterCorrection(bfExp, bfKernel,
1497 self.config.brighterFatterMaxIter,
1498 self.config.brighterFatterThreshold,
1499 self.config.brighterFatterApplyGain,
1500 bfGains)
1501 if bfResults[1] == self.config.brighterFatterMaxIter:
1502 self.log.warn("Brighter fatter correction did not converge, final difference %f.",
1503 bfResults[0])
1504 else:
1505 self.log.info("Finished brighter fatter correction in %d iterations.",
1506 bfResults[1])
1507 image = ccdExposure.getMaskedImage().getImage()
1508 bfCorr = bfExp.getMaskedImage().getImage()
1509 bfCorr -= interpExp.getMaskedImage().getImage()
1510 image += bfCorr
1512 # Applying the brighter-fatter correction applies a
1513 # convolution to the science image. At the edges this
1514 # convolution may not have sufficient valid pixels to
1515 # produce a valid correction. Mark pixels within the size
1516 # of the brighter-fatter kernel as EDGE to warn of this
1517 # fact.
1518 self.log.info("Ensuring image edges are masked as SUSPECT to the brighter-fatter kernel size.")
1519 self.maskEdges(ccdExposure, numEdgePixels=numpy.max(bfKernel.shape) // 2,
1520 maskPlane="EDGE")
1522 if self.config.brighterFatterMaskGrowSize > 0:
1523 self.log.info("Growing masks to account for brighter-fatter kernel convolution.")
1524 for maskPlane in self.config.maskListToInterpolate:
1525 isrFunctions.growMasks(ccdExposure.getMask(),
1526 radius=self.config.brighterFatterMaskGrowSize,
1527 maskNameList=maskPlane,
1528 maskValue=maskPlane)
1530 self.debugView(ccdExposure, "doBrighterFatter")
1532 if self.config.doDark:
1533 self.log.info("Applying dark correction.")
1534 self.darkCorrection(ccdExposure, dark)
1535 self.debugView(ccdExposure, "doDark")
1537 if self.config.doFringe and not self.config.fringeAfterFlat:
1538 self.log.info("Applying fringe correction before flat.")
1539 self.fringe.run(ccdExposure, **fringes.getDict())
1540 self.debugView(ccdExposure, "doFringe")
1542 if self.config.doStrayLight and self.strayLight.check(ccdExposure):
1543 self.log.info("Checking strayLight correction.")
1544 self.strayLight.run(ccdExposure, strayLightData)
1545 self.debugView(ccdExposure, "doStrayLight")
1547 if self.config.doFlat:
1548 self.log.info("Applying flat correction.")
1549 self.flatCorrection(ccdExposure, flat)
1550 self.debugView(ccdExposure, "doFlat")
1552 if self.config.doApplyGains:
1553 self.log.info("Applying gain correction instead of flat.")
1554 isrFunctions.applyGains(ccdExposure, self.config.normalizeGains)
1556 if self.config.doFringe and self.config.fringeAfterFlat:
1557 self.log.info("Applying fringe correction after flat.")
1558 self.fringe.run(ccdExposure, **fringes.getDict())
1560 if self.config.doVignette:
1561 self.log.info("Constructing Vignette polygon.")
1562 self.vignettePolygon = self.vignette.run(ccdExposure)
1564 if self.config.vignette.doWriteVignettePolygon:
1565 self.setValidPolygonIntersect(ccdExposure, self.vignettePolygon)
1567 if self.config.doAttachTransmissionCurve:
1568 self.log.info("Adding transmission curves.")
1569 isrFunctions.attachTransmissionCurve(ccdExposure, opticsTransmission=opticsTransmission,
1570 filterTransmission=filterTransmission,
1571 sensorTransmission=sensorTransmission,
1572 atmosphereTransmission=atmosphereTransmission)
1574 flattenedThumb = None
1575 if self.config.qa.doThumbnailFlattened:
1576 flattenedThumb = isrQa.makeThumbnail(ccdExposure, isrQaConfig=self.config.qa)
1578 if self.config.doIlluminationCorrection and filterName in self.config.illumFilters:
1579 self.log.info("Performing illumination correction.")
1580 isrFunctions.illuminationCorrection(ccdExposure.getMaskedImage(),
1581 illumMaskedImage, illumScale=self.config.illumScale,
1582 trimToFit=self.config.doTrimToMatchCalib)
1584 preInterpExp = None
1585 if self.config.doSaveInterpPixels:
1586 preInterpExp = ccdExposure.clone()
1588 # Reset and interpolate bad pixels.
1589 #
1590 # Large contiguous bad regions (which should have the BAD mask
1591 # bit set) should have their values set to the image median.
1592 # This group should include defects and bad amplifiers. As the
1593 # area covered by these defects are large, there's little
1594 # reason to expect that interpolation would provide a more
1595 # useful value.
1596 #
1597 # Smaller defects can be safely interpolated after the larger
1598 # regions have had their pixel values reset. This ensures
1599 # that the remaining defects adjacent to bad amplifiers (as an
1600 # example) do not attempt to interpolate extreme values.
1601 if self.config.doSetBadRegions:
1602 badPixelCount, badPixelValue = isrFunctions.setBadRegions(ccdExposure)
1603 if badPixelCount > 0:
1604 self.log.info("Set %d BAD pixels to %f.", badPixelCount, badPixelValue)
1606 if self.config.doInterpolate:
1607 self.log.info("Interpolating masked pixels.")
1608 isrFunctions.interpolateFromMask(
1609 maskedImage=ccdExposure.getMaskedImage(),
1610 fwhm=self.config.fwhm,
1611 growSaturatedFootprints=self.config.growSaturationFootprintSize,
1612 maskNameList=list(self.config.maskListToInterpolate)
1613 )
1615 self.roughZeroPoint(ccdExposure)
1617 if self.config.doMeasureBackground:
1618 self.log.info("Measuring background level.")
1619 self.measureBackground(ccdExposure, self.config.qa)
1621 if self.config.qa is not None and self.config.qa.saveStats is True:
1622 for amp in ccd:
1623 ampExposure = ccdExposure.Factory(ccdExposure, amp.getBBox())
1624 qaStats = afwMath.makeStatistics(ampExposure.getImage(),
1625 afwMath.MEDIAN | afwMath.STDEVCLIP)
1626 self.metadata.set("ISR BACKGROUND {} MEDIAN".format(amp.getName()),
1627 qaStats.getValue(afwMath.MEDIAN))
1628 self.metadata.set("ISR BACKGROUND {} STDEV".format(amp.getName()),
1629 qaStats.getValue(afwMath.STDEVCLIP))
1630 self.log.debug(" Background stats for amplifer %s: %f +/- %f",
1631 amp.getName(), qaStats.getValue(afwMath.MEDIAN),
1632 qaStats.getValue(afwMath.STDEVCLIP))
1634 self.debugView(ccdExposure, "postISRCCD")
1636 return pipeBase.Struct(
1637 exposure=ccdExposure,
1638 ossThumb=ossThumb,
1639 flattenedThumb=flattenedThumb,
1641 preInterpolatedExposure=preInterpExp,
1642 outputExposure=ccdExposure,
1643 outputOssThumbnail=ossThumb,
1644 outputFlattenedThumbnail=flattenedThumb,
1645 )
1647 @pipeBase.timeMethod
1648 def runDataRef(self, sensorRef):
1649 """Perform instrument signature removal on a ButlerDataRef of a Sensor.
1651 This method contains the `CmdLineTask` interface to the ISR
1652 processing. All IO is handled here, freeing the `run()` method
1653 to manage only pixel-level calculations. The steps performed
1654 are:
1655 - Read in necessary detrending/isr/calibration data.
1656 - Process raw exposure in `run()`.
1657 - Persist the ISR-corrected exposure as "postISRCCD" if
1658 config.doWrite=True.
1660 Parameters
1661 ----------
1662 sensorRef : `daf.persistence.butlerSubset.ButlerDataRef`
1663 DataRef of the detector data to be processed
1665 Returns
1666 -------
1667 result : `lsst.pipe.base.Struct`
1668 Result struct with component:
1669 - ``exposure`` : `afw.image.Exposure`
1670 The fully ISR corrected exposure.
1672 Raises
1673 ------
1674 RuntimeError
1675 Raised if a configuration option is set to True, but the
1676 required calibration data does not exist.
1678 """
1679 self.log.info("Performing ISR on sensor %s.", sensorRef.dataId)
1681 ccdExposure = sensorRef.get(self.config.datasetType)
1683 camera = sensorRef.get("camera")
1684 isrData = self.readIsrData(sensorRef, ccdExposure)
1686 result = self.run(ccdExposure, camera=camera, **isrData.getDict())
1688 if self.config.doWrite:
1689 sensorRef.put(result.exposure, "postISRCCD")
1690 if result.preInterpolatedExposure is not None:
1691 sensorRef.put(result.preInterpolatedExposure, "postISRCCD_uninterpolated")
1692 if result.ossThumb is not None:
1693 isrQa.writeThumbnail(sensorRef, result.ossThumb, "ossThumb")
1694 if result.flattenedThumb is not None:
1695 isrQa.writeThumbnail(sensorRef, result.flattenedThumb, "flattenedThumb")
1697 return result
1699 def getIsrExposure(self, dataRef, datasetType, dateObs=None, immediate=True):
1700 """Retrieve a calibration dataset for removing instrument signature.
1702 Parameters
1703 ----------
1705 dataRef : `daf.persistence.butlerSubset.ButlerDataRef`
1706 DataRef of the detector data to find calibration datasets
1707 for.
1708 datasetType : `str`
1709 Type of dataset to retrieve (e.g. 'bias', 'flat', etc).
1710 dateObs : `str`, optional
1711 Date of the observation. Used to correct butler failures
1712 when using fallback filters.
1713 immediate : `Bool`
1714 If True, disable butler proxies to enable error handling
1715 within this routine.
1717 Returns
1718 -------
1719 exposure : `lsst.afw.image.Exposure`
1720 Requested calibration frame.
1722 Raises
1723 ------
1724 RuntimeError
1725 Raised if no matching calibration frame can be found.
1726 """
1727 try:
1728 exp = dataRef.get(datasetType, immediate=immediate)
1729 except Exception as exc1:
1730 if not self.config.fallbackFilterName:
1731 raise RuntimeError("Unable to retrieve %s for %s: %s." % (datasetType, dataRef.dataId, exc1))
1732 try:
1733 if self.config.useFallbackDate and dateObs:
1734 exp = dataRef.get(datasetType, filter=self.config.fallbackFilterName,
1735 dateObs=dateObs, immediate=immediate)
1736 else:
1737 exp = dataRef.get(datasetType, filter=self.config.fallbackFilterName, immediate=immediate)
1738 except Exception as exc2:
1739 raise RuntimeError("Unable to retrieve %s for %s, even with fallback filter %s: %s AND %s." %
1740 (datasetType, dataRef.dataId, self.config.fallbackFilterName, exc1, exc2))
1741 self.log.warn("Using fallback calibration from filter %s.", self.config.fallbackFilterName)
1743 if self.config.doAssembleIsrExposures:
1744 exp = self.assembleCcd.assembleCcd(exp)
1745 return exp
1747 def ensureExposure(self, inputExp, camera, detectorNum):
1748 """Ensure that the data returned by Butler is a fully constructed exposure.
1750 ISR requires exposure-level image data for historical reasons, so if we did
1751 not recieve that from Butler, construct it from what we have, modifying the
1752 input in place.
1754 Parameters
1755 ----------
1756 inputExp : `lsst.afw.image.Exposure`, `lsst.afw.image.DecoratedImageU`, or
1757 `lsst.afw.image.ImageF`
1758 The input data structure obtained from Butler.
1759 camera : `lsst.afw.cameraGeom.camera`
1760 The camera associated with the image. Used to find the appropriate
1761 detector.
1762 detectorNum : `int`
1763 The detector this exposure should match.
1765 Returns
1766 -------
1767 inputExp : `lsst.afw.image.Exposure`
1768 The re-constructed exposure, with appropriate detector parameters.
1770 Raises
1771 ------
1772 TypeError
1773 Raised if the input data cannot be used to construct an exposure.
1774 """
1775 if isinstance(inputExp, afwImage.DecoratedImageU):
1776 inputExp = afwImage.makeExposure(afwImage.makeMaskedImage(inputExp))
1777 elif isinstance(inputExp, afwImage.ImageF):
1778 inputExp = afwImage.makeExposure(afwImage.makeMaskedImage(inputExp))
1779 elif isinstance(inputExp, afwImage.MaskedImageF):
1780 inputExp = afwImage.makeExposure(inputExp)
1781 elif isinstance(inputExp, afwImage.Exposure):
1782 pass
1783 elif inputExp is None:
1784 # Assume this will be caught by the setup if it is a problem.
1785 return inputExp
1786 else:
1787 raise TypeError("Input Exposure is not known type in isrTask.ensureExposure: %s." %
1788 (type(inputExp), ))
1790 if inputExp.getDetector() is None:
1791 inputExp.setDetector(camera[detectorNum])
1793 return inputExp
1795 def convertIntToFloat(self, exposure):
1796 """Convert exposure image from uint16 to float.
1798 If the exposure does not need to be converted, the input is
1799 immediately returned. For exposures that are converted to use
1800 floating point pixels, the variance is set to unity and the
1801 mask to zero.
1803 Parameters
1804 ----------
1805 exposure : `lsst.afw.image.Exposure`
1806 The raw exposure to be converted.
1808 Returns
1809 -------
1810 newexposure : `lsst.afw.image.Exposure`
1811 The input ``exposure``, converted to floating point pixels.
1813 Raises
1814 ------
1815 RuntimeError
1816 Raised if the exposure type cannot be converted to float.
1818 """
1819 if isinstance(exposure, afwImage.ExposureF):
1820 # Nothing to be done
1821 self.log.debug("Exposure already of type float.")
1822 return exposure
1823 if not hasattr(exposure, "convertF"):
1824 raise RuntimeError("Unable to convert exposure (%s) to float." % type(exposure))
1826 newexposure = exposure.convertF()
1827 newexposure.variance[:] = 1
1828 newexposure.mask[:] = 0x0
1830 return newexposure
1832 def maskAmplifier(self, ccdExposure, amp, defects):
1833 """Identify bad amplifiers, saturated and suspect pixels.
1835 Parameters
1836 ----------
1837 ccdExposure : `lsst.afw.image.Exposure`
1838 Input exposure to be masked.
1839 amp : `lsst.afw.table.AmpInfoCatalog`
1840 Catalog of parameters defining the amplifier on this
1841 exposure to mask.
1842 defects : `lsst.meas.algorithms.Defects`
1843 List of defects. Used to determine if the entire
1844 amplifier is bad.
1846 Returns
1847 -------
1848 badAmp : `Bool`
1849 If this is true, the entire amplifier area is covered by
1850 defects and unusable.
1852 """
1853 maskedImage = ccdExposure.getMaskedImage()
1855 badAmp = False
1857 # Check if entire amp region is defined as a defect (need to use amp.getBBox() for correct
1858 # comparison with current defects definition.
1859 if defects is not None:
1860 badAmp = bool(sum([v.getBBox().contains(amp.getBBox()) for v in defects]))
1862 # In the case of a bad amp, we will set mask to "BAD" (here use amp.getRawBBox() for correct
1863 # association with pixels in current ccdExposure).
1864 if badAmp:
1865 dataView = afwImage.MaskedImageF(maskedImage, amp.getRawBBox(),
1866 afwImage.PARENT)
1867 maskView = dataView.getMask()
1868 maskView |= maskView.getPlaneBitMask("BAD")
1869 del maskView
1870 return badAmp
1872 # Mask remaining defects after assembleCcd() to allow for defects that cross amplifier boundaries.
1873 # Saturation and suspect pixels can be masked now, though.
1874 limits = dict()
1875 if self.config.doSaturation and not badAmp:
1876 limits.update({self.config.saturatedMaskName: amp.getSaturation()})
1877 if self.config.doSuspect and not badAmp:
1878 limits.update({self.config.suspectMaskName: amp.getSuspectLevel()})
1879 if math.isfinite(self.config.saturation):
1880 limits.update({self.config.saturatedMaskName: self.config.saturation})
1882 for maskName, maskThreshold in limits.items():
1883 if not math.isnan(maskThreshold):
1884 dataView = maskedImage.Factory(maskedImage, amp.getRawBBox())
1885 isrFunctions.makeThresholdMask(
1886 maskedImage=dataView,
1887 threshold=maskThreshold,
1888 growFootprints=0,
1889 maskName=maskName
1890 )
1892 # Determine if we've fully masked this amplifier with SUSPECT and SAT pixels.
1893 maskView = afwImage.Mask(maskedImage.getMask(), amp.getRawDataBBox(),
1894 afwImage.PARENT)
1895 maskVal = maskView.getPlaneBitMask([self.config.saturatedMaskName,
1896 self.config.suspectMaskName])
1897 if numpy.all(maskView.getArray() & maskVal > 0):
1898 badAmp = True
1899 maskView |= maskView.getPlaneBitMask("BAD")
1901 return badAmp
1903 def overscanCorrection(self, ccdExposure, amp):
1904 """Apply overscan correction in place.
1906 This method does initial pixel rejection of the overscan
1907 region. The overscan can also be optionally segmented to
1908 allow for discontinuous overscan responses to be fit
1909 separately. The actual overscan subtraction is performed by
1910 the `lsst.ip.isr.isrFunctions.overscanCorrection` function,
1911 which is called here after the amplifier is preprocessed.
1913 Parameters
1914 ----------
1915 ccdExposure : `lsst.afw.image.Exposure`
1916 Exposure to have overscan correction performed.
1917 amp : `lsst.afw.cameraGeom.Amplifer`
1918 The amplifier to consider while correcting the overscan.
1920 Returns
1921 -------
1922 overscanResults : `lsst.pipe.base.Struct`
1923 Result struct with components:
1924 - ``imageFit`` : scalar or `lsst.afw.image.Image`
1925 Value or fit subtracted from the amplifier image data.
1926 - ``overscanFit`` : scalar or `lsst.afw.image.Image`
1927 Value or fit subtracted from the overscan image data.
1928 - ``overscanImage`` : `lsst.afw.image.Image`
1929 Image of the overscan region with the overscan
1930 correction applied. This quantity is used to estimate
1931 the amplifier read noise empirically.
1933 Raises
1934 ------
1935 RuntimeError
1936 Raised if the ``amp`` does not contain raw pixel information.
1938 See Also
1939 --------
1940 lsst.ip.isr.isrFunctions.overscanCorrection
1941 """
1942 if amp.getRawHorizontalOverscanBBox().isEmpty():
1943 self.log.info("ISR_OSCAN: No overscan region. Not performing overscan correction.")
1944 return None
1946 statControl = afwMath.StatisticsControl()
1947 statControl.setAndMask(ccdExposure.mask.getPlaneBitMask("SAT"))
1949 # Determine the bounding boxes
1950 dataBBox = amp.getRawDataBBox()
1951 oscanBBox = amp.getRawHorizontalOverscanBBox()
1952 dx0 = 0
1953 dx1 = 0
1955 prescanBBox = amp.getRawPrescanBBox()
1956 if (oscanBBox.getBeginX() > prescanBBox.getBeginX()): # amp is at the right
1957 dx0 += self.config.overscanNumLeadingColumnsToSkip
1958 dx1 -= self.config.overscanNumTrailingColumnsToSkip
1959 else:
1960 dx0 += self.config.overscanNumTrailingColumnsToSkip
1961 dx1 -= self.config.overscanNumLeadingColumnsToSkip
1963 # Determine if we need to work on subregions of the amplifier and overscan.
1964 imageBBoxes = []
1965 overscanBBoxes = []
1967 if ((self.config.overscanBiasJump
1968 and self.config.overscanBiasJumpLocation)
1969 and (ccdExposure.getMetadata().exists(self.config.overscanBiasJumpKeyword)
1970 and ccdExposure.getMetadata().getScalar(self.config.overscanBiasJumpKeyword) in
1971 self.config.overscanBiasJumpDevices)):
1972 if amp.getReadoutCorner() in (ReadoutCorner.LL, ReadoutCorner.LR):
1973 yLower = self.config.overscanBiasJumpLocation
1974 yUpper = dataBBox.getHeight() - yLower
1975 else:
1976 yUpper = self.config.overscanBiasJumpLocation
1977 yLower = dataBBox.getHeight() - yUpper
1979 imageBBoxes.append(lsst.geom.Box2I(dataBBox.getBegin(),
1980 lsst.geom.Extent2I(dataBBox.getWidth(), yLower)))
1981 overscanBBoxes.append(lsst.geom.Box2I(oscanBBox.getBegin() + lsst.geom.Extent2I(dx0, 0),
1982 lsst.geom.Extent2I(oscanBBox.getWidth() - dx0 + dx1,
1983 yLower)))
1985 imageBBoxes.append(lsst.geom.Box2I(dataBBox.getBegin() + lsst.geom.Extent2I(0, yLower),
1986 lsst.geom.Extent2I(dataBBox.getWidth(), yUpper)))
1987 overscanBBoxes.append(lsst.geom.Box2I(oscanBBox.getBegin() + lsst.geom.Extent2I(dx0, yLower),
1988 lsst.geom.Extent2I(oscanBBox.getWidth() - dx0 + dx1,
1989 yUpper)))
1990 else:
1991 imageBBoxes.append(lsst.geom.Box2I(dataBBox.getBegin(),
1992 lsst.geom.Extent2I(dataBBox.getWidth(), dataBBox.getHeight())))
1993 overscanBBoxes.append(lsst.geom.Box2I(oscanBBox.getBegin() + lsst.geom.Extent2I(dx0, 0),
1994 lsst.geom.Extent2I(oscanBBox.getWidth() - dx0 + dx1,
1995 oscanBBox.getHeight())))
1997 # Perform overscan correction on subregions, ensuring saturated pixels are masked.
1998 for imageBBox, overscanBBox in zip(imageBBoxes, overscanBBoxes):
1999 ampImage = ccdExposure.maskedImage[imageBBox]
2000 overscanImage = ccdExposure.maskedImage[overscanBBox]
2002 overscanArray = overscanImage.image.array
2003 median = numpy.ma.median(numpy.ma.masked_where(overscanImage.mask.array, overscanArray))
2004 bad = numpy.where(numpy.abs(overscanArray - median) > self.config.overscanMaxDev)
2005 overscanImage.mask.array[bad] = overscanImage.mask.getPlaneBitMask("SAT")
2007 statControl = afwMath.StatisticsControl()
2008 statControl.setAndMask(ccdExposure.mask.getPlaneBitMask("SAT"))
2010 overscanResults = self.overscan.run(ampImage.getImage(), overscanImage, amp)
2012 # Measure average overscan levels and record them in the metadata.
2013 levelStat = afwMath.MEDIAN
2014 sigmaStat = afwMath.STDEVCLIP
2016 sctrl = afwMath.StatisticsControl(self.config.qa.flatness.clipSigma,
2017 self.config.qa.flatness.nIter)
2018 metadata = ccdExposure.getMetadata()
2019 ampNum = amp.getName()
2020 # if self.config.overscanFitType in ("MEDIAN", "MEAN", "MEANCLIP"):
2021 if isinstance(overscanResults.overscanFit, float):
2022 metadata.set("ISR_OSCAN_LEVEL%s" % ampNum, overscanResults.overscanFit)
2023 metadata.set("ISR_OSCAN_SIGMA%s" % ampNum, 0.0)
2024 else:
2025 stats = afwMath.makeStatistics(overscanResults.overscanFit, levelStat | sigmaStat, sctrl)
2026 metadata.set("ISR_OSCAN_LEVEL%s" % ampNum, stats.getValue(levelStat))
2027 metadata.set("ISR_OSCAN_SIGMA%s" % ampNum, stats.getValue(sigmaStat))
2029 return overscanResults
2031 def updateVariance(self, ampExposure, amp, overscanImage=None):
2032 """Set the variance plane using the amplifier gain and read noise
2034 The read noise is calculated from the ``overscanImage`` if the
2035 ``doEmpiricalReadNoise`` option is set in the configuration; otherwise
2036 the value from the amplifier data is used.
2038 Parameters
2039 ----------
2040 ampExposure : `lsst.afw.image.Exposure`
2041 Exposure to process.
2042 amp : `lsst.afw.table.AmpInfoRecord` or `FakeAmp`
2043 Amplifier detector data.
2044 overscanImage : `lsst.afw.image.MaskedImage`, optional.
2045 Image of overscan, required only for empirical read noise.
2047 See also
2048 --------
2049 lsst.ip.isr.isrFunctions.updateVariance
2050 """
2051 maskPlanes = [self.config.saturatedMaskName, self.config.suspectMaskName]
2052 gain = amp.getGain()
2054 if math.isnan(gain):
2055 gain = 1.0
2056 self.log.warn("Gain set to NAN! Updating to 1.0 to generate Poisson variance.")
2057 elif gain <= 0:
2058 patchedGain = 1.0
2059 self.log.warn("Gain for amp %s == %g <= 0; setting to %f.",
2060 amp.getName(), gain, patchedGain)
2061 gain = patchedGain
2063 if self.config.doEmpiricalReadNoise and overscanImage is None:
2064 self.log.info("Overscan is none for EmpiricalReadNoise.")
2066 if self.config.doEmpiricalReadNoise and overscanImage is not None:
2067 stats = afwMath.StatisticsControl()
2068 stats.setAndMask(overscanImage.mask.getPlaneBitMask(maskPlanes))
2069 readNoise = afwMath.makeStatistics(overscanImage, afwMath.STDEVCLIP, stats).getValue()
2070 self.log.info("Calculated empirical read noise for amp %s: %f.",
2071 amp.getName(), readNoise)
2072 else:
2073 readNoise = amp.getReadNoise()
2075 isrFunctions.updateVariance(
2076 maskedImage=ampExposure.getMaskedImage(),
2077 gain=gain,
2078 readNoise=readNoise,
2079 )
2081 def darkCorrection(self, exposure, darkExposure, invert=False):
2082 """Apply dark correction in place.
2084 Parameters
2085 ----------
2086 exposure : `lsst.afw.image.Exposure`
2087 Exposure to process.
2088 darkExposure : `lsst.afw.image.Exposure`
2089 Dark exposure of the same size as ``exposure``.
2090 invert : `Bool`, optional
2091 If True, re-add the dark to an already corrected image.
2093 Raises
2094 ------
2095 RuntimeError
2096 Raised if either ``exposure`` or ``darkExposure`` do not
2097 have their dark time defined.
2099 See Also
2100 --------
2101 lsst.ip.isr.isrFunctions.darkCorrection
2102 """
2103 expScale = exposure.getInfo().getVisitInfo().getDarkTime()
2104 if math.isnan(expScale):
2105 raise RuntimeError("Exposure darktime is NAN.")
2106 if darkExposure.getInfo().getVisitInfo() is not None \
2107 and not math.isnan(darkExposure.getInfo().getVisitInfo().getDarkTime()):
2108 darkScale = darkExposure.getInfo().getVisitInfo().getDarkTime()
2109 else:
2110 # DM-17444: darkExposure.getInfo.getVisitInfo() is None
2111 # so getDarkTime() does not exist.
2112 self.log.warn("darkExposure.getInfo().getVisitInfo() does not exist. Using darkScale = 1.0.")
2113 darkScale = 1.0
2115 isrFunctions.darkCorrection(
2116 maskedImage=exposure.getMaskedImage(),
2117 darkMaskedImage=darkExposure.getMaskedImage(),
2118 expScale=expScale,
2119 darkScale=darkScale,
2120 invert=invert,
2121 trimToFit=self.config.doTrimToMatchCalib
2122 )
2124 def doLinearize(self, detector):
2125 """Check if linearization is needed for the detector cameraGeom.
2127 Checks config.doLinearize and the linearity type of the first
2128 amplifier.
2130 Parameters
2131 ----------
2132 detector : `lsst.afw.cameraGeom.Detector`
2133 Detector to get linearity type from.
2135 Returns
2136 -------
2137 doLinearize : `Bool`
2138 If True, linearization should be performed.
2139 """
2140 return self.config.doLinearize and \
2141 detector.getAmplifiers()[0].getLinearityType() != NullLinearityType
2143 def flatCorrection(self, exposure, flatExposure, invert=False):
2144 """Apply flat correction in place.
2146 Parameters
2147 ----------
2148 exposure : `lsst.afw.image.Exposure`
2149 Exposure to process.
2150 flatExposure : `lsst.afw.image.Exposure`
2151 Flat exposure of the same size as ``exposure``.
2152 invert : `Bool`, optional
2153 If True, unflatten an already flattened image.
2155 See Also
2156 --------
2157 lsst.ip.isr.isrFunctions.flatCorrection
2158 """
2159 isrFunctions.flatCorrection(
2160 maskedImage=exposure.getMaskedImage(),
2161 flatMaskedImage=flatExposure.getMaskedImage(),
2162 scalingType=self.config.flatScalingType,
2163 userScale=self.config.flatUserScale,
2164 invert=invert,
2165 trimToFit=self.config.doTrimToMatchCalib
2166 )
2168 def saturationDetection(self, exposure, amp):
2169 """Detect saturated pixels and mask them using mask plane config.saturatedMaskName, in place.
2171 Parameters
2172 ----------
2173 exposure : `lsst.afw.image.Exposure`
2174 Exposure to process. Only the amplifier DataSec is processed.
2175 amp : `lsst.afw.table.AmpInfoCatalog`
2176 Amplifier detector data.
2178 See Also
2179 --------
2180 lsst.ip.isr.isrFunctions.makeThresholdMask
2181 """
2182 if not math.isnan(amp.getSaturation()):
2183 maskedImage = exposure.getMaskedImage()
2184 dataView = maskedImage.Factory(maskedImage, amp.getRawBBox())
2185 isrFunctions.makeThresholdMask(
2186 maskedImage=dataView,
2187 threshold=amp.getSaturation(),
2188 growFootprints=0,
2189 maskName=self.config.saturatedMaskName,
2190 )
2192 def saturationInterpolation(self, exposure):
2193 """Interpolate over saturated pixels, in place.
2195 This method should be called after `saturationDetection`, to
2196 ensure that the saturated pixels have been identified in the
2197 SAT mask. It should also be called after `assembleCcd`, since
2198 saturated regions may cross amplifier boundaries.
2200 Parameters
2201 ----------
2202 exposure : `lsst.afw.image.Exposure`
2203 Exposure to process.
2205 See Also
2206 --------
2207 lsst.ip.isr.isrTask.saturationDetection
2208 lsst.ip.isr.isrFunctions.interpolateFromMask
2209 """
2210 isrFunctions.interpolateFromMask(
2211 maskedImage=exposure.getMaskedImage(),
2212 fwhm=self.config.fwhm,
2213 growSaturatedFootprints=self.config.growSaturationFootprintSize,
2214 maskNameList=list(self.config.saturatedMaskName),
2215 )
2217 def suspectDetection(self, exposure, amp):
2218 """Detect suspect pixels and mask them using mask plane config.suspectMaskName, in place.
2220 Parameters
2221 ----------
2222 exposure : `lsst.afw.image.Exposure`
2223 Exposure to process. Only the amplifier DataSec is processed.
2224 amp : `lsst.afw.table.AmpInfoCatalog`
2225 Amplifier detector data.
2227 See Also
2228 --------
2229 lsst.ip.isr.isrFunctions.makeThresholdMask
2231 Notes
2232 -----
2233 Suspect pixels are pixels whose value is greater than amp.getSuspectLevel().
2234 This is intended to indicate pixels that may be affected by unknown systematics;
2235 for example if non-linearity corrections above a certain level are unstable
2236 then that would be a useful value for suspectLevel. A value of `nan` indicates
2237 that no such level exists and no pixels are to be masked as suspicious.
2238 """
2239 suspectLevel = amp.getSuspectLevel()
2240 if math.isnan(suspectLevel):
2241 return
2243 maskedImage = exposure.getMaskedImage()
2244 dataView = maskedImage.Factory(maskedImage, amp.getRawBBox())
2245 isrFunctions.makeThresholdMask(
2246 maskedImage=dataView,
2247 threshold=suspectLevel,
2248 growFootprints=0,
2249 maskName=self.config.suspectMaskName,
2250 )
2252 def maskDefect(self, exposure, defectBaseList):
2253 """Mask defects using mask plane "BAD", in place.
2255 Parameters
2256 ----------
2257 exposure : `lsst.afw.image.Exposure`
2258 Exposure to process.
2259 defectBaseList : `lsst.meas.algorithms.Defects` or `list` of
2260 `lsst.afw.image.DefectBase`.
2261 List of defects to mask.
2263 Notes
2264 -----
2265 Call this after CCD assembly, since defects may cross amplifier boundaries.
2266 """
2267 maskedImage = exposure.getMaskedImage()
2268 if not isinstance(defectBaseList, Defects):
2269 # Promotes DefectBase to Defect
2270 defectList = Defects(defectBaseList)
2271 else:
2272 defectList = defectBaseList
2273 defectList.maskPixels(maskedImage, maskName="BAD")
2275 def maskEdges(self, exposure, numEdgePixels=0, maskPlane="SUSPECT", level='DETECTOR'):
2276 """Mask edge pixels with applicable mask plane.
2278 Parameters
2279 ----------
2280 exposure : `lsst.afw.image.Exposure`
2281 Exposure to process.
2282 numEdgePixels : `int`, optional
2283 Number of edge pixels to mask.
2284 maskPlane : `str`, optional
2285 Mask plane name to use.
2286 level : `str`, optional
2287 Level at which to mask edges.
2288 """
2289 maskedImage = exposure.getMaskedImage()
2290 maskBitMask = maskedImage.getMask().getPlaneBitMask(maskPlane)
2292 if numEdgePixels > 0:
2293 if level == 'DETECTOR':
2294 boxes = [maskedImage.getBBox()]
2295 elif level == 'AMP':
2296 boxes = [amp.getBBox() for amp in exposure.getDetector()]
2298 for box in boxes:
2299 # This makes a bbox numEdgeSuspect pixels smaller than the image on each side
2300 subImage = maskedImage[box]
2301 box.grow(-numEdgePixels)
2302 # Mask pixels outside box
2303 SourceDetectionTask.setEdgeBits(
2304 subImage,
2305 box,
2306 maskBitMask)
2308 def maskAndInterpolateDefects(self, exposure, defectBaseList):
2309 """Mask and interpolate defects using mask plane "BAD", in place.
2311 Parameters
2312 ----------
2313 exposure : `lsst.afw.image.Exposure`
2314 Exposure to process.
2315 defectBaseList : `lsst.meas.algorithms.Defects` or `list` of
2316 `lsst.afw.image.DefectBase`.
2317 List of defects to mask and interpolate.
2319 See Also
2320 --------
2321 lsst.ip.isr.isrTask.maskDefect
2322 """
2323 self.maskDefect(exposure, defectBaseList)
2324 self.maskEdges(exposure, numEdgePixels=self.config.numEdgeSuspect,
2325 maskPlane="SUSPECT", level=self.config.edgeMaskLevel)
2326 isrFunctions.interpolateFromMask(
2327 maskedImage=exposure.getMaskedImage(),
2328 fwhm=self.config.fwhm,
2329 growSaturatedFootprints=0,
2330 maskNameList=["BAD"],
2331 )
2333 def maskNan(self, exposure):
2334 """Mask NaNs using mask plane "UNMASKEDNAN", in place.
2336 Parameters
2337 ----------
2338 exposure : `lsst.afw.image.Exposure`
2339 Exposure to process.
2341 Notes
2342 -----
2343 We mask over all NaNs, including those that are masked with
2344 other bits (because those may or may not be interpolated over
2345 later, and we want to remove all NaNs). Despite this
2346 behaviour, the "UNMASKEDNAN" mask plane is used to preserve
2347 the historical name.
2348 """
2349 maskedImage = exposure.getMaskedImage()
2351 # Find and mask NaNs
2352 maskedImage.getMask().addMaskPlane("UNMASKEDNAN")
2353 maskVal = maskedImage.getMask().getPlaneBitMask("UNMASKEDNAN")
2354 numNans = maskNans(maskedImage, maskVal)
2355 self.metadata.set("NUMNANS", numNans)
2356 if numNans > 0:
2357 self.log.warn("There were %d unmasked NaNs.", numNans)
2359 def maskAndInterpolateNan(self, exposure):
2360 """"Mask and interpolate NaNs using mask plane "UNMASKEDNAN", in place.
2362 Parameters
2363 ----------
2364 exposure : `lsst.afw.image.Exposure`
2365 Exposure to process.
2367 See Also
2368 --------
2369 lsst.ip.isr.isrTask.maskNan
2370 """
2371 self.maskNan(exposure)
2372 isrFunctions.interpolateFromMask(
2373 maskedImage=exposure.getMaskedImage(),
2374 fwhm=self.config.fwhm,
2375 growSaturatedFootprints=0,
2376 maskNameList=["UNMASKEDNAN"],
2377 )
2379 def measureBackground(self, exposure, IsrQaConfig=None):
2380 """Measure the image background in subgrids, for quality control purposes.
2382 Parameters
2383 ----------
2384 exposure : `lsst.afw.image.Exposure`
2385 Exposure to process.
2386 IsrQaConfig : `lsst.ip.isr.isrQa.IsrQaConfig`
2387 Configuration object containing parameters on which background
2388 statistics and subgrids to use.
2389 """
2390 if IsrQaConfig is not None:
2391 statsControl = afwMath.StatisticsControl(IsrQaConfig.flatness.clipSigma,
2392 IsrQaConfig.flatness.nIter)
2393 maskVal = exposure.getMaskedImage().getMask().getPlaneBitMask(["BAD", "SAT", "DETECTED"])
2394 statsControl.setAndMask(maskVal)
2395 maskedImage = exposure.getMaskedImage()
2396 stats = afwMath.makeStatistics(maskedImage, afwMath.MEDIAN | afwMath.STDEVCLIP, statsControl)
2397 skyLevel = stats.getValue(afwMath.MEDIAN)
2398 skySigma = stats.getValue(afwMath.STDEVCLIP)
2399 self.log.info("Flattened sky level: %f +/- %f.", skyLevel, skySigma)
2400 metadata = exposure.getMetadata()
2401 metadata.set('SKYLEVEL', skyLevel)
2402 metadata.set('SKYSIGMA', skySigma)
2404 # calcluating flatlevel over the subgrids
2405 stat = afwMath.MEANCLIP if IsrQaConfig.flatness.doClip else afwMath.MEAN
2406 meshXHalf = int(IsrQaConfig.flatness.meshX/2.)
2407 meshYHalf = int(IsrQaConfig.flatness.meshY/2.)
2408 nX = int((exposure.getWidth() + meshXHalf) / IsrQaConfig.flatness.meshX)
2409 nY = int((exposure.getHeight() + meshYHalf) / IsrQaConfig.flatness.meshY)
2410 skyLevels = numpy.zeros((nX, nY))
2412 for j in range(nY):
2413 yc = meshYHalf + j * IsrQaConfig.flatness.meshY
2414 for i in range(nX):
2415 xc = meshXHalf + i * IsrQaConfig.flatness.meshX
2417 xLLC = xc - meshXHalf
2418 yLLC = yc - meshYHalf
2419 xURC = xc + meshXHalf - 1
2420 yURC = yc + meshYHalf - 1
2422 bbox = lsst.geom.Box2I(lsst.geom.Point2I(xLLC, yLLC), lsst.geom.Point2I(xURC, yURC))
2423 miMesh = maskedImage.Factory(exposure.getMaskedImage(), bbox, afwImage.LOCAL)
2425 skyLevels[i, j] = afwMath.makeStatistics(miMesh, stat, statsControl).getValue()
2427 good = numpy.where(numpy.isfinite(skyLevels))
2428 skyMedian = numpy.median(skyLevels[good])
2429 flatness = (skyLevels[good] - skyMedian) / skyMedian
2430 flatness_rms = numpy.std(flatness)
2431 flatness_pp = flatness.max() - flatness.min() if len(flatness) > 0 else numpy.nan
2433 self.log.info("Measuring sky levels in %dx%d grids: %f.", nX, nY, skyMedian)
2434 self.log.info("Sky flatness in %dx%d grids - pp: %f rms: %f.",
2435 nX, nY, flatness_pp, flatness_rms)
2437 metadata.set('FLATNESS_PP', float(flatness_pp))
2438 metadata.set('FLATNESS_RMS', float(flatness_rms))
2439 metadata.set('FLATNESS_NGRIDS', '%dx%d' % (nX, nY))
2440 metadata.set('FLATNESS_MESHX', IsrQaConfig.flatness.meshX)
2441 metadata.set('FLATNESS_MESHY', IsrQaConfig.flatness.meshY)
2443 def roughZeroPoint(self, exposure):
2444 """Set an approximate magnitude zero point for the exposure.
2446 Parameters
2447 ----------
2448 exposure : `lsst.afw.image.Exposure`
2449 Exposure to process.
2450 """
2451 filterName = afwImage.Filter(exposure.getFilter().getId()).getName() # Canonical name for filter
2452 if filterName in self.config.fluxMag0T1:
2453 fluxMag0 = self.config.fluxMag0T1[filterName]
2454 else:
2455 self.log.warn("No rough magnitude zero point set for filter %s.", filterName)
2456 fluxMag0 = self.config.defaultFluxMag0T1
2458 expTime = exposure.getInfo().getVisitInfo().getExposureTime()
2459 if not expTime > 0: # handle NaN as well as <= 0
2460 self.log.warn("Non-positive exposure time; skipping rough zero point.")
2461 return
2463 self.log.info("Setting rough magnitude zero point: %f", 2.5*math.log10(fluxMag0*expTime))
2464 exposure.setPhotoCalib(afwImage.makePhotoCalibFromCalibZeroPoint(fluxMag0*expTime, 0.0))
2466 def setValidPolygonIntersect(self, ccdExposure, fpPolygon):
2467 """Set the valid polygon as the intersection of fpPolygon and the ccd corners.
2469 Parameters
2470 ----------
2471 ccdExposure : `lsst.afw.image.Exposure`
2472 Exposure to process.
2473 fpPolygon : `lsst.afw.geom.Polygon`
2474 Polygon in focal plane coordinates.
2475 """
2476 # Get ccd corners in focal plane coordinates
2477 ccd = ccdExposure.getDetector()
2478 fpCorners = ccd.getCorners(FOCAL_PLANE)
2479 ccdPolygon = Polygon(fpCorners)
2481 # Get intersection of ccd corners with fpPolygon
2482 intersect = ccdPolygon.intersectionSingle(fpPolygon)
2484 # Transform back to pixel positions and build new polygon
2485 ccdPoints = ccd.transform(intersect, FOCAL_PLANE, PIXELS)
2486 validPolygon = Polygon(ccdPoints)
2487 ccdExposure.getInfo().setValidPolygon(validPolygon)
2489 @contextmanager
2490 def flatContext(self, exp, flat, dark=None):
2491 """Context manager that applies and removes flats and darks,
2492 if the task is configured to apply them.
2494 Parameters
2495 ----------
2496 exp : `lsst.afw.image.Exposure`
2497 Exposure to process.
2498 flat : `lsst.afw.image.Exposure`
2499 Flat exposure the same size as ``exp``.
2500 dark : `lsst.afw.image.Exposure`, optional
2501 Dark exposure the same size as ``exp``.
2503 Yields
2504 ------
2505 exp : `lsst.afw.image.Exposure`
2506 The flat and dark corrected exposure.
2507 """
2508 if self.config.doDark and dark is not None:
2509 self.darkCorrection(exp, dark)
2510 if self.config.doFlat:
2511 self.flatCorrection(exp, flat)
2512 try:
2513 yield exp
2514 finally:
2515 if self.config.doFlat:
2516 self.flatCorrection(exp, flat, invert=True)
2517 if self.config.doDark and dark is not None:
2518 self.darkCorrection(exp, dark, invert=True)
2520 def debugView(self, exposure, stepname):
2521 """Utility function to examine ISR exposure at different stages.
2523 Parameters
2524 ----------
2525 exposure : `lsst.afw.image.Exposure`
2526 Exposure to view.
2527 stepname : `str`
2528 State of processing to view.
2529 """
2530 frame = getDebugFrame(self._display, stepname)
2531 if frame:
2532 display = getDisplay(frame)
2533 display.scale('asinh', 'zscale')
2534 display.mtv(exposure)
2535 prompt = "Press Enter to continue [c]... "
2536 while True:
2537 ans = input(prompt).lower()
2538 if ans in ("", "c",):
2539 break
2542class FakeAmp(object):
2543 """A Detector-like object that supports returning gain and saturation level
2545 This is used when the input exposure does not have a detector.
2547 Parameters
2548 ----------
2549 exposure : `lsst.afw.image.Exposure`
2550 Exposure to generate a fake amplifier for.
2551 config : `lsst.ip.isr.isrTaskConfig`
2552 Configuration to apply to the fake amplifier.
2553 """
2555 def __init__(self, exposure, config):
2556 self._bbox = exposure.getBBox(afwImage.LOCAL)
2557 self._RawHorizontalOverscanBBox = lsst.geom.Box2I()
2558 self._gain = config.gain
2559 self._readNoise = config.readNoise
2560 self._saturation = config.saturation
2562 def getBBox(self):
2563 return self._bbox
2565 def getRawBBox(self):
2566 return self._bbox
2568 def getRawHorizontalOverscanBBox(self):
2569 return self._RawHorizontalOverscanBBox
2571 def getGain(self):
2572 return self._gain
2574 def getReadNoise(self):
2575 return self._readNoise
2577 def getSaturation(self):
2578 return self._saturation
2580 def getSuspectLevel(self):
2581 return float("NaN")
2584class RunIsrConfig(pexConfig.Config):
2585 isr = pexConfig.ConfigurableField(target=IsrTask, doc="Instrument signature removal")
2588class RunIsrTask(pipeBase.CmdLineTask):
2589 """Task to wrap the default IsrTask to allow it to be retargeted.
2591 The standard IsrTask can be called directly from a command line
2592 program, but doing so removes the ability of the task to be
2593 retargeted. As most cameras override some set of the IsrTask
2594 methods, this would remove those data-specific methods in the
2595 output post-ISR images. This wrapping class fixes the issue,
2596 allowing identical post-ISR images to be generated by both the
2597 processCcd and isrTask code.
2598 """
2599 ConfigClass = RunIsrConfig
2600 _DefaultName = "runIsr"
2602 def __init__(self, *args, **kwargs):
2603 super().__init__(*args, **kwargs)
2604 self.makeSubtask("isr")
2606 def runDataRef(self, dataRef):
2607 """
2608 Parameters
2609 ----------
2610 dataRef : `lsst.daf.persistence.ButlerDataRef`
2611 data reference of the detector data to be processed
2613 Returns
2614 -------
2615 result : `pipeBase.Struct`
2616 Result struct with component:
2618 - exposure : `lsst.afw.image.Exposure`
2619 Post-ISR processed exposure.
2620 """
2621 return self.isr.runDataRef(dataRef)