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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 deduplicate=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", "calibration_label"],
113 )
115 crosstalk = cT.PrerequisiteInput(
116 name="crosstalk",
117 doc="Input crosstalk object",
118 storageClass="CrosstalkCalib",
119 dimensions=["instrument", "calibration_label", "detector"],
120 )
121 # TODO: DM-25348. This does not work yet to correctly load
122 # possible crosstalk sources.
123 crosstalkSources = cT.PrerequisiteInput(
124 name="isrOverscanCorrected",
125 doc="Overscan corrected input images.",
126 storageClass="Exposure",
127 dimensions=["instrument", "exposure", "detector"],
128 deferLoad=True,
129 multiple=True,
130 lookupFunction=crosstalkSourceLookup,
131 )
132 bias = cT.PrerequisiteInput(
133 name="bias",
134 doc="Input bias calibration.",
135 storageClass="ExposureF",
136 dimensions=["instrument", "calibration_label", "detector"],
137 )
138 dark = cT.PrerequisiteInput(
139 name='dark',
140 doc="Input dark calibration.",
141 storageClass="ExposureF",
142 dimensions=["instrument", "calibration_label", "detector"],
143 )
144 flat = cT.PrerequisiteInput(
145 name="flat",
146 doc="Input flat calibration.",
147 storageClass="ExposureF",
148 dimensions=["instrument", "physical_filter", "calibration_label", "detector"],
149 )
150 fringes = cT.PrerequisiteInput(
151 name="fringe",
152 doc="Input fringe calibration.",
153 storageClass="ExposureF",
154 dimensions=["instrument", "physical_filter", "calibration_label", "detector"],
155 )
156 strayLightData = cT.PrerequisiteInput(
157 name='yBackground',
158 doc="Input stray light calibration.",
159 storageClass="StrayLightData",
160 dimensions=["instrument", "physical_filter", "calibration_label", "detector"],
161 )
162 bfKernel = cT.PrerequisiteInput(
163 name='bfKernel',
164 doc="Input brighter-fatter kernel.",
165 storageClass="NumpyArray",
166 dimensions=["instrument", "calibration_label"],
167 )
168 newBFKernel = cT.PrerequisiteInput(
169 name='brighterFatterKernel',
170 doc="Newer complete kernel + gain solutions.",
171 storageClass="BrighterFatterKernel",
172 dimensions=["instrument", "calibration_label", "detector"],
173 )
174 defects = cT.PrerequisiteInput(
175 name='defects',
176 doc="Input defect tables.",
177 storageClass="Defects",
178 dimensions=["instrument", "calibration_label", "detector"],
179 )
180 opticsTransmission = cT.PrerequisiteInput(
181 name="transmission_optics",
182 storageClass="TransmissionCurve",
183 doc="Transmission curve due to the optics.",
184 dimensions=["instrument", "calibration_label"],
185 )
186 filterTransmission = cT.PrerequisiteInput(
187 name="transmission_filter",
188 storageClass="TransmissionCurve",
189 doc="Transmission curve due to the filter.",
190 dimensions=["instrument", "physical_filter", "calibration_label"],
191 )
192 sensorTransmission = cT.PrerequisiteInput(
193 name="transmission_sensor",
194 storageClass="TransmissionCurve",
195 doc="Transmission curve due to the sensor.",
196 dimensions=["instrument", "calibration_label", "detector"],
197 )
198 atmosphereTransmission = cT.PrerequisiteInput(
199 name="transmission_atmosphere",
200 storageClass="TransmissionCurve",
201 doc="Transmission curve due to the atmosphere.",
202 dimensions=["instrument"],
203 )
204 illumMaskedImage = cT.PrerequisiteInput(
205 name="illum",
206 doc="Input illumination correction.",
207 storageClass="MaskedImageF",
208 dimensions=["instrument", "physical_filter", "calibration_label", "detector"],
209 )
211 outputExposure = cT.Output(
212 name='postISRCCD',
213 doc="Output ISR processed exposure.",
214 storageClass="Exposure",
215 dimensions=["instrument", "exposure", "detector"],
216 )
217 preInterpExposure = cT.Output(
218 name='preInterpISRCCD',
219 doc="Output ISR processed exposure, with pixels left uninterpolated.",
220 storageClass="ExposureF",
221 dimensions=["instrument", "exposure", "detector"],
222 )
223 outputOssThumbnail = cT.Output(
224 name="OssThumb",
225 doc="Output Overscan-subtracted thumbnail image.",
226 storageClass="Thumbnail",
227 dimensions=["instrument", "exposure", "detector"],
228 )
229 outputFlattenedThumbnail = cT.Output(
230 name="FlattenedThumb",
231 doc="Output flat-corrected thumbnail image.",
232 storageClass="Thumbnail",
233 dimensions=["instrument", "exposure", "detector"],
234 )
236 def __init__(self, *, config=None):
237 super().__init__(config=config)
239 if config.doBias is not True:
240 self.prerequisiteInputs.discard("bias")
241 if config.doLinearize is not True:
242 self.prerequisiteInputs.discard("linearizer")
243 if config.doCrosstalk is not True:
244 self.inputs.discard("crosstalkSources")
245 self.prerequisiteInputs.discard("crosstalk")
246 if config.doBrighterFatter is not True:
247 self.prerequisiteInputs.discard("bfKernel")
248 self.prerequisiteInputs.discard("newBFKernel")
249 if config.doDefect is not True:
250 self.prerequisiteInputs.discard("defects")
251 if config.doDark is not True:
252 self.prerequisiteInputs.discard("dark")
253 if config.doFlat is not True:
254 self.prerequisiteInputs.discard("flat")
255 if config.doAttachTransmissionCurve is not True:
256 self.prerequisiteInputs.discard("opticsTransmission")
257 self.prerequisiteInputs.discard("filterTransmission")
258 self.prerequisiteInputs.discard("sensorTransmission")
259 self.prerequisiteInputs.discard("atmosphereTransmission")
260 if config.doUseOpticsTransmission is not True:
261 self.prerequisiteInputs.discard("opticsTransmission")
262 if config.doUseFilterTransmission is not True:
263 self.prerequisiteInputs.discard("filterTransmission")
264 if config.doUseSensorTransmission is not True:
265 self.prerequisiteInputs.discard("sensorTransmission")
266 if config.doUseAtmosphereTransmission is not True:
267 self.prerequisiteInputs.discard("atmosphereTransmission")
268 if config.doIlluminationCorrection is not True:
269 self.prerequisiteInputs.discard("illumMaskedImage")
271 if config.doWrite is not True:
272 self.outputs.discard("outputExposure")
273 self.outputs.discard("preInterpExposure")
274 self.outputs.discard("outputFlattenedThumbnail")
275 self.outputs.discard("outputOssThumbnail")
276 if config.doSaveInterpPixels is not True:
277 self.outputs.discard("preInterpExposure")
278 if config.qa.doThumbnailOss is not True:
279 self.outputs.discard("outputOssThumbnail")
280 if config.qa.doThumbnailFlattened is not True:
281 self.outputs.discard("outputFlattenedThumbnail")
284class IsrTaskConfig(pipeBase.PipelineTaskConfig,
285 pipelineConnections=IsrTaskConnections):
286 """Configuration parameters for IsrTask.
288 Items are grouped in the order in which they are executed by the task.
289 """
290 datasetType = pexConfig.Field(
291 dtype=str,
292 doc="Dataset type for input data; users will typically leave this alone, "
293 "but camera-specific ISR tasks will override it",
294 default="raw",
295 )
297 fallbackFilterName = pexConfig.Field(
298 dtype=str,
299 doc="Fallback default filter name for calibrations.",
300 optional=True
301 )
302 useFallbackDate = pexConfig.Field(
303 dtype=bool,
304 doc="Pass observation date when using fallback filter.",
305 default=False,
306 )
307 expectWcs = pexConfig.Field(
308 dtype=bool,
309 default=True,
310 doc="Expect input science images to have a WCS (set False for e.g. spectrographs)."
311 )
312 fwhm = pexConfig.Field(
313 dtype=float,
314 doc="FWHM of PSF in arcseconds.",
315 default=1.0,
316 )
317 qa = pexConfig.ConfigField(
318 dtype=isrQa.IsrQaConfig,
319 doc="QA related configuration options.",
320 )
322 # Image conversion configuration
323 doConvertIntToFloat = pexConfig.Field(
324 dtype=bool,
325 doc="Convert integer raw images to floating point values?",
326 default=True,
327 )
329 # Saturated pixel handling.
330 doSaturation = pexConfig.Field(
331 dtype=bool,
332 doc="Mask saturated pixels? NB: this is totally independent of the"
333 " interpolation option - this is ONLY setting the bits in the mask."
334 " To have them interpolated make sure doSaturationInterpolation=True",
335 default=True,
336 )
337 saturatedMaskName = pexConfig.Field(
338 dtype=str,
339 doc="Name of mask plane to use in saturation detection and interpolation",
340 default="SAT",
341 )
342 saturation = pexConfig.Field(
343 dtype=float,
344 doc="The saturation level to use if no Detector is present in the Exposure (ignored if NaN)",
345 default=float("NaN"),
346 )
347 growSaturationFootprintSize = pexConfig.Field(
348 dtype=int,
349 doc="Number of pixels by which to grow the saturation footprints",
350 default=1,
351 )
353 # Suspect pixel handling.
354 doSuspect = pexConfig.Field(
355 dtype=bool,
356 doc="Mask suspect pixels?",
357 default=False,
358 )
359 suspectMaskName = pexConfig.Field(
360 dtype=str,
361 doc="Name of mask plane to use for suspect pixels",
362 default="SUSPECT",
363 )
364 numEdgeSuspect = pexConfig.Field(
365 dtype=int,
366 doc="Number of edge pixels to be flagged as untrustworthy.",
367 default=0,
368 )
369 edgeMaskLevel = pexConfig.ChoiceField(
370 dtype=str,
371 doc="Mask edge pixels in which coordinate frame: DETECTOR or AMP?",
372 default="DETECTOR",
373 allowed={
374 'DETECTOR': 'Mask only the edges of the full detector.',
375 'AMP': 'Mask edges of each amplifier.',
376 },
377 )
379 # Initial masking options.
380 doSetBadRegions = pexConfig.Field(
381 dtype=bool,
382 doc="Should we set the level of all BAD patches of the chip to the chip's average value?",
383 default=True,
384 )
385 badStatistic = pexConfig.ChoiceField(
386 dtype=str,
387 doc="How to estimate the average value for BAD regions.",
388 default='MEANCLIP',
389 allowed={
390 "MEANCLIP": "Correct using the (clipped) mean of good data",
391 "MEDIAN": "Correct using the median of the good data",
392 },
393 )
395 # Overscan subtraction configuration.
396 doOverscan = pexConfig.Field(
397 dtype=bool,
398 doc="Do overscan subtraction?",
399 default=True,
400 )
401 overscan = pexConfig.ConfigurableField(
402 target=OverscanCorrectionTask,
403 doc="Overscan subtraction task for image segments.",
404 )
406 overscanFitType = pexConfig.ChoiceField(
407 dtype=str,
408 doc="The method for fitting the overscan bias level.",
409 default='MEDIAN',
410 allowed={
411 "POLY": "Fit ordinary polynomial to the longest axis of the overscan region",
412 "CHEB": "Fit Chebyshev polynomial to the longest axis of the overscan region",
413 "LEG": "Fit Legendre polynomial to the longest axis of the overscan region",
414 "NATURAL_SPLINE": "Fit natural spline to the longest axis of the overscan region",
415 "CUBIC_SPLINE": "Fit cubic spline to the longest axis of the overscan region",
416 "AKIMA_SPLINE": "Fit Akima spline to the longest axis of the overscan region",
417 "MEAN": "Correct using the mean of the overscan region",
418 "MEANCLIP": "Correct using a clipped mean of the overscan region",
419 "MEDIAN": "Correct using the median of the overscan region",
420 "MEDIAN_PER_ROW": "Correct using the median per row of the overscan region",
421 },
422 deprecated=("Please configure overscan via the OverscanCorrectionConfig interface."
423 " This option will no longer be used, and will be removed after v20.")
424 )
425 overscanOrder = pexConfig.Field(
426 dtype=int,
427 doc=("Order of polynomial or to fit if overscan fit type is a polynomial, "
428 "or number of spline knots if overscan fit type is a spline."),
429 default=1,
430 deprecated=("Please configure overscan via the OverscanCorrectionConfig interface."
431 " This option will no longer be used, and will be removed after v20.")
432 )
433 overscanNumSigmaClip = pexConfig.Field(
434 dtype=float,
435 doc="Rejection threshold (sigma) for collapsing overscan before fit",
436 default=3.0,
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 overscanIsInt = pexConfig.Field(
441 dtype=bool,
442 doc="Treat overscan as an integer image for purposes of overscan.FitType=MEDIAN"
443 " and overscan.FitType=MEDIAN_PER_ROW.",
444 default=True,
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 # These options do not get deprecated, as they define how we slice up the image data.
449 overscanNumLeadingColumnsToSkip = pexConfig.Field(
450 dtype=int,
451 doc="Number of columns to skip in overscan, i.e. those closest to amplifier",
452 default=0,
453 )
454 overscanNumTrailingColumnsToSkip = pexConfig.Field(
455 dtype=int,
456 doc="Number of columns to skip in overscan, i.e. those farthest from amplifier",
457 default=0,
458 )
459 overscanMaxDev = pexConfig.Field( 459 ↛ exitline 459 didn't jump to the function exit
460 dtype=float,
461 doc="Maximum deviation from the median for overscan",
462 default=1000.0, check=lambda x: x > 0
463 )
464 overscanBiasJump = pexConfig.Field(
465 dtype=bool,
466 doc="Fit the overscan in a piecewise-fashion to correct for bias jumps?",
467 default=False,
468 )
469 overscanBiasJumpKeyword = pexConfig.Field(
470 dtype=str,
471 doc="Header keyword containing information about devices.",
472 default="NO_SUCH_KEY",
473 )
474 overscanBiasJumpDevices = pexConfig.ListField(
475 dtype=str,
476 doc="List of devices that need piecewise overscan correction.",
477 default=(),
478 )
479 overscanBiasJumpLocation = pexConfig.Field(
480 dtype=int,
481 doc="Location of bias jump along y-axis.",
482 default=0,
483 )
485 # Amplifier to CCD assembly configuration
486 doAssembleCcd = pexConfig.Field(
487 dtype=bool,
488 default=True,
489 doc="Assemble amp-level exposures into a ccd-level exposure?"
490 )
491 assembleCcd = pexConfig.ConfigurableField(
492 target=AssembleCcdTask,
493 doc="CCD assembly task",
494 )
496 # General calibration configuration.
497 doAssembleIsrExposures = pexConfig.Field(
498 dtype=bool,
499 default=False,
500 doc="Assemble amp-level calibration exposures into ccd-level exposure?"
501 )
502 doTrimToMatchCalib = pexConfig.Field(
503 dtype=bool,
504 default=False,
505 doc="Trim raw data to match calibration bounding boxes?"
506 )
508 # Bias subtraction.
509 doBias = pexConfig.Field(
510 dtype=bool,
511 doc="Apply bias frame correction?",
512 default=True,
513 )
514 biasDataProductName = pexConfig.Field(
515 dtype=str,
516 doc="Name of the bias data product",
517 default="bias",
518 )
520 # Variance construction
521 doVariance = pexConfig.Field(
522 dtype=bool,
523 doc="Calculate variance?",
524 default=True
525 )
526 gain = pexConfig.Field(
527 dtype=float,
528 doc="The gain to use if no Detector is present in the Exposure (ignored if NaN)",
529 default=float("NaN"),
530 )
531 readNoise = pexConfig.Field(
532 dtype=float,
533 doc="The read noise to use if no Detector is present in the Exposure",
534 default=0.0,
535 )
536 doEmpiricalReadNoise = pexConfig.Field(
537 dtype=bool,
538 default=False,
539 doc="Calculate empirical read noise instead of value from AmpInfo data?"
540 )
542 # Linearization.
543 doLinearize = pexConfig.Field(
544 dtype=bool,
545 doc="Correct for nonlinearity of the detector's response?",
546 default=True,
547 )
549 # Crosstalk.
550 doCrosstalk = pexConfig.Field(
551 dtype=bool,
552 doc="Apply intra-CCD crosstalk correction?",
553 default=False,
554 )
555 doCrosstalkBeforeAssemble = pexConfig.Field(
556 dtype=bool,
557 doc="Apply crosstalk correction before CCD assembly, and before trimming?",
558 default=False,
559 )
560 crosstalk = pexConfig.ConfigurableField(
561 target=CrosstalkTask,
562 doc="Intra-CCD crosstalk correction",
563 )
565 # Masking options.
566 doDefect = pexConfig.Field(
567 dtype=bool,
568 doc="Apply correction for CCD defects, e.g. hot pixels?",
569 default=True,
570 )
571 doNanMasking = pexConfig.Field(
572 dtype=bool,
573 doc="Mask NAN pixels?",
574 default=True,
575 )
576 doWidenSaturationTrails = pexConfig.Field(
577 dtype=bool,
578 doc="Widen bleed trails based on their width?",
579 default=True
580 )
582 # Brighter-Fatter correction.
583 doBrighterFatter = pexConfig.Field(
584 dtype=bool,
585 default=False,
586 doc="Apply the brighter fatter correction"
587 )
588 brighterFatterLevel = pexConfig.ChoiceField(
589 dtype=str,
590 default="DETECTOR",
591 doc="The level at which to correct for brighter-fatter.",
592 allowed={
593 "AMP": "Every amplifier treated separately.",
594 "DETECTOR": "One kernel per detector",
595 }
596 )
597 brighterFatterMaxIter = pexConfig.Field(
598 dtype=int,
599 default=10,
600 doc="Maximum number of iterations for the brighter fatter correction"
601 )
602 brighterFatterThreshold = pexConfig.Field(
603 dtype=float,
604 default=1000,
605 doc="Threshold used to stop iterating the brighter fatter correction. It is the "
606 " absolute value of the difference between the current corrected image and the one"
607 " from the previous iteration summed over all the pixels."
608 )
609 brighterFatterApplyGain = pexConfig.Field(
610 dtype=bool,
611 default=True,
612 doc="Should the gain be applied when applying the brighter fatter correction?"
613 )
614 brighterFatterMaskGrowSize = pexConfig.Field(
615 dtype=int,
616 default=0,
617 doc="Number of pixels to grow the masks listed in config.maskListToInterpolate "
618 " when brighter-fatter correction is applied."
619 )
621 # Dark subtraction.
622 doDark = pexConfig.Field(
623 dtype=bool,
624 doc="Apply dark frame correction?",
625 default=True,
626 )
627 darkDataProductName = pexConfig.Field(
628 dtype=str,
629 doc="Name of the dark data product",
630 default="dark",
631 )
633 # Camera-specific stray light removal.
634 doStrayLight = pexConfig.Field(
635 dtype=bool,
636 doc="Subtract stray light in the y-band (due to encoder LEDs)?",
637 default=False,
638 )
639 strayLight = pexConfig.ConfigurableField(
640 target=StrayLightTask,
641 doc="y-band stray light correction"
642 )
644 # Flat correction.
645 doFlat = pexConfig.Field(
646 dtype=bool,
647 doc="Apply flat field correction?",
648 default=True,
649 )
650 flatDataProductName = pexConfig.Field(
651 dtype=str,
652 doc="Name of the flat data product",
653 default="flat",
654 )
655 flatScalingType = pexConfig.ChoiceField(
656 dtype=str,
657 doc="The method for scaling the flat on the fly.",
658 default='USER',
659 allowed={
660 "USER": "Scale by flatUserScale",
661 "MEAN": "Scale by the inverse of the mean",
662 "MEDIAN": "Scale by the inverse of the median",
663 },
664 )
665 flatUserScale = pexConfig.Field(
666 dtype=float,
667 doc="If flatScalingType is 'USER' then scale flat by this amount; ignored otherwise",
668 default=1.0,
669 )
670 doTweakFlat = pexConfig.Field(
671 dtype=bool,
672 doc="Tweak flats to match observed amplifier ratios?",
673 default=False
674 )
676 # Amplifier normalization based on gains instead of using flats configuration.
677 doApplyGains = pexConfig.Field(
678 dtype=bool,
679 doc="Correct the amplifiers for their gains instead of applying flat correction",
680 default=False,
681 )
682 normalizeGains = pexConfig.Field(
683 dtype=bool,
684 doc="Normalize all the amplifiers in each CCD to have the same median value.",
685 default=False,
686 )
688 # Fringe correction.
689 doFringe = pexConfig.Field(
690 dtype=bool,
691 doc="Apply fringe correction?",
692 default=True,
693 )
694 fringe = pexConfig.ConfigurableField(
695 target=FringeTask,
696 doc="Fringe subtraction task",
697 )
698 fringeAfterFlat = pexConfig.Field(
699 dtype=bool,
700 doc="Do fringe subtraction after flat-fielding?",
701 default=True,
702 )
704 # Initial CCD-level background statistics options.
705 doMeasureBackground = pexConfig.Field(
706 dtype=bool,
707 doc="Measure the background level on the reduced image?",
708 default=False,
709 )
711 # Camera-specific masking configuration.
712 doCameraSpecificMasking = pexConfig.Field(
713 dtype=bool,
714 doc="Mask camera-specific bad regions?",
715 default=False,
716 )
717 masking = pexConfig.ConfigurableField(
718 target=MaskingTask,
719 doc="Masking task."
720 )
722 # Interpolation options.
724 doInterpolate = pexConfig.Field(
725 dtype=bool,
726 doc="Interpolate masked pixels?",
727 default=True,
728 )
729 doSaturationInterpolation = pexConfig.Field(
730 dtype=bool,
731 doc="Perform interpolation over pixels masked as saturated?"
732 " NB: This is independent of doSaturation; if that is False this plane"
733 " will likely be blank, resulting in a no-op here.",
734 default=True,
735 )
736 doNanInterpolation = pexConfig.Field(
737 dtype=bool,
738 doc="Perform interpolation over pixels masked as NaN?"
739 " NB: This is independent of doNanMasking; if that is False this plane"
740 " will likely be blank, resulting in a no-op here.",
741 default=True,
742 )
743 doNanInterpAfterFlat = pexConfig.Field(
744 dtype=bool,
745 doc=("If True, ensure we interpolate NaNs after flat-fielding, even if we "
746 "also have to interpolate them before flat-fielding."),
747 default=False,
748 )
749 maskListToInterpolate = pexConfig.ListField(
750 dtype=str,
751 doc="List of mask planes that should be interpolated.",
752 default=['SAT', 'BAD', 'UNMASKEDNAN'],
753 )
754 doSaveInterpPixels = pexConfig.Field(
755 dtype=bool,
756 doc="Save a copy of the pre-interpolated pixel values?",
757 default=False,
758 )
760 # Default photometric calibration options.
761 fluxMag0T1 = pexConfig.DictField(
762 keytype=str,
763 itemtype=float,
764 doc="The approximate flux of a zero-magnitude object in a one-second exposure, per filter.",
765 default=dict((f, pow(10.0, 0.4*m)) for f, m in (("Unknown", 28.0),
766 ))
767 )
768 defaultFluxMag0T1 = pexConfig.Field(
769 dtype=float,
770 doc="Default value for fluxMag0T1 (for an unrecognized filter).",
771 default=pow(10.0, 0.4*28.0)
772 )
774 # Vignette correction configuration.
775 doVignette = pexConfig.Field(
776 dtype=bool,
777 doc="Apply vignetting parameters?",
778 default=False,
779 )
780 vignette = pexConfig.ConfigurableField(
781 target=VignetteTask,
782 doc="Vignetting task.",
783 )
785 # Transmission curve configuration.
786 doAttachTransmissionCurve = pexConfig.Field(
787 dtype=bool,
788 default=False,
789 doc="Construct and attach a wavelength-dependent throughput curve for this CCD image?"
790 )
791 doUseOpticsTransmission = pexConfig.Field(
792 dtype=bool,
793 default=True,
794 doc="Load and use transmission_optics (if doAttachTransmissionCurve is True)?"
795 )
796 doUseFilterTransmission = pexConfig.Field(
797 dtype=bool,
798 default=True,
799 doc="Load and use transmission_filter (if doAttachTransmissionCurve is True)?"
800 )
801 doUseSensorTransmission = pexConfig.Field(
802 dtype=bool,
803 default=True,
804 doc="Load and use transmission_sensor (if doAttachTransmissionCurve is True)?"
805 )
806 doUseAtmosphereTransmission = pexConfig.Field(
807 dtype=bool,
808 default=True,
809 doc="Load and use transmission_atmosphere (if doAttachTransmissionCurve is True)?"
810 )
812 # Illumination correction.
813 doIlluminationCorrection = pexConfig.Field(
814 dtype=bool,
815 default=False,
816 doc="Perform illumination correction?"
817 )
818 illuminationCorrectionDataProductName = pexConfig.Field(
819 dtype=str,
820 doc="Name of the illumination correction data product.",
821 default="illumcor",
822 )
823 illumScale = pexConfig.Field(
824 dtype=float,
825 doc="Scale factor for the illumination correction.",
826 default=1.0,
827 )
828 illumFilters = pexConfig.ListField(
829 dtype=str,
830 default=[],
831 doc="Only perform illumination correction for these filters."
832 )
834 # Write the outputs to disk. If ISR is run as a subtask, this may not be needed.
835 doWrite = pexConfig.Field(
836 dtype=bool,
837 doc="Persist postISRCCD?",
838 default=True,
839 )
841 def validate(self):
842 super().validate()
843 if self.doFlat and self.doApplyGains:
844 raise ValueError("You may not specify both doFlat and doApplyGains")
845 if self.doSaturationInterpolation and "SAT" not in self.maskListToInterpolate:
846 self.config.maskListToInterpolate.append("SAT")
847 if self.doNanInterpolation and "UNMASKEDNAN" not in self.maskListToInterpolate:
848 self.config.maskListToInterpolate.append("UNMASKEDNAN")
851class IsrTask(pipeBase.PipelineTask, pipeBase.CmdLineTask):
852 """Apply common instrument signature correction algorithms to a raw frame.
854 The process for correcting imaging data is very similar from
855 camera to camera. This task provides a vanilla implementation of
856 doing these corrections, including the ability to turn certain
857 corrections off if they are not needed. The inputs to the primary
858 method, `run()`, are a raw exposure to be corrected and the
859 calibration data products. The raw input is a single chip sized
860 mosaic of all amps including overscans and other non-science
861 pixels. The method `runDataRef()` identifies and defines the
862 calibration data products, and is intended for use by a
863 `lsst.pipe.base.cmdLineTask.CmdLineTask` and takes as input only a
864 `daf.persistence.butlerSubset.ButlerDataRef`. This task may be
865 subclassed for different camera, although the most camera specific
866 methods have been split into subtasks that can be redirected
867 appropriately.
869 The __init__ method sets up the subtasks for ISR processing, using
870 the defaults from `lsst.ip.isr`.
872 Parameters
873 ----------
874 args : `list`
875 Positional arguments passed to the Task constructor. None used at this time.
876 kwargs : `dict`, optional
877 Keyword arguments passed on to the Task constructor. None used at this time.
878 """
879 ConfigClass = IsrTaskConfig
880 _DefaultName = "isr"
882 def __init__(self, **kwargs):
883 super().__init__(**kwargs)
884 self.makeSubtask("assembleCcd")
885 self.makeSubtask("crosstalk")
886 self.makeSubtask("strayLight")
887 self.makeSubtask("fringe")
888 self.makeSubtask("masking")
889 self.makeSubtask("overscan")
890 self.makeSubtask("vignette")
892 def runQuantum(self, butlerQC, inputRefs, outputRefs):
893 inputs = butlerQC.get(inputRefs)
895 try:
896 inputs['detectorNum'] = inputRefs.ccdExposure.dataId['detector']
897 except Exception as e:
898 raise ValueError("Failure to find valid detectorNum value for Dataset %s: %s." %
899 (inputRefs, e))
901 inputs['isGen3'] = True
903 detector = inputs['ccdExposure'].getDetector()
905 if self.config.doCrosstalk is True:
906 # Crosstalk sources need to be defined by the pipeline
907 # yaml if they exist.
908 if 'crosstalk' in inputs and inputs['crosstalk'] is not None:
909 if not isinstance(inputs['crosstalk'], CrosstalkCalib):
910 inputs['crosstalk'] = CrosstalkCalib.fromTable(inputs['crosstalk'])
911 else:
912 coeffVector = (self.config.crosstalk.crosstalkValues
913 if self.config.crosstalk.useConfigCoefficients else None)
914 crosstalkCalib = CrosstalkCalib().fromDetector(detector, coeffVector=coeffVector)
915 inputs['crosstalk'] = crosstalkCalib
916 if inputs['crosstalk'].interChip and len(inputs['crosstalk'].interChip) > 0:
917 if 'crosstalkSources' not in inputs:
918 self.log.warn("No crosstalkSources found for chip with interChip terms!")
920 if self.doLinearize(detector) is True:
921 if 'linearizer' in inputs and isinstance(inputs['linearizer'], dict):
922 linearizer = linearize.Linearizer(detector=detector, log=self.log)
923 linearizer.fromYaml(inputs['linearizer'])
924 else:
925 linearizer = linearize.Linearizer(table=inputs.get('linearizer', None), detector=detector,
926 log=self.log)
927 inputs['linearizer'] = linearizer
929 if self.config.doDefect is True:
930 if "defects" in inputs and inputs['defects'] is not None:
931 # defects is loaded as a BaseCatalog with columns x0, y0, width, height.
932 # masking expects a list of defects defined by their bounding box
933 if not isinstance(inputs["defects"], Defects):
934 inputs["defects"] = Defects.fromTable(inputs["defects"])
936 # Load the correct style of brighter fatter kernel, and repack
937 # the information as a numpy array.
938 if self.config.doBrighterFatter:
939 brighterFatterKernel = inputs.pop('newBFKernel', None)
940 if brighterFatterKernel is None:
941 brighterFatterKernel = inputs.get('bfKernel', None)
943 if brighterFatterKernel is not None and not isinstance(brighterFatterKernel, numpy.ndarray):
944 detId = detector.getId()
945 inputs['bfGains'] = brighterFatterKernel.gain
946 # If the kernel is not an ndarray, it's the cp_pipe version
947 # so extract the kernel for this detector, or raise an error
948 if self.config.brighterFatterLevel == 'DETECTOR':
949 if brighterFatterKernel.detectorKernel:
950 inputs['bfKernel'] = brighterFatterKernel.detectorKernel[detId]
951 elif brighterFatterKernel.detectorKernelFromAmpKernels:
952 inputs['bfKernel'] = brighterFatterKernel.detectorKernelFromAmpKernels[detId]
953 else:
954 raise RuntimeError("Failed to extract kernel from new-style BF kernel.")
955 else:
956 # TODO DM-15631 for implementing this
957 raise NotImplementedError("Per-amplifier brighter-fatter correction not implemented")
959 if self.config.doFringe is True and self.fringe.checkFilter(inputs['ccdExposure']):
960 expId = inputs['ccdExposure'].getInfo().getVisitInfo().getExposureId()
961 inputs['fringes'] = self.fringe.loadFringes(inputs['fringes'],
962 expId=expId,
963 assembler=self.assembleCcd
964 if self.config.doAssembleIsrExposures else None)
965 else:
966 inputs['fringes'] = pipeBase.Struct(fringes=None)
968 if self.config.doStrayLight is True and self.strayLight.checkFilter(inputs['ccdExposure']):
969 if 'strayLightData' not in inputs:
970 inputs['strayLightData'] = None
972 outputs = self.run(**inputs)
973 butlerQC.put(outputs, outputRefs)
975 def readIsrData(self, dataRef, rawExposure):
976 """!Retrieve necessary frames for instrument signature removal.
978 Pre-fetching all required ISR data products limits the IO
979 required by the ISR. Any conflict between the calibration data
980 available and that needed for ISR is also detected prior to
981 doing processing, allowing it to fail quickly.
983 Parameters
984 ----------
985 dataRef : `daf.persistence.butlerSubset.ButlerDataRef`
986 Butler reference of the detector data to be processed
987 rawExposure : `afw.image.Exposure`
988 The raw exposure that will later be corrected with the
989 retrieved calibration data; should not be modified in this
990 method.
992 Returns
993 -------
994 result : `lsst.pipe.base.Struct`
995 Result struct with components (which may be `None`):
996 - ``bias``: bias calibration frame (`afw.image.Exposure`)
997 - ``linearizer``: functor for linearization (`ip.isr.linearize.LinearizeBase`)
998 - ``crosstalkSources``: list of possible crosstalk sources (`list`)
999 - ``dark``: dark calibration frame (`afw.image.Exposure`)
1000 - ``flat``: flat calibration frame (`afw.image.Exposure`)
1001 - ``bfKernel``: Brighter-Fatter kernel (`numpy.ndarray`)
1002 - ``defects``: list of defects (`lsst.meas.algorithms.Defects`)
1003 - ``fringes``: `lsst.pipe.base.Struct` with components:
1004 - ``fringes``: fringe calibration frame (`afw.image.Exposure`)
1005 - ``seed``: random seed derived from the ccdExposureId for random
1006 number generator (`uint32`).
1007 - ``opticsTransmission``: `lsst.afw.image.TransmissionCurve`
1008 A ``TransmissionCurve`` that represents the throughput of the optics,
1009 to be evaluated in focal-plane coordinates.
1010 - ``filterTransmission`` : `lsst.afw.image.TransmissionCurve`
1011 A ``TransmissionCurve`` that represents the throughput of the filter
1012 itself, to be evaluated in focal-plane coordinates.
1013 - ``sensorTransmission`` : `lsst.afw.image.TransmissionCurve`
1014 A ``TransmissionCurve`` that represents the throughput of the sensor
1015 itself, to be evaluated in post-assembly trimmed detector coordinates.
1016 - ``atmosphereTransmission`` : `lsst.afw.image.TransmissionCurve`
1017 A ``TransmissionCurve`` that represents the throughput of the
1018 atmosphere, assumed to be spatially constant.
1019 - ``strayLightData`` : `object`
1020 An opaque object containing calibration information for
1021 stray-light correction. If `None`, no correction will be
1022 performed.
1023 - ``illumMaskedImage`` : illumination correction image (`lsst.afw.image.MaskedImage`)
1025 Raises
1026 ------
1027 NotImplementedError :
1028 Raised if a per-amplifier brighter-fatter kernel is requested by the configuration.
1029 """
1030 try:
1031 dateObs = rawExposure.getInfo().getVisitInfo().getDate()
1032 dateObs = dateObs.toPython().isoformat()
1033 except RuntimeError:
1034 self.log.warn("Unable to identify dateObs for rawExposure.")
1035 dateObs = None
1037 ccd = rawExposure.getDetector()
1038 filterName = afwImage.Filter(rawExposure.getFilter().getId()).getName() # Canonical name for filter
1039 rawExposure.mask.addMaskPlane("UNMASKEDNAN") # needed to match pre DM-15862 processing.
1040 biasExposure = (self.getIsrExposure(dataRef, self.config.biasDataProductName)
1041 if self.config.doBias else None)
1042 # immediate=True required for functors and linearizers are functors; see ticket DM-6515
1043 linearizer = (dataRef.get("linearizer", immediate=True)
1044 if self.doLinearize(ccd) else None)
1045 if linearizer is not None and not isinstance(linearizer, numpy.ndarray):
1046 linearizer.log = self.log
1047 if isinstance(linearizer, numpy.ndarray):
1048 linearizer = linearize.Linearizer(table=linearizer, detector=ccd)
1050 crosstalkCalib = None
1051 if self.config.doCrosstalk:
1052 try:
1053 crosstalkCalib = dataRef.get("crosstalk", immediate=True)
1054 except NoResults:
1055 coeffVector = (self.config.crosstalk.crosstalkValues
1056 if self.config.crosstalk.useConfigCoefficients else None)
1057 crosstalkCalib = CrosstalkCalib().fromDetector(ccd, coeffVector=coeffVector)
1058 crosstalkSources = (self.crosstalk.prepCrosstalk(dataRef, crosstalkCalib)
1059 if self.config.doCrosstalk else None)
1061 darkExposure = (self.getIsrExposure(dataRef, self.config.darkDataProductName)
1062 if self.config.doDark else None)
1063 flatExposure = (self.getIsrExposure(dataRef, self.config.flatDataProductName,
1064 dateObs=dateObs)
1065 if self.config.doFlat else None)
1067 brighterFatterKernel = None
1068 brighterFatterGains = None
1069 if self.config.doBrighterFatter is True:
1070 try:
1071 # Use the new-style cp_pipe version of the kernel if it exists
1072 # If using a new-style kernel, always use the self-consistent
1073 # gains, i.e. the ones inside the kernel object itself
1074 brighterFatterKernel = dataRef.get("brighterFatterKernel")
1075 brighterFatterGains = brighterFatterKernel.gain
1076 self.log.info("New style bright-fatter kernel (brighterFatterKernel) loaded")
1077 except NoResults:
1078 try: # Fall back to the old-style numpy-ndarray style kernel if necessary.
1079 brighterFatterKernel = dataRef.get("bfKernel")
1080 self.log.info("Old style bright-fatter kernel (np.array) loaded")
1081 except NoResults:
1082 brighterFatterKernel = None
1083 if brighterFatterKernel is not None and not isinstance(brighterFatterKernel, numpy.ndarray):
1084 # If the kernel is not an ndarray, it's the cp_pipe version
1085 # so extract the kernel for this detector, or raise an error
1086 if self.config.brighterFatterLevel == 'DETECTOR':
1087 if brighterFatterKernel.detectorKernel:
1088 brighterFatterKernel = brighterFatterKernel.detectorKernel[ccd.getId()]
1089 elif brighterFatterKernel.detectorKernelFromAmpKernels:
1090 brighterFatterKernel = brighterFatterKernel.detectorKernelFromAmpKernels[ccd.getId()]
1091 else:
1092 raise RuntimeError("Failed to extract kernel from new-style BF kernel.")
1093 else:
1094 # TODO DM-15631 for implementing this
1095 raise NotImplementedError("Per-amplifier brighter-fatter correction not implemented")
1097 defectList = (dataRef.get("defects")
1098 if self.config.doDefect else None)
1099 fringeStruct = (self.fringe.readFringes(dataRef, assembler=self.assembleCcd
1100 if self.config.doAssembleIsrExposures else None)
1101 if self.config.doFringe and self.fringe.checkFilter(rawExposure)
1102 else pipeBase.Struct(fringes=None))
1104 if self.config.doAttachTransmissionCurve:
1105 opticsTransmission = (dataRef.get("transmission_optics")
1106 if self.config.doUseOpticsTransmission else None)
1107 filterTransmission = (dataRef.get("transmission_filter")
1108 if self.config.doUseFilterTransmission else None)
1109 sensorTransmission = (dataRef.get("transmission_sensor")
1110 if self.config.doUseSensorTransmission else None)
1111 atmosphereTransmission = (dataRef.get("transmission_atmosphere")
1112 if self.config.doUseAtmosphereTransmission else None)
1113 else:
1114 opticsTransmission = None
1115 filterTransmission = None
1116 sensorTransmission = None
1117 atmosphereTransmission = None
1119 if self.config.doStrayLight:
1120 strayLightData = self.strayLight.readIsrData(dataRef, rawExposure)
1121 else:
1122 strayLightData = None
1124 illumMaskedImage = (self.getIsrExposure(dataRef,
1125 self.config.illuminationCorrectionDataProductName).getMaskedImage()
1126 if (self.config.doIlluminationCorrection
1127 and filterName in self.config.illumFilters)
1128 else None)
1130 # Struct should include only kwargs to run()
1131 return pipeBase.Struct(bias=biasExposure,
1132 linearizer=linearizer,
1133 crosstalk=crosstalkCalib,
1134 crosstalkSources=crosstalkSources,
1135 dark=darkExposure,
1136 flat=flatExposure,
1137 bfKernel=brighterFatterKernel,
1138 bfGains=brighterFatterGains,
1139 defects=defectList,
1140 fringes=fringeStruct,
1141 opticsTransmission=opticsTransmission,
1142 filterTransmission=filterTransmission,
1143 sensorTransmission=sensorTransmission,
1144 atmosphereTransmission=atmosphereTransmission,
1145 strayLightData=strayLightData,
1146 illumMaskedImage=illumMaskedImage
1147 )
1149 @pipeBase.timeMethod
1150 def run(self, ccdExposure, camera=None, bias=None, linearizer=None,
1151 crosstalk=None, crosstalkSources=None,
1152 dark=None, flat=None, bfKernel=None, bfGains=None, defects=None,
1153 fringes=pipeBase.Struct(fringes=None), opticsTransmission=None, filterTransmission=None,
1154 sensorTransmission=None, atmosphereTransmission=None,
1155 detectorNum=None, strayLightData=None, illumMaskedImage=None,
1156 isGen3=False,
1157 ):
1158 """!Perform instrument signature removal on an exposure.
1160 Steps included in the ISR processing, in order performed, are:
1161 - saturation and suspect pixel masking
1162 - overscan subtraction
1163 - CCD assembly of individual amplifiers
1164 - bias subtraction
1165 - variance image construction
1166 - linearization of non-linear response
1167 - crosstalk masking
1168 - brighter-fatter correction
1169 - dark subtraction
1170 - fringe correction
1171 - stray light subtraction
1172 - flat correction
1173 - masking of known defects and camera specific features
1174 - vignette calculation
1175 - appending transmission curve and distortion model
1177 Parameters
1178 ----------
1179 ccdExposure : `lsst.afw.image.Exposure`
1180 The raw exposure that is to be run through ISR. The
1181 exposure is modified by this method.
1182 camera : `lsst.afw.cameraGeom.Camera`, optional
1183 The camera geometry for this exposure. Required if ``isGen3`` is
1184 `True` and one or more of ``ccdExposure``, ``bias``, ``dark``, or
1185 ``flat`` does not have an associated detector.
1186 bias : `lsst.afw.image.Exposure`, optional
1187 Bias calibration frame.
1188 linearizer : `lsst.ip.isr.linearize.LinearizeBase`, optional
1189 Functor for linearization.
1190 crosstalk : `lsst.ip.isr.crosstalk.CrosstalkCalib`, optional
1191 Calibration for crosstalk.
1192 crosstalkSources : `list`, optional
1193 List of possible crosstalk sources.
1194 dark : `lsst.afw.image.Exposure`, optional
1195 Dark calibration frame.
1196 flat : `lsst.afw.image.Exposure`, optional
1197 Flat calibration frame.
1198 bfKernel : `numpy.ndarray`, optional
1199 Brighter-fatter kernel.
1200 bfGains : `dict` of `float`, optional
1201 Gains used to override the detector's nominal gains for the
1202 brighter-fatter correction. A dict keyed by amplifier name for
1203 the detector in question.
1204 defects : `lsst.meas.algorithms.Defects`, optional
1205 List of defects.
1206 fringes : `lsst.pipe.base.Struct`, optional
1207 Struct containing the fringe correction data, with
1208 elements:
1209 - ``fringes``: fringe calibration frame (`afw.image.Exposure`)
1210 - ``seed``: random seed derived from the ccdExposureId for random
1211 number generator (`uint32`)
1212 opticsTransmission: `lsst.afw.image.TransmissionCurve`, optional
1213 A ``TransmissionCurve`` that represents the throughput of the optics,
1214 to be evaluated in focal-plane coordinates.
1215 filterTransmission : `lsst.afw.image.TransmissionCurve`
1216 A ``TransmissionCurve`` that represents the throughput of the filter
1217 itself, to be evaluated in focal-plane coordinates.
1218 sensorTransmission : `lsst.afw.image.TransmissionCurve`
1219 A ``TransmissionCurve`` that represents the throughput of the sensor
1220 itself, to be evaluated in post-assembly trimmed detector coordinates.
1221 atmosphereTransmission : `lsst.afw.image.TransmissionCurve`
1222 A ``TransmissionCurve`` that represents the throughput of the
1223 atmosphere, assumed to be spatially constant.
1224 detectorNum : `int`, optional
1225 The integer number for the detector to process.
1226 isGen3 : bool, optional
1227 Flag this call to run() as using the Gen3 butler environment.
1228 strayLightData : `object`, optional
1229 Opaque object containing calibration information for stray-light
1230 correction. If `None`, no correction will be performed.
1231 illumMaskedImage : `lsst.afw.image.MaskedImage`, optional
1232 Illumination correction image.
1234 Returns
1235 -------
1236 result : `lsst.pipe.base.Struct`
1237 Result struct with component:
1238 - ``exposure`` : `afw.image.Exposure`
1239 The fully ISR corrected exposure.
1240 - ``outputExposure`` : `afw.image.Exposure`
1241 An alias for `exposure`
1242 - ``ossThumb`` : `numpy.ndarray`
1243 Thumbnail image of the exposure after overscan subtraction.
1244 - ``flattenedThumb`` : `numpy.ndarray`
1245 Thumbnail image of the exposure after flat-field correction.
1247 Raises
1248 ------
1249 RuntimeError
1250 Raised if a configuration option is set to True, but the
1251 required calibration data has not been specified.
1253 Notes
1254 -----
1255 The current processed exposure can be viewed by setting the
1256 appropriate lsstDebug entries in the `debug.display`
1257 dictionary. The names of these entries correspond to some of
1258 the IsrTaskConfig Boolean options, with the value denoting the
1259 frame to use. The exposure is shown inside the matching
1260 option check and after the processing of that step has
1261 finished. The steps with debug points are:
1263 doAssembleCcd
1264 doBias
1265 doCrosstalk
1266 doBrighterFatter
1267 doDark
1268 doFringe
1269 doStrayLight
1270 doFlat
1272 In addition, setting the "postISRCCD" entry displays the
1273 exposure after all ISR processing has finished.
1275 """
1277 if isGen3 is True:
1278 # Gen3 currently cannot automatically do configuration overrides.
1279 # DM-15257 looks to discuss this issue.
1280 # Configure input exposures;
1281 if detectorNum is None:
1282 raise RuntimeError("Must supply the detectorNum if running as Gen3.")
1284 ccdExposure = self.ensureExposure(ccdExposure, camera, detectorNum)
1285 bias = self.ensureExposure(bias, camera, detectorNum)
1286 dark = self.ensureExposure(dark, camera, detectorNum)
1287 flat = self.ensureExposure(flat, camera, detectorNum)
1288 else:
1289 if isinstance(ccdExposure, ButlerDataRef):
1290 return self.runDataRef(ccdExposure)
1292 ccd = ccdExposure.getDetector()
1293 filterName = afwImage.Filter(ccdExposure.getFilter().getId()).getName() # Canonical name for filter
1295 if not ccd:
1296 assert not self.config.doAssembleCcd, "You need a Detector to run assembleCcd."
1297 ccd = [FakeAmp(ccdExposure, self.config)]
1299 # Validate Input
1300 if self.config.doBias and bias is None:
1301 raise RuntimeError("Must supply a bias exposure if config.doBias=True.")
1302 if self.doLinearize(ccd) and linearizer is None:
1303 raise RuntimeError("Must supply a linearizer if config.doLinearize=True for this detector.")
1304 if self.config.doBrighterFatter and bfKernel is None:
1305 raise RuntimeError("Must supply a kernel if config.doBrighterFatter=True.")
1306 if self.config.doDark and dark is None:
1307 raise RuntimeError("Must supply a dark exposure if config.doDark=True.")
1308 if self.config.doFlat and flat is None:
1309 raise RuntimeError("Must supply a flat exposure if config.doFlat=True.")
1310 if self.config.doDefect and defects is None:
1311 raise RuntimeError("Must supply defects if config.doDefect=True.")
1312 if (self.config.doFringe and filterName in self.fringe.config.filters
1313 and fringes.fringes is None):
1314 # The `fringes` object needs to be a pipeBase.Struct, as
1315 # we use it as a `dict` for the parameters of
1316 # `FringeTask.run()`. The `fringes.fringes` `list` may
1317 # not be `None` if `doFringe=True`. Otherwise, raise.
1318 raise RuntimeError("Must supply fringe exposure as a pipeBase.Struct.")
1319 if (self.config.doIlluminationCorrection and filterName in self.config.illumFilters
1320 and illumMaskedImage is None):
1321 raise RuntimeError("Must supply an illumcor if config.doIlluminationCorrection=True.")
1323 # Begin ISR processing.
1324 if self.config.doConvertIntToFloat:
1325 self.log.info("Converting exposure to floating point values.")
1326 ccdExposure = self.convertIntToFloat(ccdExposure)
1328 # Amplifier level processing.
1329 overscans = []
1330 for amp in ccd:
1331 # if ccdExposure is one amp, check for coverage to prevent performing ops multiple times
1332 if ccdExposure.getBBox().contains(amp.getBBox()):
1333 # Check for fully masked bad amplifiers, and generate masks for SUSPECT and SATURATED values.
1334 badAmp = self.maskAmplifier(ccdExposure, amp, defects)
1336 if self.config.doOverscan and not badAmp:
1337 # Overscan correction on amp-by-amp basis.
1338 overscanResults = self.overscanCorrection(ccdExposure, amp)
1339 self.log.debug("Corrected overscan for amplifier %s.", amp.getName())
1340 if overscanResults is not None and \
1341 self.config.qa is not None and self.config.qa.saveStats is True:
1342 if isinstance(overscanResults.overscanFit, float):
1343 qaMedian = overscanResults.overscanFit
1344 qaStdev = float("NaN")
1345 else:
1346 qaStats = afwMath.makeStatistics(overscanResults.overscanFit,
1347 afwMath.MEDIAN | afwMath.STDEVCLIP)
1348 qaMedian = qaStats.getValue(afwMath.MEDIAN)
1349 qaStdev = qaStats.getValue(afwMath.STDEVCLIP)
1351 self.metadata.set(f"ISR OSCAN {amp.getName()} MEDIAN", qaMedian)
1352 self.metadata.set(f"ISR OSCAN {amp.getName()} STDEV", qaStdev)
1353 self.log.debug(" Overscan stats for amplifer %s: %f +/- %f",
1354 amp.getName(), qaMedian, qaStdev)
1355 ccdExposure.getMetadata().set('OVERSCAN', "Overscan corrected")
1356 else:
1357 if badAmp:
1358 self.log.warn("Amplifier %s is bad.", amp.getName())
1359 overscanResults = None
1361 overscans.append(overscanResults if overscanResults is not None else None)
1362 else:
1363 self.log.info("Skipped OSCAN for %s.", amp.getName())
1365 if self.config.doCrosstalk and self.config.doCrosstalkBeforeAssemble:
1366 self.log.info("Applying crosstalk correction.")
1367 self.crosstalk.run(ccdExposure, crosstalk=crosstalk,
1368 crosstalkSources=crosstalkSources)
1369 self.debugView(ccdExposure, "doCrosstalk")
1371 if self.config.doAssembleCcd:
1372 self.log.info("Assembling CCD from amplifiers.")
1373 ccdExposure = self.assembleCcd.assembleCcd(ccdExposure)
1375 if self.config.expectWcs and not ccdExposure.getWcs():
1376 self.log.warn("No WCS found in input exposure.")
1377 self.debugView(ccdExposure, "doAssembleCcd")
1379 ossThumb = None
1380 if self.config.qa.doThumbnailOss:
1381 ossThumb = isrQa.makeThumbnail(ccdExposure, isrQaConfig=self.config.qa)
1383 if self.config.doBias:
1384 self.log.info("Applying bias correction.")
1385 isrFunctions.biasCorrection(ccdExposure.getMaskedImage(), bias.getMaskedImage(),
1386 trimToFit=self.config.doTrimToMatchCalib)
1387 self.debugView(ccdExposure, "doBias")
1389 if self.config.doVariance:
1390 for amp, overscanResults in zip(ccd, overscans):
1391 if ccdExposure.getBBox().contains(amp.getBBox()):
1392 self.log.debug("Constructing variance map for amplifer %s.", amp.getName())
1393 ampExposure = ccdExposure.Factory(ccdExposure, amp.getBBox())
1394 if overscanResults is not None:
1395 self.updateVariance(ampExposure, amp,
1396 overscanImage=overscanResults.overscanImage)
1397 else:
1398 self.updateVariance(ampExposure, amp,
1399 overscanImage=None)
1400 if self.config.qa is not None and self.config.qa.saveStats is True:
1401 qaStats = afwMath.makeStatistics(ampExposure.getVariance(),
1402 afwMath.MEDIAN | afwMath.STDEVCLIP)
1403 self.metadata.set(f"ISR VARIANCE {amp.getName()} MEDIAN",
1404 qaStats.getValue(afwMath.MEDIAN))
1405 self.metadata.set(f"ISR VARIANCE {amp.getName()} STDEV",
1406 qaStats.getValue(afwMath.STDEVCLIP))
1407 self.log.debug(" Variance stats for amplifer %s: %f +/- %f.",
1408 amp.getName(), qaStats.getValue(afwMath.MEDIAN),
1409 qaStats.getValue(afwMath.STDEVCLIP))
1411 if self.doLinearize(ccd):
1412 self.log.info("Applying linearizer.")
1413 linearizer.applyLinearity(image=ccdExposure.getMaskedImage().getImage(),
1414 detector=ccd, log=self.log)
1416 if self.config.doCrosstalk and not self.config.doCrosstalkBeforeAssemble:
1417 self.log.info("Applying crosstalk correction.")
1418 self.crosstalk.run(ccdExposure, crosstalk=crosstalk,
1419 crosstalkSources=crosstalkSources, isTrimmed=True)
1420 self.debugView(ccdExposure, "doCrosstalk")
1422 # Masking block. Optionally mask known defects, NAN pixels, widen trails, and do
1423 # anything else the camera needs. Saturated and suspect pixels have already been masked.
1424 if self.config.doDefect:
1425 self.log.info("Masking defects.")
1426 self.maskDefect(ccdExposure, defects)
1428 if self.config.numEdgeSuspect > 0:
1429 self.log.info("Masking edges as SUSPECT.")
1430 self.maskEdges(ccdExposure, numEdgePixels=self.config.numEdgeSuspect,
1431 maskPlane="SUSPECT", level=self.config.edgeMaskLevel)
1433 if self.config.doNanMasking:
1434 self.log.info("Masking NAN value pixels.")
1435 self.maskNan(ccdExposure)
1437 if self.config.doWidenSaturationTrails:
1438 self.log.info("Widening saturation trails.")
1439 isrFunctions.widenSaturationTrails(ccdExposure.getMaskedImage().getMask())
1441 if self.config.doCameraSpecificMasking:
1442 self.log.info("Masking regions for camera specific reasons.")
1443 self.masking.run(ccdExposure)
1445 if self.config.doBrighterFatter:
1446 # We need to apply flats and darks before we can interpolate, and we
1447 # need to interpolate before we do B-F, but we do B-F without the
1448 # flats and darks applied so we can work in units of electrons or holes.
1449 # This context manager applies and then removes the darks and flats.
1450 #
1451 # We also do not want to interpolate values here, so operate on temporary
1452 # images so we can apply only the BF-correction and roll back the
1453 # interpolation.
1454 interpExp = ccdExposure.clone()
1455 with self.flatContext(interpExp, flat, dark):
1456 isrFunctions.interpolateFromMask(
1457 maskedImage=interpExp.getMaskedImage(),
1458 fwhm=self.config.fwhm,
1459 growSaturatedFootprints=self.config.growSaturationFootprintSize,
1460 maskNameList=self.config.maskListToInterpolate
1461 )
1462 bfExp = interpExp.clone()
1464 self.log.info("Applying brighter fatter correction using kernel type %s / gains %s.",
1465 type(bfKernel), type(bfGains))
1466 bfResults = isrFunctions.brighterFatterCorrection(bfExp, bfKernel,
1467 self.config.brighterFatterMaxIter,
1468 self.config.brighterFatterThreshold,
1469 self.config.brighterFatterApplyGain,
1470 bfGains)
1471 if bfResults[1] == self.config.brighterFatterMaxIter:
1472 self.log.warn("Brighter fatter correction did not converge, final difference %f.",
1473 bfResults[0])
1474 else:
1475 self.log.info("Finished brighter fatter correction in %d iterations.",
1476 bfResults[1])
1477 image = ccdExposure.getMaskedImage().getImage()
1478 bfCorr = bfExp.getMaskedImage().getImage()
1479 bfCorr -= interpExp.getMaskedImage().getImage()
1480 image += bfCorr
1482 # Applying the brighter-fatter correction applies a
1483 # convolution to the science image. At the edges this
1484 # convolution may not have sufficient valid pixels to
1485 # produce a valid correction. Mark pixels within the size
1486 # of the brighter-fatter kernel as EDGE to warn of this
1487 # fact.
1488 self.log.info("Ensuring image edges are masked as SUSPECT to the brighter-fatter kernel size.")
1489 self.maskEdges(ccdExposure, numEdgePixels=numpy.max(bfKernel.shape) // 2,
1490 maskPlane="EDGE")
1492 if self.config.brighterFatterMaskGrowSize > 0:
1493 self.log.info("Growing masks to account for brighter-fatter kernel convolution.")
1494 for maskPlane in self.config.maskListToInterpolate:
1495 isrFunctions.growMasks(ccdExposure.getMask(),
1496 radius=self.config.brighterFatterMaskGrowSize,
1497 maskNameList=maskPlane,
1498 maskValue=maskPlane)
1500 self.debugView(ccdExposure, "doBrighterFatter")
1502 if self.config.doDark:
1503 self.log.info("Applying dark correction.")
1504 self.darkCorrection(ccdExposure, dark)
1505 self.debugView(ccdExposure, "doDark")
1507 if self.config.doFringe and not self.config.fringeAfterFlat:
1508 self.log.info("Applying fringe correction before flat.")
1509 self.fringe.run(ccdExposure, **fringes.getDict())
1510 self.debugView(ccdExposure, "doFringe")
1512 if self.config.doStrayLight and self.strayLight.check(ccdExposure):
1513 self.log.info("Checking strayLight correction.")
1514 self.strayLight.run(ccdExposure, strayLightData)
1515 self.debugView(ccdExposure, "doStrayLight")
1517 if self.config.doFlat:
1518 self.log.info("Applying flat correction.")
1519 self.flatCorrection(ccdExposure, flat)
1520 self.debugView(ccdExposure, "doFlat")
1522 if self.config.doApplyGains:
1523 self.log.info("Applying gain correction instead of flat.")
1524 isrFunctions.applyGains(ccdExposure, self.config.normalizeGains)
1526 if self.config.doFringe and self.config.fringeAfterFlat:
1527 self.log.info("Applying fringe correction after flat.")
1528 self.fringe.run(ccdExposure, **fringes.getDict())
1530 if self.config.doVignette:
1531 self.log.info("Constructing Vignette polygon.")
1532 self.vignettePolygon = self.vignette.run(ccdExposure)
1534 if self.config.vignette.doWriteVignettePolygon:
1535 self.setValidPolygonIntersect(ccdExposure, self.vignettePolygon)
1537 if self.config.doAttachTransmissionCurve:
1538 self.log.info("Adding transmission curves.")
1539 isrFunctions.attachTransmissionCurve(ccdExposure, opticsTransmission=opticsTransmission,
1540 filterTransmission=filterTransmission,
1541 sensorTransmission=sensorTransmission,
1542 atmosphereTransmission=atmosphereTransmission)
1544 flattenedThumb = None
1545 if self.config.qa.doThumbnailFlattened:
1546 flattenedThumb = isrQa.makeThumbnail(ccdExposure, isrQaConfig=self.config.qa)
1548 if self.config.doIlluminationCorrection and filterName in self.config.illumFilters:
1549 self.log.info("Performing illumination correction.")
1550 isrFunctions.illuminationCorrection(ccdExposure.getMaskedImage(),
1551 illumMaskedImage, illumScale=self.config.illumScale,
1552 trimToFit=self.config.doTrimToMatchCalib)
1554 preInterpExp = None
1555 if self.config.doSaveInterpPixels:
1556 preInterpExp = ccdExposure.clone()
1558 # Reset and interpolate bad pixels.
1559 #
1560 # Large contiguous bad regions (which should have the BAD mask
1561 # bit set) should have their values set to the image median.
1562 # This group should include defects and bad amplifiers. As the
1563 # area covered by these defects are large, there's little
1564 # reason to expect that interpolation would provide a more
1565 # useful value.
1566 #
1567 # Smaller defects can be safely interpolated after the larger
1568 # regions have had their pixel values reset. This ensures
1569 # that the remaining defects adjacent to bad amplifiers (as an
1570 # example) do not attempt to interpolate extreme values.
1571 if self.config.doSetBadRegions:
1572 badPixelCount, badPixelValue = isrFunctions.setBadRegions(ccdExposure)
1573 if badPixelCount > 0:
1574 self.log.info("Set %d BAD pixels to %f.", badPixelCount, badPixelValue)
1576 if self.config.doInterpolate:
1577 self.log.info("Interpolating masked pixels.")
1578 isrFunctions.interpolateFromMask(
1579 maskedImage=ccdExposure.getMaskedImage(),
1580 fwhm=self.config.fwhm,
1581 growSaturatedFootprints=self.config.growSaturationFootprintSize,
1582 maskNameList=list(self.config.maskListToInterpolate)
1583 )
1585 self.roughZeroPoint(ccdExposure)
1587 if self.config.doMeasureBackground:
1588 self.log.info("Measuring background level.")
1589 self.measureBackground(ccdExposure, self.config.qa)
1591 if self.config.qa is not None and self.config.qa.saveStats is True:
1592 for amp in ccd:
1593 ampExposure = ccdExposure.Factory(ccdExposure, amp.getBBox())
1594 qaStats = afwMath.makeStatistics(ampExposure.getImage(),
1595 afwMath.MEDIAN | afwMath.STDEVCLIP)
1596 self.metadata.set("ISR BACKGROUND {} MEDIAN".format(amp.getName()),
1597 qaStats.getValue(afwMath.MEDIAN))
1598 self.metadata.set("ISR BACKGROUND {} STDEV".format(amp.getName()),
1599 qaStats.getValue(afwMath.STDEVCLIP))
1600 self.log.debug(" Background stats for amplifer %s: %f +/- %f",
1601 amp.getName(), qaStats.getValue(afwMath.MEDIAN),
1602 qaStats.getValue(afwMath.STDEVCLIP))
1604 self.debugView(ccdExposure, "postISRCCD")
1606 return pipeBase.Struct(
1607 exposure=ccdExposure,
1608 ossThumb=ossThumb,
1609 flattenedThumb=flattenedThumb,
1611 preInterpolatedExposure=preInterpExp,
1612 outputExposure=ccdExposure,
1613 outputOssThumbnail=ossThumb,
1614 outputFlattenedThumbnail=flattenedThumb,
1615 )
1617 @pipeBase.timeMethod
1618 def runDataRef(self, sensorRef):
1619 """Perform instrument signature removal on a ButlerDataRef of a Sensor.
1621 This method contains the `CmdLineTask` interface to the ISR
1622 processing. All IO is handled here, freeing the `run()` method
1623 to manage only pixel-level calculations. The steps performed
1624 are:
1625 - Read in necessary detrending/isr/calibration data.
1626 - Process raw exposure in `run()`.
1627 - Persist the ISR-corrected exposure as "postISRCCD" if
1628 config.doWrite=True.
1630 Parameters
1631 ----------
1632 sensorRef : `daf.persistence.butlerSubset.ButlerDataRef`
1633 DataRef of the detector data to be processed
1635 Returns
1636 -------
1637 result : `lsst.pipe.base.Struct`
1638 Result struct with component:
1639 - ``exposure`` : `afw.image.Exposure`
1640 The fully ISR corrected exposure.
1642 Raises
1643 ------
1644 RuntimeError
1645 Raised if a configuration option is set to True, but the
1646 required calibration data does not exist.
1648 """
1649 self.log.info("Performing ISR on sensor %s.", sensorRef.dataId)
1651 ccdExposure = sensorRef.get(self.config.datasetType)
1653 camera = sensorRef.get("camera")
1654 isrData = self.readIsrData(sensorRef, ccdExposure)
1656 result = self.run(ccdExposure, camera=camera, **isrData.getDict())
1658 if self.config.doWrite:
1659 sensorRef.put(result.exposure, "postISRCCD")
1660 if result.preInterpolatedExposure is not None:
1661 sensorRef.put(result.preInterpolatedExposure, "postISRCCD_uninterpolated")
1662 if result.ossThumb is not None:
1663 isrQa.writeThumbnail(sensorRef, result.ossThumb, "ossThumb")
1664 if result.flattenedThumb is not None:
1665 isrQa.writeThumbnail(sensorRef, result.flattenedThumb, "flattenedThumb")
1667 return result
1669 def getIsrExposure(self, dataRef, datasetType, dateObs=None, immediate=True):
1670 """!Retrieve a calibration dataset for removing instrument signature.
1672 Parameters
1673 ----------
1675 dataRef : `daf.persistence.butlerSubset.ButlerDataRef`
1676 DataRef of the detector data to find calibration datasets
1677 for.
1678 datasetType : `str`
1679 Type of dataset to retrieve (e.g. 'bias', 'flat', etc).
1680 dateObs : `str`, optional
1681 Date of the observation. Used to correct butler failures
1682 when using fallback filters.
1683 immediate : `Bool`
1684 If True, disable butler proxies to enable error handling
1685 within this routine.
1687 Returns
1688 -------
1689 exposure : `lsst.afw.image.Exposure`
1690 Requested calibration frame.
1692 Raises
1693 ------
1694 RuntimeError
1695 Raised if no matching calibration frame can be found.
1696 """
1697 try:
1698 exp = dataRef.get(datasetType, immediate=immediate)
1699 except Exception as exc1:
1700 if not self.config.fallbackFilterName:
1701 raise RuntimeError("Unable to retrieve %s for %s: %s." % (datasetType, dataRef.dataId, exc1))
1702 try:
1703 if self.config.useFallbackDate and dateObs:
1704 exp = dataRef.get(datasetType, filter=self.config.fallbackFilterName,
1705 dateObs=dateObs, immediate=immediate)
1706 else:
1707 exp = dataRef.get(datasetType, filter=self.config.fallbackFilterName, immediate=immediate)
1708 except Exception as exc2:
1709 raise RuntimeError("Unable to retrieve %s for %s, even with fallback filter %s: %s AND %s." %
1710 (datasetType, dataRef.dataId, self.config.fallbackFilterName, exc1, exc2))
1711 self.log.warn("Using fallback calibration from filter %s.", self.config.fallbackFilterName)
1713 if self.config.doAssembleIsrExposures:
1714 exp = self.assembleCcd.assembleCcd(exp)
1715 return exp
1717 def ensureExposure(self, inputExp, camera, detectorNum):
1718 """Ensure that the data returned by Butler is a fully constructed exposure.
1720 ISR requires exposure-level image data for historical reasons, so if we did
1721 not recieve that from Butler, construct it from what we have, modifying the
1722 input in place.
1724 Parameters
1725 ----------
1726 inputExp : `lsst.afw.image.Exposure`, `lsst.afw.image.DecoratedImageU`, or
1727 `lsst.afw.image.ImageF`
1728 The input data structure obtained from Butler.
1729 camera : `lsst.afw.cameraGeom.camera`
1730 The camera associated with the image. Used to find the appropriate
1731 detector.
1732 detectorNum : `int`
1733 The detector this exposure should match.
1735 Returns
1736 -------
1737 inputExp : `lsst.afw.image.Exposure`
1738 The re-constructed exposure, with appropriate detector parameters.
1740 Raises
1741 ------
1742 TypeError
1743 Raised if the input data cannot be used to construct an exposure.
1744 """
1745 if isinstance(inputExp, afwImage.DecoratedImageU):
1746 inputExp = afwImage.makeExposure(afwImage.makeMaskedImage(inputExp))
1747 elif isinstance(inputExp, afwImage.ImageF):
1748 inputExp = afwImage.makeExposure(afwImage.makeMaskedImage(inputExp))
1749 elif isinstance(inputExp, afwImage.MaskedImageF):
1750 inputExp = afwImage.makeExposure(inputExp)
1751 elif isinstance(inputExp, afwImage.Exposure):
1752 pass
1753 elif inputExp is None:
1754 # Assume this will be caught by the setup if it is a problem.
1755 return inputExp
1756 else:
1757 raise TypeError("Input Exposure is not known type in isrTask.ensureExposure: %s." %
1758 (type(inputExp), ))
1760 if inputExp.getDetector() is None:
1761 inputExp.setDetector(camera[detectorNum])
1763 return inputExp
1765 def convertIntToFloat(self, exposure):
1766 """Convert exposure image from uint16 to float.
1768 If the exposure does not need to be converted, the input is
1769 immediately returned. For exposures that are converted to use
1770 floating point pixels, the variance is set to unity and the
1771 mask to zero.
1773 Parameters
1774 ----------
1775 exposure : `lsst.afw.image.Exposure`
1776 The raw exposure to be converted.
1778 Returns
1779 -------
1780 newexposure : `lsst.afw.image.Exposure`
1781 The input ``exposure``, converted to floating point pixels.
1783 Raises
1784 ------
1785 RuntimeError
1786 Raised if the exposure type cannot be converted to float.
1788 """
1789 if isinstance(exposure, afwImage.ExposureF):
1790 # Nothing to be done
1791 self.log.debug("Exposure already of type float.")
1792 return exposure
1793 if not hasattr(exposure, "convertF"):
1794 raise RuntimeError("Unable to convert exposure (%s) to float." % type(exposure))
1796 newexposure = exposure.convertF()
1797 newexposure.variance[:] = 1
1798 newexposure.mask[:] = 0x0
1800 return newexposure
1802 def maskAmplifier(self, ccdExposure, amp, defects):
1803 """Identify bad amplifiers, saturated and suspect pixels.
1805 Parameters
1806 ----------
1807 ccdExposure : `lsst.afw.image.Exposure`
1808 Input exposure to be masked.
1809 amp : `lsst.afw.table.AmpInfoCatalog`
1810 Catalog of parameters defining the amplifier on this
1811 exposure to mask.
1812 defects : `lsst.meas.algorithms.Defects`
1813 List of defects. Used to determine if the entire
1814 amplifier is bad.
1816 Returns
1817 -------
1818 badAmp : `Bool`
1819 If this is true, the entire amplifier area is covered by
1820 defects and unusable.
1822 """
1823 maskedImage = ccdExposure.getMaskedImage()
1825 badAmp = False
1827 # Check if entire amp region is defined as a defect (need to use amp.getBBox() for correct
1828 # comparison with current defects definition.
1829 if defects is not None:
1830 badAmp = bool(sum([v.getBBox().contains(amp.getBBox()) for v in defects]))
1832 # In the case of a bad amp, we will set mask to "BAD" (here use amp.getRawBBox() for correct
1833 # association with pixels in current ccdExposure).
1834 if badAmp:
1835 dataView = afwImage.MaskedImageF(maskedImage, amp.getRawBBox(),
1836 afwImage.PARENT)
1837 maskView = dataView.getMask()
1838 maskView |= maskView.getPlaneBitMask("BAD")
1839 del maskView
1840 return badAmp
1842 # Mask remaining defects after assembleCcd() to allow for defects that cross amplifier boundaries.
1843 # Saturation and suspect pixels can be masked now, though.
1844 limits = dict()
1845 if self.config.doSaturation and not badAmp:
1846 limits.update({self.config.saturatedMaskName: amp.getSaturation()})
1847 if self.config.doSuspect and not badAmp:
1848 limits.update({self.config.suspectMaskName: amp.getSuspectLevel()})
1849 if math.isfinite(self.config.saturation):
1850 limits.update({self.config.saturatedMaskName: self.config.saturation})
1852 for maskName, maskThreshold in limits.items():
1853 if not math.isnan(maskThreshold):
1854 dataView = maskedImage.Factory(maskedImage, amp.getRawBBox())
1855 isrFunctions.makeThresholdMask(
1856 maskedImage=dataView,
1857 threshold=maskThreshold,
1858 growFootprints=0,
1859 maskName=maskName
1860 )
1862 # Determine if we've fully masked this amplifier with SUSPECT and SAT pixels.
1863 maskView = afwImage.Mask(maskedImage.getMask(), amp.getRawDataBBox(),
1864 afwImage.PARENT)
1865 maskVal = maskView.getPlaneBitMask([self.config.saturatedMaskName,
1866 self.config.suspectMaskName])
1867 if numpy.all(maskView.getArray() & maskVal > 0):
1868 badAmp = True
1869 maskView |= maskView.getPlaneBitMask("BAD")
1871 return badAmp
1873 def overscanCorrection(self, ccdExposure, amp):
1874 """Apply overscan correction in place.
1876 This method does initial pixel rejection of the overscan
1877 region. The overscan can also be optionally segmented to
1878 allow for discontinuous overscan responses to be fit
1879 separately. The actual overscan subtraction is performed by
1880 the `lsst.ip.isr.isrFunctions.overscanCorrection` function,
1881 which is called here after the amplifier is preprocessed.
1883 Parameters
1884 ----------
1885 ccdExposure : `lsst.afw.image.Exposure`
1886 Exposure to have overscan correction performed.
1887 amp : `lsst.afw.table.AmpInfoCatalog`
1888 The amplifier to consider while correcting the overscan.
1890 Returns
1891 -------
1892 overscanResults : `lsst.pipe.base.Struct`
1893 Result struct with components:
1894 - ``imageFit`` : scalar or `lsst.afw.image.Image`
1895 Value or fit subtracted from the amplifier image data.
1896 - ``overscanFit`` : scalar or `lsst.afw.image.Image`
1897 Value or fit subtracted from the overscan image data.
1898 - ``overscanImage`` : `lsst.afw.image.Image`
1899 Image of the overscan region with the overscan
1900 correction applied. This quantity is used to estimate
1901 the amplifier read noise empirically.
1903 Raises
1904 ------
1905 RuntimeError
1906 Raised if the ``amp`` does not contain raw pixel information.
1908 See Also
1909 --------
1910 lsst.ip.isr.isrFunctions.overscanCorrection
1911 """
1912 if amp.getRawHorizontalOverscanBBox().isEmpty():
1913 self.log.info("ISR_OSCAN: No overscan region. Not performing overscan correction.")
1914 return None
1916 statControl = afwMath.StatisticsControl()
1917 statControl.setAndMask(ccdExposure.mask.getPlaneBitMask("SAT"))
1919 # Determine the bounding boxes
1920 dataBBox = amp.getRawDataBBox()
1921 oscanBBox = amp.getRawHorizontalOverscanBBox()
1922 dx0 = 0
1923 dx1 = 0
1925 prescanBBox = amp.getRawPrescanBBox()
1926 if (oscanBBox.getBeginX() > prescanBBox.getBeginX()): # amp is at the right
1927 dx0 += self.config.overscanNumLeadingColumnsToSkip
1928 dx1 -= self.config.overscanNumTrailingColumnsToSkip
1929 else:
1930 dx0 += self.config.overscanNumTrailingColumnsToSkip
1931 dx1 -= self.config.overscanNumLeadingColumnsToSkip
1933 # Determine if we need to work on subregions of the amplifier and overscan.
1934 imageBBoxes = []
1935 overscanBBoxes = []
1937 if ((self.config.overscanBiasJump
1938 and self.config.overscanBiasJumpLocation)
1939 and (ccdExposure.getMetadata().exists(self.config.overscanBiasJumpKeyword)
1940 and ccdExposure.getMetadata().getScalar(self.config.overscanBiasJumpKeyword) in
1941 self.config.overscanBiasJumpDevices)):
1942 if amp.getReadoutCorner() in (ReadoutCorner.LL, ReadoutCorner.LR):
1943 yLower = self.config.overscanBiasJumpLocation
1944 yUpper = dataBBox.getHeight() - yLower
1945 else:
1946 yUpper = self.config.overscanBiasJumpLocation
1947 yLower = dataBBox.getHeight() - yUpper
1949 imageBBoxes.append(lsst.geom.Box2I(dataBBox.getBegin(),
1950 lsst.geom.Extent2I(dataBBox.getWidth(), yLower)))
1951 overscanBBoxes.append(lsst.geom.Box2I(oscanBBox.getBegin() + lsst.geom.Extent2I(dx0, 0),
1952 lsst.geom.Extent2I(oscanBBox.getWidth() - dx0 + dx1,
1953 yLower)))
1955 imageBBoxes.append(lsst.geom.Box2I(dataBBox.getBegin() + lsst.geom.Extent2I(0, yLower),
1956 lsst.geom.Extent2I(dataBBox.getWidth(), yUpper)))
1957 overscanBBoxes.append(lsst.geom.Box2I(oscanBBox.getBegin() + lsst.geom.Extent2I(dx0, yLower),
1958 lsst.geom.Extent2I(oscanBBox.getWidth() - dx0 + dx1,
1959 yUpper)))
1960 else:
1961 imageBBoxes.append(lsst.geom.Box2I(dataBBox.getBegin(),
1962 lsst.geom.Extent2I(dataBBox.getWidth(), dataBBox.getHeight())))
1963 overscanBBoxes.append(lsst.geom.Box2I(oscanBBox.getBegin() + lsst.geom.Extent2I(dx0, 0),
1964 lsst.geom.Extent2I(oscanBBox.getWidth() - dx0 + dx1,
1965 oscanBBox.getHeight())))
1967 # Perform overscan correction on subregions, ensuring saturated pixels are masked.
1968 for imageBBox, overscanBBox in zip(imageBBoxes, overscanBBoxes):
1969 ampImage = ccdExposure.maskedImage[imageBBox]
1970 overscanImage = ccdExposure.maskedImage[overscanBBox]
1972 overscanArray = overscanImage.image.array
1973 median = numpy.ma.median(numpy.ma.masked_where(overscanImage.mask.array, overscanArray))
1974 bad = numpy.where(numpy.abs(overscanArray - median) > self.config.overscanMaxDev)
1975 overscanImage.mask.array[bad] = overscanImage.mask.getPlaneBitMask("SAT")
1977 statControl = afwMath.StatisticsControl()
1978 statControl.setAndMask(ccdExposure.mask.getPlaneBitMask("SAT"))
1980 overscanResults = self.overscan.run(ampImage.getImage(), overscanImage)
1982 # Measure average overscan levels and record them in the metadata.
1983 levelStat = afwMath.MEDIAN
1984 sigmaStat = afwMath.STDEVCLIP
1986 sctrl = afwMath.StatisticsControl(self.config.qa.flatness.clipSigma,
1987 self.config.qa.flatness.nIter)
1988 metadata = ccdExposure.getMetadata()
1989 ampNum = amp.getName()
1990 # if self.config.overscanFitType in ("MEDIAN", "MEAN", "MEANCLIP"):
1991 if isinstance(overscanResults.overscanFit, float):
1992 metadata.set("ISR_OSCAN_LEVEL%s" % ampNum, overscanResults.overscanFit)
1993 metadata.set("ISR_OSCAN_SIGMA%s" % ampNum, 0.0)
1994 else:
1995 stats = afwMath.makeStatistics(overscanResults.overscanFit, levelStat | sigmaStat, sctrl)
1996 metadata.set("ISR_OSCAN_LEVEL%s" % ampNum, stats.getValue(levelStat))
1997 metadata.set("ISR_OSCAN_SIGMA%s" % ampNum, stats.getValue(sigmaStat))
1999 return overscanResults
2001 def updateVariance(self, ampExposure, amp, overscanImage=None):
2002 """Set the variance plane using the amplifier gain and read noise
2004 The read noise is calculated from the ``overscanImage`` if the
2005 ``doEmpiricalReadNoise`` option is set in the configuration; otherwise
2006 the value from the amplifier data is used.
2008 Parameters
2009 ----------
2010 ampExposure : `lsst.afw.image.Exposure`
2011 Exposure to process.
2012 amp : `lsst.afw.table.AmpInfoRecord` or `FakeAmp`
2013 Amplifier detector data.
2014 overscanImage : `lsst.afw.image.MaskedImage`, optional.
2015 Image of overscan, required only for empirical read noise.
2017 See also
2018 --------
2019 lsst.ip.isr.isrFunctions.updateVariance
2020 """
2021 maskPlanes = [self.config.saturatedMaskName, self.config.suspectMaskName]
2022 gain = amp.getGain()
2024 if math.isnan(gain):
2025 gain = 1.0
2026 self.log.warn("Gain set to NAN! Updating to 1.0 to generate Poisson variance.")
2027 elif gain <= 0:
2028 patchedGain = 1.0
2029 self.log.warn("Gain for amp %s == %g <= 0; setting to %f.",
2030 amp.getName(), gain, patchedGain)
2031 gain = patchedGain
2033 if self.config.doEmpiricalReadNoise and overscanImage is None:
2034 self.log.info("Overscan is none for EmpiricalReadNoise.")
2036 if self.config.doEmpiricalReadNoise and overscanImage is not None:
2037 stats = afwMath.StatisticsControl()
2038 stats.setAndMask(overscanImage.mask.getPlaneBitMask(maskPlanes))
2039 readNoise = afwMath.makeStatistics(overscanImage, afwMath.STDEVCLIP, stats).getValue()
2040 self.log.info("Calculated empirical read noise for amp %s: %f.",
2041 amp.getName(), readNoise)
2042 else:
2043 readNoise = amp.getReadNoise()
2045 isrFunctions.updateVariance(
2046 maskedImage=ampExposure.getMaskedImage(),
2047 gain=gain,
2048 readNoise=readNoise,
2049 )
2051 def darkCorrection(self, exposure, darkExposure, invert=False):
2052 """!Apply dark correction in place.
2054 Parameters
2055 ----------
2056 exposure : `lsst.afw.image.Exposure`
2057 Exposure to process.
2058 darkExposure : `lsst.afw.image.Exposure`
2059 Dark exposure of the same size as ``exposure``.
2060 invert : `Bool`, optional
2061 If True, re-add the dark to an already corrected image.
2063 Raises
2064 ------
2065 RuntimeError
2066 Raised if either ``exposure`` or ``darkExposure`` do not
2067 have their dark time defined.
2069 See Also
2070 --------
2071 lsst.ip.isr.isrFunctions.darkCorrection
2072 """
2073 expScale = exposure.getInfo().getVisitInfo().getDarkTime()
2074 if math.isnan(expScale):
2075 raise RuntimeError("Exposure darktime is NAN.")
2076 if darkExposure.getInfo().getVisitInfo() is not None \
2077 and not math.isnan(darkExposure.getInfo().getVisitInfo().getDarkTime()):
2078 darkScale = darkExposure.getInfo().getVisitInfo().getDarkTime()
2079 else:
2080 # DM-17444: darkExposure.getInfo.getVisitInfo() is None
2081 # so getDarkTime() does not exist.
2082 self.log.warn("darkExposure.getInfo().getVisitInfo() does not exist. Using darkScale = 1.0.")
2083 darkScale = 1.0
2085 isrFunctions.darkCorrection(
2086 maskedImage=exposure.getMaskedImage(),
2087 darkMaskedImage=darkExposure.getMaskedImage(),
2088 expScale=expScale,
2089 darkScale=darkScale,
2090 invert=invert,
2091 trimToFit=self.config.doTrimToMatchCalib
2092 )
2094 def doLinearize(self, detector):
2095 """!Check if linearization is needed for the detector cameraGeom.
2097 Checks config.doLinearize and the linearity type of the first
2098 amplifier.
2100 Parameters
2101 ----------
2102 detector : `lsst.afw.cameraGeom.Detector`
2103 Detector to get linearity type from.
2105 Returns
2106 -------
2107 doLinearize : `Bool`
2108 If True, linearization should be performed.
2109 """
2110 return self.config.doLinearize and \
2111 detector.getAmplifiers()[0].getLinearityType() != NullLinearityType
2113 def flatCorrection(self, exposure, flatExposure, invert=False):
2114 """!Apply flat correction in place.
2116 Parameters
2117 ----------
2118 exposure : `lsst.afw.image.Exposure`
2119 Exposure to process.
2120 flatExposure : `lsst.afw.image.Exposure`
2121 Flat exposure of the same size as ``exposure``.
2122 invert : `Bool`, optional
2123 If True, unflatten an already flattened image.
2125 See Also
2126 --------
2127 lsst.ip.isr.isrFunctions.flatCorrection
2128 """
2129 isrFunctions.flatCorrection(
2130 maskedImage=exposure.getMaskedImage(),
2131 flatMaskedImage=flatExposure.getMaskedImage(),
2132 scalingType=self.config.flatScalingType,
2133 userScale=self.config.flatUserScale,
2134 invert=invert,
2135 trimToFit=self.config.doTrimToMatchCalib
2136 )
2138 def saturationDetection(self, exposure, amp):
2139 """!Detect saturated pixels and mask them using mask plane config.saturatedMaskName, in place.
2141 Parameters
2142 ----------
2143 exposure : `lsst.afw.image.Exposure`
2144 Exposure to process. Only the amplifier DataSec is processed.
2145 amp : `lsst.afw.table.AmpInfoCatalog`
2146 Amplifier detector data.
2148 See Also
2149 --------
2150 lsst.ip.isr.isrFunctions.makeThresholdMask
2151 """
2152 if not math.isnan(amp.getSaturation()):
2153 maskedImage = exposure.getMaskedImage()
2154 dataView = maskedImage.Factory(maskedImage, amp.getRawBBox())
2155 isrFunctions.makeThresholdMask(
2156 maskedImage=dataView,
2157 threshold=amp.getSaturation(),
2158 growFootprints=0,
2159 maskName=self.config.saturatedMaskName,
2160 )
2162 def saturationInterpolation(self, exposure):
2163 """!Interpolate over saturated pixels, in place.
2165 This method should be called after `saturationDetection`, to
2166 ensure that the saturated pixels have been identified in the
2167 SAT mask. It should also be called after `assembleCcd`, since
2168 saturated regions may cross amplifier boundaries.
2170 Parameters
2171 ----------
2172 exposure : `lsst.afw.image.Exposure`
2173 Exposure to process.
2175 See Also
2176 --------
2177 lsst.ip.isr.isrTask.saturationDetection
2178 lsst.ip.isr.isrFunctions.interpolateFromMask
2179 """
2180 isrFunctions.interpolateFromMask(
2181 maskedImage=exposure.getMaskedImage(),
2182 fwhm=self.config.fwhm,
2183 growSaturatedFootprints=self.config.growSaturationFootprintSize,
2184 maskNameList=list(self.config.saturatedMaskName),
2185 )
2187 def suspectDetection(self, exposure, amp):
2188 """!Detect suspect pixels and mask them using mask plane config.suspectMaskName, in place.
2190 Parameters
2191 ----------
2192 exposure : `lsst.afw.image.Exposure`
2193 Exposure to process. Only the amplifier DataSec is processed.
2194 amp : `lsst.afw.table.AmpInfoCatalog`
2195 Amplifier detector data.
2197 See Also
2198 --------
2199 lsst.ip.isr.isrFunctions.makeThresholdMask
2201 Notes
2202 -----
2203 Suspect pixels are pixels whose value is greater than amp.getSuspectLevel().
2204 This is intended to indicate pixels that may be affected by unknown systematics;
2205 for example if non-linearity corrections above a certain level are unstable
2206 then that would be a useful value for suspectLevel. A value of `nan` indicates
2207 that no such level exists and no pixels are to be masked as suspicious.
2208 """
2209 suspectLevel = amp.getSuspectLevel()
2210 if math.isnan(suspectLevel):
2211 return
2213 maskedImage = exposure.getMaskedImage()
2214 dataView = maskedImage.Factory(maskedImage, amp.getRawBBox())
2215 isrFunctions.makeThresholdMask(
2216 maskedImage=dataView,
2217 threshold=suspectLevel,
2218 growFootprints=0,
2219 maskName=self.config.suspectMaskName,
2220 )
2222 def maskDefect(self, exposure, defectBaseList):
2223 """!Mask defects using mask plane "BAD", in place.
2225 Parameters
2226 ----------
2227 exposure : `lsst.afw.image.Exposure`
2228 Exposure to process.
2229 defectBaseList : `lsst.meas.algorithms.Defects` or `list` of
2230 `lsst.afw.image.DefectBase`.
2231 List of defects to mask.
2233 Notes
2234 -----
2235 Call this after CCD assembly, since defects may cross amplifier boundaries.
2236 """
2237 maskedImage = exposure.getMaskedImage()
2238 if not isinstance(defectBaseList, Defects):
2239 # Promotes DefectBase to Defect
2240 defectList = Defects(defectBaseList)
2241 else:
2242 defectList = defectBaseList
2243 defectList.maskPixels(maskedImage, maskName="BAD")
2245 def maskEdges(self, exposure, numEdgePixels=0, maskPlane="SUSPECT", level='DETECTOR'):
2246 """!Mask edge pixels with applicable mask plane.
2248 Parameters
2249 ----------
2250 exposure : `lsst.afw.image.Exposure`
2251 Exposure to process.
2252 numEdgePixels : `int`, optional
2253 Number of edge pixels to mask.
2254 maskPlane : `str`, optional
2255 Mask plane name to use.
2256 level : `str`, optional
2257 Level at which to mask edges.
2258 """
2259 maskedImage = exposure.getMaskedImage()
2260 maskBitMask = maskedImage.getMask().getPlaneBitMask(maskPlane)
2262 if numEdgePixels > 0:
2263 if level == 'DETECTOR':
2264 boxes = [maskedImage.getBBox()]
2265 elif level == 'AMP':
2266 boxes = [amp.getBBox() for amp in exposure.getDetector()]
2268 for box in boxes:
2269 # This makes a bbox numEdgeSuspect pixels smaller than the image on each side
2270 subImage = maskedImage[box]
2271 box.grow(-numEdgePixels)
2272 # Mask pixels outside box
2273 SourceDetectionTask.setEdgeBits(
2274 subImage,
2275 box,
2276 maskBitMask)
2278 def maskAndInterpolateDefects(self, exposure, defectBaseList):
2279 """Mask and interpolate defects using mask plane "BAD", in place.
2281 Parameters
2282 ----------
2283 exposure : `lsst.afw.image.Exposure`
2284 Exposure to process.
2285 defectBaseList : `lsst.meas.algorithms.Defects` or `list` of
2286 `lsst.afw.image.DefectBase`.
2287 List of defects to mask and interpolate.
2289 See Also
2290 --------
2291 lsst.ip.isr.isrTask.maskDefect()
2292 """
2293 self.maskDefect(exposure, defectBaseList)
2294 self.maskEdges(exposure, numEdgePixels=self.config.numEdgeSuspect,
2295 maskPlane="SUSPECT", level=self.config.edgeMaskLevel)
2296 isrFunctions.interpolateFromMask(
2297 maskedImage=exposure.getMaskedImage(),
2298 fwhm=self.config.fwhm,
2299 growSaturatedFootprints=0,
2300 maskNameList=["BAD"],
2301 )
2303 def maskNan(self, exposure):
2304 """Mask NaNs using mask plane "UNMASKEDNAN", in place.
2306 Parameters
2307 ----------
2308 exposure : `lsst.afw.image.Exposure`
2309 Exposure to process.
2311 Notes
2312 -----
2313 We mask over all NaNs, including those that are masked with
2314 other bits (because those may or may not be interpolated over
2315 later, and we want to remove all NaNs). Despite this
2316 behaviour, the "UNMASKEDNAN" mask plane is used to preserve
2317 the historical name.
2318 """
2319 maskedImage = exposure.getMaskedImage()
2321 # Find and mask NaNs
2322 maskedImage.getMask().addMaskPlane("UNMASKEDNAN")
2323 maskVal = maskedImage.getMask().getPlaneBitMask("UNMASKEDNAN")
2324 numNans = maskNans(maskedImage, maskVal)
2325 self.metadata.set("NUMNANS", numNans)
2326 if numNans > 0:
2327 self.log.warn("There were %d unmasked NaNs.", numNans)
2329 def maskAndInterpolateNan(self, exposure):
2330 """"Mask and interpolate NaNs using mask plane "UNMASKEDNAN", in place.
2332 Parameters
2333 ----------
2334 exposure : `lsst.afw.image.Exposure`
2335 Exposure to process.
2337 See Also
2338 --------
2339 lsst.ip.isr.isrTask.maskNan()
2340 """
2341 self.maskNan(exposure)
2342 isrFunctions.interpolateFromMask(
2343 maskedImage=exposure.getMaskedImage(),
2344 fwhm=self.config.fwhm,
2345 growSaturatedFootprints=0,
2346 maskNameList=["UNMASKEDNAN"],
2347 )
2349 def measureBackground(self, exposure, IsrQaConfig=None):
2350 """Measure the image background in subgrids, for quality control purposes.
2352 Parameters
2353 ----------
2354 exposure : `lsst.afw.image.Exposure`
2355 Exposure to process.
2356 IsrQaConfig : `lsst.ip.isr.isrQa.IsrQaConfig`
2357 Configuration object containing parameters on which background
2358 statistics and subgrids to use.
2359 """
2360 if IsrQaConfig is not None:
2361 statsControl = afwMath.StatisticsControl(IsrQaConfig.flatness.clipSigma,
2362 IsrQaConfig.flatness.nIter)
2363 maskVal = exposure.getMaskedImage().getMask().getPlaneBitMask(["BAD", "SAT", "DETECTED"])
2364 statsControl.setAndMask(maskVal)
2365 maskedImage = exposure.getMaskedImage()
2366 stats = afwMath.makeStatistics(maskedImage, afwMath.MEDIAN | afwMath.STDEVCLIP, statsControl)
2367 skyLevel = stats.getValue(afwMath.MEDIAN)
2368 skySigma = stats.getValue(afwMath.STDEVCLIP)
2369 self.log.info("Flattened sky level: %f +/- %f.", skyLevel, skySigma)
2370 metadata = exposure.getMetadata()
2371 metadata.set('SKYLEVEL', skyLevel)
2372 metadata.set('SKYSIGMA', skySigma)
2374 # calcluating flatlevel over the subgrids
2375 stat = afwMath.MEANCLIP if IsrQaConfig.flatness.doClip else afwMath.MEAN
2376 meshXHalf = int(IsrQaConfig.flatness.meshX/2.)
2377 meshYHalf = int(IsrQaConfig.flatness.meshY/2.)
2378 nX = int((exposure.getWidth() + meshXHalf) / IsrQaConfig.flatness.meshX)
2379 nY = int((exposure.getHeight() + meshYHalf) / IsrQaConfig.flatness.meshY)
2380 skyLevels = numpy.zeros((nX, nY))
2382 for j in range(nY):
2383 yc = meshYHalf + j * IsrQaConfig.flatness.meshY
2384 for i in range(nX):
2385 xc = meshXHalf + i * IsrQaConfig.flatness.meshX
2387 xLLC = xc - meshXHalf
2388 yLLC = yc - meshYHalf
2389 xURC = xc + meshXHalf - 1
2390 yURC = yc + meshYHalf - 1
2392 bbox = lsst.geom.Box2I(lsst.geom.Point2I(xLLC, yLLC), lsst.geom.Point2I(xURC, yURC))
2393 miMesh = maskedImage.Factory(exposure.getMaskedImage(), bbox, afwImage.LOCAL)
2395 skyLevels[i, j] = afwMath.makeStatistics(miMesh, stat, statsControl).getValue()
2397 good = numpy.where(numpy.isfinite(skyLevels))
2398 skyMedian = numpy.median(skyLevels[good])
2399 flatness = (skyLevels[good] - skyMedian) / skyMedian
2400 flatness_rms = numpy.std(flatness)
2401 flatness_pp = flatness.max() - flatness.min() if len(flatness) > 0 else numpy.nan
2403 self.log.info("Measuring sky levels in %dx%d grids: %f.", nX, nY, skyMedian)
2404 self.log.info("Sky flatness in %dx%d grids - pp: %f rms: %f.",
2405 nX, nY, flatness_pp, flatness_rms)
2407 metadata.set('FLATNESS_PP', float(flatness_pp))
2408 metadata.set('FLATNESS_RMS', float(flatness_rms))
2409 metadata.set('FLATNESS_NGRIDS', '%dx%d' % (nX, nY))
2410 metadata.set('FLATNESS_MESHX', IsrQaConfig.flatness.meshX)
2411 metadata.set('FLATNESS_MESHY', IsrQaConfig.flatness.meshY)
2413 def roughZeroPoint(self, exposure):
2414 """Set an approximate magnitude zero point for the exposure.
2416 Parameters
2417 ----------
2418 exposure : `lsst.afw.image.Exposure`
2419 Exposure to process.
2420 """
2421 filterName = afwImage.Filter(exposure.getFilter().getId()).getName() # Canonical name for filter
2422 if filterName in self.config.fluxMag0T1:
2423 fluxMag0 = self.config.fluxMag0T1[filterName]
2424 else:
2425 self.log.warn("No rough magnitude zero point set for filter %s.", filterName)
2426 fluxMag0 = self.config.defaultFluxMag0T1
2428 expTime = exposure.getInfo().getVisitInfo().getExposureTime()
2429 if not expTime > 0: # handle NaN as well as <= 0
2430 self.log.warn("Non-positive exposure time; skipping rough zero point.")
2431 return
2433 self.log.info("Setting rough magnitude zero point: %f", 2.5*math.log10(fluxMag0*expTime))
2434 exposure.setPhotoCalib(afwImage.makePhotoCalibFromCalibZeroPoint(fluxMag0*expTime, 0.0))
2436 def setValidPolygonIntersect(self, ccdExposure, fpPolygon):
2437 """!Set the valid polygon as the intersection of fpPolygon and the ccd corners.
2439 Parameters
2440 ----------
2441 ccdExposure : `lsst.afw.image.Exposure`
2442 Exposure to process.
2443 fpPolygon : `lsst.afw.geom.Polygon`
2444 Polygon in focal plane coordinates.
2445 """
2446 # Get ccd corners in focal plane coordinates
2447 ccd = ccdExposure.getDetector()
2448 fpCorners = ccd.getCorners(FOCAL_PLANE)
2449 ccdPolygon = Polygon(fpCorners)
2451 # Get intersection of ccd corners with fpPolygon
2452 intersect = ccdPolygon.intersectionSingle(fpPolygon)
2454 # Transform back to pixel positions and build new polygon
2455 ccdPoints = ccd.transform(intersect, FOCAL_PLANE, PIXELS)
2456 validPolygon = Polygon(ccdPoints)
2457 ccdExposure.getInfo().setValidPolygon(validPolygon)
2459 @contextmanager
2460 def flatContext(self, exp, flat, dark=None):
2461 """Context manager that applies and removes flats and darks,
2462 if the task is configured to apply them.
2464 Parameters
2465 ----------
2466 exp : `lsst.afw.image.Exposure`
2467 Exposure to process.
2468 flat : `lsst.afw.image.Exposure`
2469 Flat exposure the same size as ``exp``.
2470 dark : `lsst.afw.image.Exposure`, optional
2471 Dark exposure the same size as ``exp``.
2473 Yields
2474 ------
2475 exp : `lsst.afw.image.Exposure`
2476 The flat and dark corrected exposure.
2477 """
2478 if self.config.doDark and dark is not None:
2479 self.darkCorrection(exp, dark)
2480 if self.config.doFlat:
2481 self.flatCorrection(exp, flat)
2482 try:
2483 yield exp
2484 finally:
2485 if self.config.doFlat:
2486 self.flatCorrection(exp, flat, invert=True)
2487 if self.config.doDark and dark is not None:
2488 self.darkCorrection(exp, dark, invert=True)
2490 def debugView(self, exposure, stepname):
2491 """Utility function to examine ISR exposure at different stages.
2493 Parameters
2494 ----------
2495 exposure : `lsst.afw.image.Exposure`
2496 Exposure to view.
2497 stepname : `str`
2498 State of processing to view.
2499 """
2500 frame = getDebugFrame(self._display, stepname)
2501 if frame:
2502 display = getDisplay(frame)
2503 display.scale('asinh', 'zscale')
2504 display.mtv(exposure)
2505 prompt = "Press Enter to continue [c]... "
2506 while True:
2507 ans = input(prompt).lower()
2508 if ans in ("", "c",):
2509 break
2512class FakeAmp(object):
2513 """A Detector-like object that supports returning gain and saturation level
2515 This is used when the input exposure does not have a detector.
2517 Parameters
2518 ----------
2519 exposure : `lsst.afw.image.Exposure`
2520 Exposure to generate a fake amplifier for.
2521 config : `lsst.ip.isr.isrTaskConfig`
2522 Configuration to apply to the fake amplifier.
2523 """
2525 def __init__(self, exposure, config):
2526 self._bbox = exposure.getBBox(afwImage.LOCAL)
2527 self._RawHorizontalOverscanBBox = lsst.geom.Box2I()
2528 self._gain = config.gain
2529 self._readNoise = config.readNoise
2530 self._saturation = config.saturation
2532 def getBBox(self):
2533 return self._bbox
2535 def getRawBBox(self):
2536 return self._bbox
2538 def getRawHorizontalOverscanBBox(self):
2539 return self._RawHorizontalOverscanBBox
2541 def getGain(self):
2542 return self._gain
2544 def getReadNoise(self):
2545 return self._readNoise
2547 def getSaturation(self):
2548 return self._saturation
2550 def getSuspectLevel(self):
2551 return float("NaN")
2554class RunIsrConfig(pexConfig.Config):
2555 isr = pexConfig.ConfigurableField(target=IsrTask, doc="Instrument signature removal")
2558class RunIsrTask(pipeBase.CmdLineTask):
2559 """Task to wrap the default IsrTask to allow it to be retargeted.
2561 The standard IsrTask can be called directly from a command line
2562 program, but doing so removes the ability of the task to be
2563 retargeted. As most cameras override some set of the IsrTask
2564 methods, this would remove those data-specific methods in the
2565 output post-ISR images. This wrapping class fixes the issue,
2566 allowing identical post-ISR images to be generated by both the
2567 processCcd and isrTask code.
2568 """
2569 ConfigClass = RunIsrConfig
2570 _DefaultName = "runIsr"
2572 def __init__(self, *args, **kwargs):
2573 super().__init__(*args, **kwargs)
2574 self.makeSubtask("isr")
2576 def runDataRef(self, dataRef):
2577 """
2578 Parameters
2579 ----------
2580 dataRef : `lsst.daf.persistence.ButlerDataRef`
2581 data reference of the detector data to be processed
2583 Returns
2584 -------
2585 result : `pipeBase.Struct`
2586 Result struct with component:
2588 - exposure : `lsst.afw.image.Exposure`
2589 Post-ISR processed exposure.
2590 """
2591 return self.isr.runDataRef(dataRef)