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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
50from .fringe import FringeTask
51from .isr import maskNans
52from .masking import MaskingTask
53from .straylight import StrayLightTask
54from .vignette import VignetteTask
57__all__ = ["IsrTask", "IsrTaskConfig", "RunIsrTask", "RunIsrConfig"]
60class IsrTaskConnections(pipeBase.PipelineTaskConnections,
61 dimensions={"instrument", "exposure", "detector"},
62 defaultTemplates={}):
63 ccdExposure = cT.Input(
64 name="raw",
65 doc="Input exposure to process.",
66 storageClass="Exposure",
67 dimensions=["instrument", "detector", "exposure"],
68 )
69 camera = cT.PrerequisiteInput(
70 name="camera",
71 storageClass="Camera",
72 doc="Input camera to construct complete exposures.",
73 dimensions=["instrument", "calibration_label"],
74 )
75 bias = cT.PrerequisiteInput(
76 name="bias",
77 doc="Input bias calibration.",
78 storageClass="ExposureF",
79 dimensions=["instrument", "calibration_label", "detector"],
80 )
81 dark = cT.PrerequisiteInput(
82 name='dark',
83 doc="Input dark calibration.",
84 storageClass="ExposureF",
85 dimensions=["instrument", "calibration_label", "detector"],
86 )
87 flat = cT.PrerequisiteInput(
88 name="flat",
89 doc="Input flat calibration.",
90 storageClass="ExposureF",
91 dimensions=["instrument", "physical_filter", "calibration_label", "detector"],
92 )
93 fringes = cT.PrerequisiteInput(
94 name="fringe",
95 doc="Input fringe calibration.",
96 storageClass="ExposureF",
97 dimensions=["instrument", "physical_filter", "calibration_label", "detector"],
98 )
99 strayLightData = cT.PrerequisiteInput(
100 name='yBackground',
101 doc="Input stray light calibration.",
102 storageClass="StrayLightData",
103 dimensions=["instrument", "physical_filter", "calibration_label", "detector"],
104 )
105 bfKernel = cT.PrerequisiteInput(
106 name='bfKernel',
107 doc="Input brighter-fatter kernel.",
108 storageClass="NumpyArray",
109 dimensions=["instrument", "calibration_label"],
110 )
111 newBFKernel = cT.PrerequisiteInput(
112 name='brighterFatterKernel',
113 doc="Newer complete kernel + gain solutions.",
114 storageClass="BrighterFatterKernel",
115 dimensions=["instrument", "calibration_label", "detector"],
116 )
117 defects = cT.PrerequisiteInput(
118 name='defects',
119 doc="Input defect tables.",
120 storageClass="Defects",
121 dimensions=["instrument", "calibration_label", "detector"],
122 )
123 opticsTransmission = cT.PrerequisiteInput(
124 name="transmission_optics",
125 storageClass="TransmissionCurve",
126 doc="Transmission curve due to the optics.",
127 dimensions=["instrument", "calibration_label"],
128 )
129 filterTransmission = cT.PrerequisiteInput(
130 name="transmission_filter",
131 storageClass="TransmissionCurve",
132 doc="Transmission curve due to the filter.",
133 dimensions=["instrument", "physical_filter", "calibration_label"],
134 )
135 sensorTransmission = cT.PrerequisiteInput(
136 name="transmission_sensor",
137 storageClass="TransmissionCurve",
138 doc="Transmission curve due to the sensor.",
139 dimensions=["instrument", "calibration_label", "detector"],
140 )
141 atmosphereTransmission = cT.PrerequisiteInput(
142 name="transmission_atmosphere",
143 storageClass="TransmissionCurve",
144 doc="Transmission curve due to the atmosphere.",
145 dimensions=["instrument"],
146 )
147 illumMaskedImage = cT.PrerequisiteInput(
148 name="illum",
149 doc="Input illumination correction.",
150 storageClass="MaskedImageF",
151 dimensions=["instrument", "physical_filter", "calibration_label", "detector"],
152 )
154 outputExposure = cT.Output(
155 name='postISRCCD',
156 doc="Output ISR processed exposure.",
157 storageClass="ExposureF",
158 dimensions=["instrument", "exposure", "detector"],
159 )
160 preInterpExposure = cT.Output(
161 name='preInterpISRCCD',
162 doc="Output ISR processed exposure, with pixels left uninterpolated.",
163 storageClass="ExposureF",
164 dimensions=["instrument", "exposure", "detector"],
165 )
166 outputOssThumbnail = cT.Output(
167 name="OssThumb",
168 doc="Output Overscan-subtracted thumbnail image.",
169 storageClass="Thumbnail",
170 dimensions=["instrument", "exposure", "detector"],
171 )
172 outputFlattenedThumbnail = cT.Output(
173 name="FlattenedThumb",
174 doc="Output flat-corrected thumbnail image.",
175 storageClass="Thumbnail",
176 dimensions=["instrument", "exposure", "detector"],
177 )
179 def __init__(self, *, config=None):
180 super().__init__(config=config)
182 if config.doBias is not True:
183 self.prerequisiteInputs.discard("bias")
184 if config.doLinearize is not True:
185 self.prerequisiteInputs.discard("linearizer")
186 if config.doCrosstalk is not True:
187 self.prerequisiteInputs.discard("crosstalkSources")
188 if config.doBrighterFatter is not True:
189 self.prerequisiteInputs.discard("bfKernel")
190 self.prerequisiteInputs.discard("newBFKernel")
191 if config.doDefect is not True:
192 self.prerequisiteInputs.discard("defects")
193 if config.doDark is not True:
194 self.prerequisiteInputs.discard("dark")
195 if config.doFlat is not True:
196 self.prerequisiteInputs.discard("flat")
197 if config.doAttachTransmissionCurve is not True:
198 self.prerequisiteInputs.discard("opticsTransmission")
199 self.prerequisiteInputs.discard("filterTransmission")
200 self.prerequisiteInputs.discard("sensorTransmission")
201 self.prerequisiteInputs.discard("atmosphereTransmission")
202 if config.doUseOpticsTransmission is not True:
203 self.prerequisiteInputs.discard("opticsTransmission")
204 if config.doUseFilterTransmission is not True:
205 self.prerequisiteInputs.discard("filterTransmission")
206 if config.doUseSensorTransmission is not True:
207 self.prerequisiteInputs.discard("sensorTransmission")
208 if config.doUseAtmosphereTransmission is not True:
209 self.prerequisiteInputs.discard("atmosphereTransmission")
210 if config.doIlluminationCorrection is not True:
211 self.prerequisiteInputs.discard("illumMaskedImage")
213 if config.doWrite is not True:
214 self.outputs.discard("outputExposure")
215 self.outputs.discard("preInterpExposure")
216 self.outputs.discard("outputFlattenedThumbnail")
217 self.outputs.discard("outputOssThumbnail")
218 if config.doSaveInterpPixels is not True:
219 self.outputs.discard("preInterpExposure")
220 if config.qa.doThumbnailOss is not True:
221 self.outputs.discard("outputOssThumbnail")
222 if config.qa.doThumbnailFlattened is not True:
223 self.outputs.discard("outputFlattenedThumbnail")
226class IsrTaskConfig(pipeBase.PipelineTaskConfig,
227 pipelineConnections=IsrTaskConnections):
228 """Configuration parameters for IsrTask.
230 Items are grouped in the order in which they are executed by the task.
231 """
232 datasetType = pexConfig.Field(
233 dtype=str,
234 doc="Dataset type for input data; users will typically leave this alone, "
235 "but camera-specific ISR tasks will override it",
236 default="raw",
237 )
239 fallbackFilterName = pexConfig.Field(
240 dtype=str,
241 doc="Fallback default filter name for calibrations.",
242 optional=True
243 )
244 useFallbackDate = pexConfig.Field(
245 dtype=bool,
246 doc="Pass observation date when using fallback filter.",
247 default=False,
248 )
249 expectWcs = pexConfig.Field(
250 dtype=bool,
251 default=True,
252 doc="Expect input science images to have a WCS (set False for e.g. spectrographs)."
253 )
254 fwhm = pexConfig.Field(
255 dtype=float,
256 doc="FWHM of PSF in arcseconds.",
257 default=1.0,
258 )
259 qa = pexConfig.ConfigField(
260 dtype=isrQa.IsrQaConfig,
261 doc="QA related configuration options.",
262 )
264 # Image conversion configuration
265 doConvertIntToFloat = pexConfig.Field(
266 dtype=bool,
267 doc="Convert integer raw images to floating point values?",
268 default=True,
269 )
271 # Saturated pixel handling.
272 doSaturation = pexConfig.Field(
273 dtype=bool,
274 doc="Mask saturated pixels? NB: this is totally independent of the"
275 " interpolation option - this is ONLY setting the bits in the mask."
276 " To have them interpolated make sure doSaturationInterpolation=True",
277 default=True,
278 )
279 saturatedMaskName = pexConfig.Field(
280 dtype=str,
281 doc="Name of mask plane to use in saturation detection and interpolation",
282 default="SAT",
283 )
284 saturation = pexConfig.Field(
285 dtype=float,
286 doc="The saturation level to use if no Detector is present in the Exposure (ignored if NaN)",
287 default=float("NaN"),
288 )
289 growSaturationFootprintSize = pexConfig.Field(
290 dtype=int,
291 doc="Number of pixels by which to grow the saturation footprints",
292 default=1,
293 )
295 # Suspect pixel handling.
296 doSuspect = pexConfig.Field(
297 dtype=bool,
298 doc="Mask suspect pixels?",
299 default=False,
300 )
301 suspectMaskName = pexConfig.Field(
302 dtype=str,
303 doc="Name of mask plane to use for suspect pixels",
304 default="SUSPECT",
305 )
306 numEdgeSuspect = pexConfig.Field(
307 dtype=int,
308 doc="Number of edge pixels to be flagged as untrustworthy.",
309 default=0,
310 )
312 # Initial masking options.
313 doSetBadRegions = pexConfig.Field(
314 dtype=bool,
315 doc="Should we set the level of all BAD patches of the chip to the chip's average value?",
316 default=True,
317 )
318 badStatistic = pexConfig.ChoiceField(
319 dtype=str,
320 doc="How to estimate the average value for BAD regions.",
321 default='MEANCLIP',
322 allowed={
323 "MEANCLIP": "Correct using the (clipped) mean of good data",
324 "MEDIAN": "Correct using the median of the good data",
325 },
326 )
328 # Overscan subtraction configuration.
329 doOverscan = pexConfig.Field(
330 dtype=bool,
331 doc="Do overscan subtraction?",
332 default=True,
333 )
334 overscanFitType = pexConfig.ChoiceField(
335 dtype=str,
336 doc="The method for fitting the overscan bias level.",
337 default='MEDIAN',
338 allowed={
339 "POLY": "Fit ordinary polynomial to the longest axis of the overscan region",
340 "CHEB": "Fit Chebyshev polynomial to the longest axis of the overscan region",
341 "LEG": "Fit Legendre polynomial to the longest axis of the overscan region",
342 "NATURAL_SPLINE": "Fit natural spline to the longest axis of the overscan region",
343 "CUBIC_SPLINE": "Fit cubic spline to the longest axis of the overscan region",
344 "AKIMA_SPLINE": "Fit Akima spline to the longest axis of the overscan region",
345 "MEAN": "Correct using the mean of the overscan region",
346 "MEANCLIP": "Correct using a clipped mean of the overscan region",
347 "MEDIAN": "Correct using the median of the overscan region",
348 "MEDIAN_PER_ROW": "Correct using the median per row of the overscan region",
349 },
350 )
351 overscanOrder = pexConfig.Field(
352 dtype=int,
353 doc=("Order of polynomial or to fit if overscan fit type is a polynomial, " +
354 "or number of spline knots if overscan fit type is a spline."),
355 default=1,
356 )
357 overscanNumSigmaClip = pexConfig.Field(
358 dtype=float,
359 doc="Rejection threshold (sigma) for collapsing overscan before fit",
360 default=3.0,
361 )
362 overscanIsInt = pexConfig.Field(
363 dtype=bool,
364 doc="Treat overscan as an integer image for purposes of overscan.FitType=MEDIAN" +
365 " and overscan.FitType=MEDIAN_PER_ROW.",
366 default=True,
367 )
368 overscanNumLeadingColumnsToSkip = pexConfig.Field(
369 dtype=int,
370 doc="Number of columns to skip in overscan, i.e. those closest to amplifier",
371 default=0,
372 )
373 overscanNumTrailingColumnsToSkip = pexConfig.Field(
374 dtype=int,
375 doc="Number of columns to skip in overscan, i.e. those farthest from amplifier",
376 default=0,
377 )
378 overscanMaxDev = pexConfig.Field( 378 ↛ exitline 378 didn't jump to the function exit
379 dtype=float,
380 doc="Maximum deviation from the median for overscan",
381 default=1000.0, check=lambda x: x > 0
382 )
383 overscanBiasJump = pexConfig.Field(
384 dtype=bool,
385 doc="Fit the overscan in a piecewise-fashion to correct for bias jumps?",
386 default=False,
387 )
388 overscanBiasJumpKeyword = pexConfig.Field(
389 dtype=str,
390 doc="Header keyword containing information about devices.",
391 default="NO_SUCH_KEY",
392 )
393 overscanBiasJumpDevices = pexConfig.ListField(
394 dtype=str,
395 doc="List of devices that need piecewise overscan correction.",
396 default=(),
397 )
398 overscanBiasJumpLocation = pexConfig.Field(
399 dtype=int,
400 doc="Location of bias jump along y-axis.",
401 default=0,
402 )
404 # Amplifier to CCD assembly configuration
405 doAssembleCcd = pexConfig.Field(
406 dtype=bool,
407 default=True,
408 doc="Assemble amp-level exposures into a ccd-level exposure?"
409 )
410 assembleCcd = pexConfig.ConfigurableField(
411 target=AssembleCcdTask,
412 doc="CCD assembly task",
413 )
415 # General calibration configuration.
416 doAssembleIsrExposures = pexConfig.Field(
417 dtype=bool,
418 default=False,
419 doc="Assemble amp-level calibration exposures into ccd-level exposure?"
420 )
421 doTrimToMatchCalib = pexConfig.Field(
422 dtype=bool,
423 default=False,
424 doc="Trim raw data to match calibration bounding boxes?"
425 )
427 # Bias subtraction.
428 doBias = pexConfig.Field(
429 dtype=bool,
430 doc="Apply bias frame correction?",
431 default=True,
432 )
433 biasDataProductName = pexConfig.Field(
434 dtype=str,
435 doc="Name of the bias data product",
436 default="bias",
437 )
439 # Variance construction
440 doVariance = pexConfig.Field(
441 dtype=bool,
442 doc="Calculate variance?",
443 default=True
444 )
445 gain = pexConfig.Field(
446 dtype=float,
447 doc="The gain to use if no Detector is present in the Exposure (ignored if NaN)",
448 default=float("NaN"),
449 )
450 readNoise = pexConfig.Field(
451 dtype=float,
452 doc="The read noise to use if no Detector is present in the Exposure",
453 default=0.0,
454 )
455 doEmpiricalReadNoise = pexConfig.Field(
456 dtype=bool,
457 default=False,
458 doc="Calculate empirical read noise instead of value from AmpInfo data?"
459 )
461 # Linearization.
462 doLinearize = pexConfig.Field(
463 dtype=bool,
464 doc="Correct for nonlinearity of the detector's response?",
465 default=True,
466 )
468 # Crosstalk.
469 doCrosstalk = pexConfig.Field(
470 dtype=bool,
471 doc="Apply intra-CCD crosstalk correction?",
472 default=False,
473 )
474 doCrosstalkBeforeAssemble = pexConfig.Field(
475 dtype=bool,
476 doc="Apply crosstalk correction before CCD assembly, and before trimming?",
477 default=False,
478 )
479 crosstalk = pexConfig.ConfigurableField(
480 target=CrosstalkTask,
481 doc="Intra-CCD crosstalk correction",
482 )
484 # Masking options.
485 doDefect = pexConfig.Field(
486 dtype=bool,
487 doc="Apply correction for CCD defects, e.g. hot pixels?",
488 default=True,
489 )
490 doNanMasking = pexConfig.Field(
491 dtype=bool,
492 doc="Mask NAN pixels?",
493 default=True,
494 )
495 doWidenSaturationTrails = pexConfig.Field(
496 dtype=bool,
497 doc="Widen bleed trails based on their width?",
498 default=True
499 )
501 # Brighter-Fatter correction.
502 doBrighterFatter = pexConfig.Field(
503 dtype=bool,
504 default=False,
505 doc="Apply the brighter fatter correction"
506 )
507 brighterFatterLevel = pexConfig.ChoiceField(
508 dtype=str,
509 default="DETECTOR",
510 doc="The level at which to correct for brighter-fatter.",
511 allowed={
512 "AMP": "Every amplifier treated separately.",
513 "DETECTOR": "One kernel per detector",
514 }
515 )
516 brighterFatterMaxIter = pexConfig.Field(
517 dtype=int,
518 default=10,
519 doc="Maximum number of iterations for the brighter fatter correction"
520 )
521 brighterFatterThreshold = pexConfig.Field(
522 dtype=float,
523 default=1000,
524 doc="Threshold used to stop iterating the brighter fatter correction. It is the "
525 " absolute value of the difference between the current corrected image and the one"
526 " from the previous iteration summed over all the pixels."
527 )
528 brighterFatterApplyGain = pexConfig.Field(
529 dtype=bool,
530 default=True,
531 doc="Should the gain be applied when applying the brighter fatter correction?"
532 )
533 brighterFatterMaskGrowSize = pexConfig.Field(
534 dtype=int,
535 default=0,
536 doc="Number of pixels to grow the masks listed in config.maskListToInterpolate "
537 " when brighter-fatter correction is applied."
538 )
540 # Dark subtraction.
541 doDark = pexConfig.Field(
542 dtype=bool,
543 doc="Apply dark frame correction?",
544 default=True,
545 )
546 darkDataProductName = pexConfig.Field(
547 dtype=str,
548 doc="Name of the dark data product",
549 default="dark",
550 )
552 # Camera-specific stray light removal.
553 doStrayLight = pexConfig.Field(
554 dtype=bool,
555 doc="Subtract stray light in the y-band (due to encoder LEDs)?",
556 default=False,
557 )
558 strayLight = pexConfig.ConfigurableField(
559 target=StrayLightTask,
560 doc="y-band stray light correction"
561 )
563 # Flat correction.
564 doFlat = pexConfig.Field(
565 dtype=bool,
566 doc="Apply flat field correction?",
567 default=True,
568 )
569 flatDataProductName = pexConfig.Field(
570 dtype=str,
571 doc="Name of the flat data product",
572 default="flat",
573 )
574 flatScalingType = pexConfig.ChoiceField(
575 dtype=str,
576 doc="The method for scaling the flat on the fly.",
577 default='USER',
578 allowed={
579 "USER": "Scale by flatUserScale",
580 "MEAN": "Scale by the inverse of the mean",
581 "MEDIAN": "Scale by the inverse of the median",
582 },
583 )
584 flatUserScale = pexConfig.Field(
585 dtype=float,
586 doc="If flatScalingType is 'USER' then scale flat by this amount; ignored otherwise",
587 default=1.0,
588 )
589 doTweakFlat = pexConfig.Field(
590 dtype=bool,
591 doc="Tweak flats to match observed amplifier ratios?",
592 default=False
593 )
595 # Amplifier normalization based on gains instead of using flats configuration.
596 doApplyGains = pexConfig.Field(
597 dtype=bool,
598 doc="Correct the amplifiers for their gains instead of applying flat correction",
599 default=False,
600 )
601 normalizeGains = pexConfig.Field(
602 dtype=bool,
603 doc="Normalize all the amplifiers in each CCD to have the same median value.",
604 default=False,
605 )
607 # Fringe correction.
608 doFringe = pexConfig.Field(
609 dtype=bool,
610 doc="Apply fringe correction?",
611 default=True,
612 )
613 fringe = pexConfig.ConfigurableField(
614 target=FringeTask,
615 doc="Fringe subtraction task",
616 )
617 fringeAfterFlat = pexConfig.Field(
618 dtype=bool,
619 doc="Do fringe subtraction after flat-fielding?",
620 default=True,
621 )
623 # Distortion model application.
624 doAddDistortionModel = pexConfig.Field(
625 dtype=bool,
626 doc="Apply a distortion model based on camera geometry to the WCS?",
627 default=True,
628 deprecated=("Camera geometry is incorporated when reading the raw files."
629 " This option no longer is used, and will be removed after v19.")
630 )
632 # Initial CCD-level background statistics options.
633 doMeasureBackground = pexConfig.Field(
634 dtype=bool,
635 doc="Measure the background level on the reduced image?",
636 default=False,
637 )
639 # Camera-specific masking configuration.
640 doCameraSpecificMasking = pexConfig.Field(
641 dtype=bool,
642 doc="Mask camera-specific bad regions?",
643 default=False,
644 )
645 masking = pexConfig.ConfigurableField(
646 target=MaskingTask,
647 doc="Masking task."
648 )
650 # Interpolation options.
652 doInterpolate = pexConfig.Field(
653 dtype=bool,
654 doc="Interpolate masked pixels?",
655 default=True,
656 )
657 doSaturationInterpolation = pexConfig.Field(
658 dtype=bool,
659 doc="Perform interpolation over pixels masked as saturated?"
660 " NB: This is independent of doSaturation; if that is False this plane"
661 " will likely be blank, resulting in a no-op here.",
662 default=True,
663 )
664 doNanInterpolation = pexConfig.Field(
665 dtype=bool,
666 doc="Perform interpolation over pixels masked as NaN?"
667 " NB: This is independent of doNanMasking; if that is False this plane"
668 " will likely be blank, resulting in a no-op here.",
669 default=True,
670 )
671 doNanInterpAfterFlat = pexConfig.Field(
672 dtype=bool,
673 doc=("If True, ensure we interpolate NaNs after flat-fielding, even if we "
674 "also have to interpolate them before flat-fielding."),
675 default=False,
676 )
677 maskListToInterpolate = pexConfig.ListField(
678 dtype=str,
679 doc="List of mask planes that should be interpolated.",
680 default=['SAT', 'BAD', 'UNMASKEDNAN'],
681 )
682 doSaveInterpPixels = pexConfig.Field(
683 dtype=bool,
684 doc="Save a copy of the pre-interpolated pixel values?",
685 default=False,
686 )
688 # Default photometric calibration options.
689 fluxMag0T1 = pexConfig.DictField(
690 keytype=str,
691 itemtype=float,
692 doc="The approximate flux of a zero-magnitude object in a one-second exposure, per filter.",
693 default=dict((f, pow(10.0, 0.4*m)) for f, m in (("Unknown", 28.0),
694 ))
695 )
696 defaultFluxMag0T1 = pexConfig.Field(
697 dtype=float,
698 doc="Default value for fluxMag0T1 (for an unrecognized filter).",
699 default=pow(10.0, 0.4*28.0)
700 )
702 # Vignette correction configuration.
703 doVignette = pexConfig.Field(
704 dtype=bool,
705 doc="Apply vignetting parameters?",
706 default=False,
707 )
708 vignette = pexConfig.ConfigurableField(
709 target=VignetteTask,
710 doc="Vignetting task.",
711 )
713 # Transmission curve configuration.
714 doAttachTransmissionCurve = pexConfig.Field(
715 dtype=bool,
716 default=False,
717 doc="Construct and attach a wavelength-dependent throughput curve for this CCD image?"
718 )
719 doUseOpticsTransmission = pexConfig.Field(
720 dtype=bool,
721 default=True,
722 doc="Load and use transmission_optics (if doAttachTransmissionCurve is True)?"
723 )
724 doUseFilterTransmission = pexConfig.Field(
725 dtype=bool,
726 default=True,
727 doc="Load and use transmission_filter (if doAttachTransmissionCurve is True)?"
728 )
729 doUseSensorTransmission = pexConfig.Field(
730 dtype=bool,
731 default=True,
732 doc="Load and use transmission_sensor (if doAttachTransmissionCurve is True)?"
733 )
734 doUseAtmosphereTransmission = pexConfig.Field(
735 dtype=bool,
736 default=True,
737 doc="Load and use transmission_atmosphere (if doAttachTransmissionCurve is True)?"
738 )
740 # Illumination correction.
741 doIlluminationCorrection = pexConfig.Field(
742 dtype=bool,
743 default=False,
744 doc="Perform illumination correction?"
745 )
746 illuminationCorrectionDataProductName = pexConfig.Field(
747 dtype=str,
748 doc="Name of the illumination correction data product.",
749 default="illumcor",
750 )
751 illumScale = pexConfig.Field(
752 dtype=float,
753 doc="Scale factor for the illumination correction.",
754 default=1.0,
755 )
756 illumFilters = pexConfig.ListField(
757 dtype=str,
758 default=[],
759 doc="Only perform illumination correction for these filters."
760 )
762 # Write the outputs to disk. If ISR is run as a subtask, this may not be needed.
763 doWrite = pexConfig.Field(
764 dtype=bool,
765 doc="Persist postISRCCD?",
766 default=True,
767 )
769 def validate(self):
770 super().validate()
771 if self.doFlat and self.doApplyGains:
772 raise ValueError("You may not specify both doFlat and doApplyGains")
773 if self.doSaturationInterpolation and "SAT" not in self.maskListToInterpolate:
774 self.config.maskListToInterpolate.append("SAT")
775 if self.doNanInterpolation and "UNMASKEDNAN" not in self.maskListToInterpolate:
776 self.config.maskListToInterpolate.append("UNMASKEDNAN")
779class IsrTask(pipeBase.PipelineTask, pipeBase.CmdLineTask):
780 """Apply common instrument signature correction algorithms to a raw frame.
782 The process for correcting imaging data is very similar from
783 camera to camera. This task provides a vanilla implementation of
784 doing these corrections, including the ability to turn certain
785 corrections off if they are not needed. The inputs to the primary
786 method, `run()`, are a raw exposure to be corrected and the
787 calibration data products. The raw input is a single chip sized
788 mosaic of all amps including overscans and other non-science
789 pixels. The method `runDataRef()` identifies and defines the
790 calibration data products, and is intended for use by a
791 `lsst.pipe.base.cmdLineTask.CmdLineTask` and takes as input only a
792 `daf.persistence.butlerSubset.ButlerDataRef`. This task may be
793 subclassed for different camera, although the most camera specific
794 methods have been split into subtasks that can be redirected
795 appropriately.
797 The __init__ method sets up the subtasks for ISR processing, using
798 the defaults from `lsst.ip.isr`.
800 Parameters
801 ----------
802 args : `list`
803 Positional arguments passed to the Task constructor. None used at this time.
804 kwargs : `dict`, optional
805 Keyword arguments passed on to the Task constructor. None used at this time.
806 """
807 ConfigClass = IsrTaskConfig
808 _DefaultName = "isr"
810 def __init__(self, **kwargs):
811 super().__init__(**kwargs)
812 self.makeSubtask("assembleCcd")
813 self.makeSubtask("crosstalk")
814 self.makeSubtask("strayLight")
815 self.makeSubtask("fringe")
816 self.makeSubtask("masking")
817 self.makeSubtask("vignette")
819 def runQuantum(self, butlerQC, inputRefs, outputRefs):
820 inputs = butlerQC.get(inputRefs)
822 try:
823 inputs['detectorNum'] = inputRefs.ccdExposure.dataId['detector']
824 except Exception as e:
825 raise ValueError("Failure to find valid detectorNum value for Dataset %s: %s." %
826 (inputRefs, e))
828 inputs['isGen3'] = True
830 detector = inputs['ccdExposure'].getDetector()
832 if self.doLinearize(detector) is True:
833 if 'linearizer' in inputs and isinstance(inputs['linearizer'], dict):
834 linearizer = linearize.Linearizer(detector=detector, log=self.log)
835 linearizer.fromYaml(inputs['linearizer'])
836 else:
837 linearizer = linearize.Linearizer(table=inputs.get('linearizer', None), detector=detector,
838 log=self.log)
839 inputs['linearizer'] = linearizer
841 if self.config.doDefect is True:
842 if "defects" in inputs and inputs['defects'] is not None:
843 # defects is loaded as a BaseCatalog with columns x0, y0, width, height.
844 # masking expects a list of defects defined by their bounding box
845 if not isinstance(inputs["defects"], Defects):
846 inputs["defects"] = Defects.fromTable(inputs["defects"])
848 # Load the correct style of brighter fatter kernel, and repack
849 # the information as a numpy array.
850 if self.config.doBrighterFatter:
851 brighterFatterKernel = inputs.pop('newBFKernel', None)
852 if brighterFatterKernel is None:
853 brighterFatterKernel = inputs.get('bfKernel', None)
855 if brighterFatterKernel is not None and not isinstance(brighterFatterKernel, numpy.ndarray):
856 detId = detector.getId()
857 inputs['bfGains'] = brighterFatterKernel.gain
858 # If the kernel is not an ndarray, it's the cp_pipe version
859 # so extract the kernel for this detector, or raise an error
860 if self.config.brighterFatterLevel == 'DETECTOR':
861 if brighterFatterKernel.detectorKernel:
862 inputs['bfKernel'] = brighterFatterKernel.detectorKernel[detId]
863 elif brighterFatterKernel.detectorKernelFromAmpKernels:
864 inputs['bfKernel'] = brighterFatterKernel.detectorKernelFromAmpKernels[detId]
865 else:
866 raise RuntimeError("Failed to extract kernel from new-style BF kernel.")
867 else:
868 # TODO DM-15631 for implementing this
869 raise NotImplementedError("Per-amplifier brighter-fatter correction not implemented")
871 # Broken: DM-17169
872 # ci_hsc does not use crosstalkSources, as it's intra-CCD CT only. This needs to be
873 # fixed for non-HSC cameras in the future.
874 # inputs['crosstalkSources'] = (self.crosstalk.prepCrosstalk(inputsIds['ccdExposure'])
875 # if self.config.doCrosstalk else None)
877 if self.config.doFringe is True and self.fringe.checkFilter(inputs['ccdExposure']):
878 expId = inputs['ccdExposure'].getInfo().getVisitInfo().getExposureId()
879 inputs['fringes'] = self.fringe.loadFringes(inputs['fringes'],
880 expId=expId,
881 assembler=self.assembleCcd
882 if self.config.doAssembleIsrExposures else None)
883 else:
884 inputs['fringes'] = pipeBase.Struct(fringes=None)
886 if self.config.doStrayLight is True and self.strayLight.checkFilter(inputs['ccdExposure']):
887 if 'strayLightData' not in inputs:
888 inputs['strayLightData'] = None
890 outputs = self.run(**inputs)
891 butlerQC.put(outputs, outputRefs)
893 def readIsrData(self, dataRef, rawExposure):
894 """!Retrieve necessary frames for instrument signature removal.
896 Pre-fetching all required ISR data products limits the IO
897 required by the ISR. Any conflict between the calibration data
898 available and that needed for ISR is also detected prior to
899 doing processing, allowing it to fail quickly.
901 Parameters
902 ----------
903 dataRef : `daf.persistence.butlerSubset.ButlerDataRef`
904 Butler reference of the detector data to be processed
905 rawExposure : `afw.image.Exposure`
906 The raw exposure that will later be corrected with the
907 retrieved calibration data; should not be modified in this
908 method.
910 Returns
911 -------
912 result : `lsst.pipe.base.Struct`
913 Result struct with components (which may be `None`):
914 - ``bias``: bias calibration frame (`afw.image.Exposure`)
915 - ``linearizer``: functor for linearization (`ip.isr.linearize.LinearizeBase`)
916 - ``crosstalkSources``: list of possible crosstalk sources (`list`)
917 - ``dark``: dark calibration frame (`afw.image.Exposure`)
918 - ``flat``: flat calibration frame (`afw.image.Exposure`)
919 - ``bfKernel``: Brighter-Fatter kernel (`numpy.ndarray`)
920 - ``defects``: list of defects (`lsst.meas.algorithms.Defects`)
921 - ``fringes``: `lsst.pipe.base.Struct` with components:
922 - ``fringes``: fringe calibration frame (`afw.image.Exposure`)
923 - ``seed``: random seed derived from the ccdExposureId for random
924 number generator (`uint32`).
925 - ``opticsTransmission``: `lsst.afw.image.TransmissionCurve`
926 A ``TransmissionCurve`` that represents the throughput of the optics,
927 to be evaluated in focal-plane coordinates.
928 - ``filterTransmission`` : `lsst.afw.image.TransmissionCurve`
929 A ``TransmissionCurve`` that represents the throughput of the filter
930 itself, to be evaluated in focal-plane coordinates.
931 - ``sensorTransmission`` : `lsst.afw.image.TransmissionCurve`
932 A ``TransmissionCurve`` that represents the throughput of the sensor
933 itself, to be evaluated in post-assembly trimmed detector coordinates.
934 - ``atmosphereTransmission`` : `lsst.afw.image.TransmissionCurve`
935 A ``TransmissionCurve`` that represents the throughput of the
936 atmosphere, assumed to be spatially constant.
937 - ``strayLightData`` : `object`
938 An opaque object containing calibration information for
939 stray-light correction. If `None`, no correction will be
940 performed.
941 - ``illumMaskedImage`` : illumination correction image (`lsst.afw.image.MaskedImage`)
943 Raises
944 ------
945 NotImplementedError :
946 Raised if a per-amplifier brighter-fatter kernel is requested by the configuration.
947 """
948 try:
949 dateObs = rawExposure.getInfo().getVisitInfo().getDate()
950 dateObs = dateObs.toPython().isoformat()
951 except RuntimeError:
952 self.log.warn("Unable to identify dateObs for rawExposure.")
953 dateObs = None
955 ccd = rawExposure.getDetector()
956 filterName = afwImage.Filter(rawExposure.getFilter().getId()).getName() # Canonical name for filter
957 rawExposure.mask.addMaskPlane("UNMASKEDNAN") # needed to match pre DM-15862 processing.
958 biasExposure = (self.getIsrExposure(dataRef, self.config.biasDataProductName)
959 if self.config.doBias else None)
960 # immediate=True required for functors and linearizers are functors; see ticket DM-6515
961 linearizer = (dataRef.get("linearizer", immediate=True)
962 if self.doLinearize(ccd) else None)
963 if linearizer is not None and not isinstance(linearizer, numpy.ndarray):
964 linearizer.log = self.log
965 if isinstance(linearizer, numpy.ndarray):
966 linearizer = linearize.Linearizer(table=linearizer, detector=ccd)
967 crosstalkSources = (self.crosstalk.prepCrosstalk(dataRef)
968 if self.config.doCrosstalk else None)
969 darkExposure = (self.getIsrExposure(dataRef, self.config.darkDataProductName)
970 if self.config.doDark else None)
971 flatExposure = (self.getIsrExposure(dataRef, self.config.flatDataProductName,
972 dateObs=dateObs)
973 if self.config.doFlat else None)
975 brighterFatterKernel = None
976 brighterFatterGains = None
977 if self.config.doBrighterFatter is True:
978 try:
979 # Use the new-style cp_pipe version of the kernel if it exists
980 # If using a new-style kernel, always use the self-consistent
981 # gains, i.e. the ones inside the kernel object itself
982 brighterFatterKernel = dataRef.get("brighterFatterKernel")
983 brighterFatterGains = brighterFatterKernel.gain
984 self.log.info("New style bright-fatter kernel (brighterFatterKernel) loaded")
985 except NoResults:
986 try: # Fall back to the old-style numpy-ndarray style kernel if necessary.
987 brighterFatterKernel = dataRef.get("bfKernel")
988 self.log.info("Old style bright-fatter kernel (np.array) loaded")
989 except NoResults:
990 brighterFatterKernel = None
991 if brighterFatterKernel is not None and not isinstance(brighterFatterKernel, numpy.ndarray):
992 # If the kernel is not an ndarray, it's the cp_pipe version
993 # so extract the kernel for this detector, or raise an error
994 if self.config.brighterFatterLevel == 'DETECTOR':
995 if brighterFatterKernel.detectorKernel:
996 brighterFatterKernel = brighterFatterKernel.detectorKernel[ccd.getId()]
997 elif brighterFatterKernel.detectorKernelFromAmpKernels:
998 brighterFatterKernel = brighterFatterKernel.detectorKernelFromAmpKernels[ccd.getId()]
999 else:
1000 raise RuntimeError("Failed to extract kernel from new-style BF kernel.")
1001 else:
1002 # TODO DM-15631 for implementing this
1003 raise NotImplementedError("Per-amplifier brighter-fatter correction not implemented")
1005 defectList = (dataRef.get("defects")
1006 if self.config.doDefect else None)
1007 fringeStruct = (self.fringe.readFringes(dataRef, assembler=self.assembleCcd
1008 if self.config.doAssembleIsrExposures else None)
1009 if self.config.doFringe and self.fringe.checkFilter(rawExposure)
1010 else pipeBase.Struct(fringes=None))
1012 if self.config.doAttachTransmissionCurve:
1013 opticsTransmission = (dataRef.get("transmission_optics")
1014 if self.config.doUseOpticsTransmission else None)
1015 filterTransmission = (dataRef.get("transmission_filter")
1016 if self.config.doUseFilterTransmission else None)
1017 sensorTransmission = (dataRef.get("transmission_sensor")
1018 if self.config.doUseSensorTransmission else None)
1019 atmosphereTransmission = (dataRef.get("transmission_atmosphere")
1020 if self.config.doUseAtmosphereTransmission else None)
1021 else:
1022 opticsTransmission = None
1023 filterTransmission = None
1024 sensorTransmission = None
1025 atmosphereTransmission = None
1027 if self.config.doStrayLight:
1028 strayLightData = self.strayLight.readIsrData(dataRef, rawExposure)
1029 else:
1030 strayLightData = None
1032 illumMaskedImage = (self.getIsrExposure(dataRef,
1033 self.config.illuminationCorrectionDataProductName).getMaskedImage()
1034 if (self.config.doIlluminationCorrection and
1035 filterName in self.config.illumFilters)
1036 else None)
1038 # Struct should include only kwargs to run()
1039 return pipeBase.Struct(bias=biasExposure,
1040 linearizer=linearizer,
1041 crosstalkSources=crosstalkSources,
1042 dark=darkExposure,
1043 flat=flatExposure,
1044 bfKernel=brighterFatterKernel,
1045 bfGains=brighterFatterGains,
1046 defects=defectList,
1047 fringes=fringeStruct,
1048 opticsTransmission=opticsTransmission,
1049 filterTransmission=filterTransmission,
1050 sensorTransmission=sensorTransmission,
1051 atmosphereTransmission=atmosphereTransmission,
1052 strayLightData=strayLightData,
1053 illumMaskedImage=illumMaskedImage
1054 )
1056 @pipeBase.timeMethod
1057 def run(self, ccdExposure, camera=None, bias=None, linearizer=None, crosstalkSources=None,
1058 dark=None, flat=None, bfKernel=None, bfGains=None, defects=None,
1059 fringes=pipeBase.Struct(fringes=None), opticsTransmission=None, filterTransmission=None,
1060 sensorTransmission=None, atmosphereTransmission=None,
1061 detectorNum=None, strayLightData=None, illumMaskedImage=None,
1062 isGen3=False,
1063 ):
1064 """!Perform instrument signature removal on an exposure.
1066 Steps included in the ISR processing, in order performed, are:
1067 - saturation and suspect pixel masking
1068 - overscan subtraction
1069 - CCD assembly of individual amplifiers
1070 - bias subtraction
1071 - variance image construction
1072 - linearization of non-linear response
1073 - crosstalk masking
1074 - brighter-fatter correction
1075 - dark subtraction
1076 - fringe correction
1077 - stray light subtraction
1078 - flat correction
1079 - masking of known defects and camera specific features
1080 - vignette calculation
1081 - appending transmission curve and distortion model
1083 Parameters
1084 ----------
1085 ccdExposure : `lsst.afw.image.Exposure`
1086 The raw exposure that is to be run through ISR. The
1087 exposure is modified by this method.
1088 camera : `lsst.afw.cameraGeom.Camera`, optional
1089 The camera geometry for this exposure. Used to select the
1090 distortion model appropriate for this data.
1091 bias : `lsst.afw.image.Exposure`, optional
1092 Bias calibration frame.
1093 linearizer : `lsst.ip.isr.linearize.LinearizeBase`, optional
1094 Functor for linearization.
1095 crosstalkSources : `list`, optional
1096 List of possible crosstalk sources.
1097 dark : `lsst.afw.image.Exposure`, optional
1098 Dark calibration frame.
1099 flat : `lsst.afw.image.Exposure`, optional
1100 Flat calibration frame.
1101 bfKernel : `numpy.ndarray`, optional
1102 Brighter-fatter kernel.
1103 bfGains : `dict` of `float`, optional
1104 Gains used to override the detector's nominal gains for the
1105 brighter-fatter correction. A dict keyed by amplifier name for
1106 the detector in question.
1107 defects : `lsst.meas.algorithms.Defects`, optional
1108 List of defects.
1109 fringes : `lsst.pipe.base.Struct`, optional
1110 Struct containing the fringe correction data, with
1111 elements:
1112 - ``fringes``: fringe calibration frame (`afw.image.Exposure`)
1113 - ``seed``: random seed derived from the ccdExposureId for random
1114 number generator (`uint32`)
1115 opticsTransmission: `lsst.afw.image.TransmissionCurve`, optional
1116 A ``TransmissionCurve`` that represents the throughput of the optics,
1117 to be evaluated in focal-plane coordinates.
1118 filterTransmission : `lsst.afw.image.TransmissionCurve`
1119 A ``TransmissionCurve`` that represents the throughput of the filter
1120 itself, to be evaluated in focal-plane coordinates.
1121 sensorTransmission : `lsst.afw.image.TransmissionCurve`
1122 A ``TransmissionCurve`` that represents the throughput of the sensor
1123 itself, to be evaluated in post-assembly trimmed detector coordinates.
1124 atmosphereTransmission : `lsst.afw.image.TransmissionCurve`
1125 A ``TransmissionCurve`` that represents the throughput of the
1126 atmosphere, assumed to be spatially constant.
1127 detectorNum : `int`, optional
1128 The integer number for the detector to process.
1129 isGen3 : bool, optional
1130 Flag this call to run() as using the Gen3 butler environment.
1131 strayLightData : `object`, optional
1132 Opaque object containing calibration information for stray-light
1133 correction. If `None`, no correction will be performed.
1134 illumMaskedImage : `lsst.afw.image.MaskedImage`, optional
1135 Illumination correction image.
1137 Returns
1138 -------
1139 result : `lsst.pipe.base.Struct`
1140 Result struct with component:
1141 - ``exposure`` : `afw.image.Exposure`
1142 The fully ISR corrected exposure.
1143 - ``outputExposure`` : `afw.image.Exposure`
1144 An alias for `exposure`
1145 - ``ossThumb`` : `numpy.ndarray`
1146 Thumbnail image of the exposure after overscan subtraction.
1147 - ``flattenedThumb`` : `numpy.ndarray`
1148 Thumbnail image of the exposure after flat-field correction.
1150 Raises
1151 ------
1152 RuntimeError
1153 Raised if a configuration option is set to True, but the
1154 required calibration data has not been specified.
1156 Notes
1157 -----
1158 The current processed exposure can be viewed by setting the
1159 appropriate lsstDebug entries in the `debug.display`
1160 dictionary. The names of these entries correspond to some of
1161 the IsrTaskConfig Boolean options, with the value denoting the
1162 frame to use. The exposure is shown inside the matching
1163 option check and after the processing of that step has
1164 finished. The steps with debug points are:
1166 doAssembleCcd
1167 doBias
1168 doCrosstalk
1169 doBrighterFatter
1170 doDark
1171 doFringe
1172 doStrayLight
1173 doFlat
1175 In addition, setting the "postISRCCD" entry displays the
1176 exposure after all ISR processing has finished.
1178 """
1180 if isGen3 is True:
1181 # Gen3 currently cannot automatically do configuration overrides.
1182 # DM-15257 looks to discuss this issue.
1183 # Configure input exposures;
1184 if detectorNum is None:
1185 raise RuntimeError("Must supply the detectorNum if running as Gen3.")
1187 ccdExposure = self.ensureExposure(ccdExposure, camera, detectorNum)
1188 bias = self.ensureExposure(bias, camera, detectorNum)
1189 dark = self.ensureExposure(dark, camera, detectorNum)
1190 flat = self.ensureExposure(flat, camera, detectorNum)
1191 else:
1192 if isinstance(ccdExposure, ButlerDataRef):
1193 return self.runDataRef(ccdExposure)
1195 ccd = ccdExposure.getDetector()
1196 filterName = afwImage.Filter(ccdExposure.getFilter().getId()).getName() # Canonical name for filter
1198 if not ccd:
1199 assert not self.config.doAssembleCcd, "You need a Detector to run assembleCcd."
1200 ccd = [FakeAmp(ccdExposure, self.config)]
1202 # Validate Input
1203 if self.config.doBias and bias is None:
1204 raise RuntimeError("Must supply a bias exposure if config.doBias=True.")
1205 if self.doLinearize(ccd) and linearizer is None:
1206 raise RuntimeError("Must supply a linearizer if config.doLinearize=True for this detector.")
1207 if self.config.doBrighterFatter and bfKernel is None:
1208 raise RuntimeError("Must supply a kernel if config.doBrighterFatter=True.")
1209 if self.config.doDark and dark is None:
1210 raise RuntimeError("Must supply a dark exposure if config.doDark=True.")
1211 if self.config.doFlat and flat is None:
1212 raise RuntimeError("Must supply a flat exposure if config.doFlat=True.")
1213 if self.config.doDefect and defects is None:
1214 raise RuntimeError("Must supply defects if config.doDefect=True.")
1215 if (self.config.doFringe and filterName in self.fringe.config.filters and
1216 fringes.fringes is None):
1217 # The `fringes` object needs to be a pipeBase.Struct, as
1218 # we use it as a `dict` for the parameters of
1219 # `FringeTask.run()`. The `fringes.fringes` `list` may
1220 # not be `None` if `doFringe=True`. Otherwise, raise.
1221 raise RuntimeError("Must supply fringe exposure as a pipeBase.Struct.")
1222 if (self.config.doIlluminationCorrection and filterName in self.config.illumFilters and
1223 illumMaskedImage is None):
1224 raise RuntimeError("Must supply an illumcor if config.doIlluminationCorrection=True.")
1226 # Begin ISR processing.
1227 if self.config.doConvertIntToFloat:
1228 self.log.info("Converting exposure to floating point values.")
1229 ccdExposure = self.convertIntToFloat(ccdExposure)
1231 # Amplifier level processing.
1232 overscans = []
1233 for amp in ccd:
1234 # if ccdExposure is one amp, check for coverage to prevent performing ops multiple times
1235 if ccdExposure.getBBox().contains(amp.getBBox()):
1236 # Check for fully masked bad amplifiers, and generate masks for SUSPECT and SATURATED values.
1237 badAmp = self.maskAmplifier(ccdExposure, amp, defects)
1239 if self.config.doOverscan and not badAmp:
1240 # Overscan correction on amp-by-amp basis.
1241 overscanResults = self.overscanCorrection(ccdExposure, amp)
1242 self.log.debug("Corrected overscan for amplifier %s.", amp.getName())
1243 if overscanResults is not None and \
1244 self.config.qa is not None and self.config.qa.saveStats is True:
1245 if isinstance(overscanResults.overscanFit, float):
1246 qaMedian = overscanResults.overscanFit
1247 qaStdev = float("NaN")
1248 else:
1249 qaStats = afwMath.makeStatistics(overscanResults.overscanFit,
1250 afwMath.MEDIAN | afwMath.STDEVCLIP)
1251 qaMedian = qaStats.getValue(afwMath.MEDIAN)
1252 qaStdev = qaStats.getValue(afwMath.STDEVCLIP)
1254 self.metadata.set(f"ISR OSCAN {amp.getName()} MEDIAN", qaMedian)
1255 self.metadata.set(f"ISR OSCAN {amp.getName()} STDEV", qaStdev)
1256 self.log.debug(" Overscan stats for amplifer %s: %f +/- %f",
1257 amp.getName(), qaMedian, qaStdev)
1258 ccdExposure.getMetadata().set('OVERSCAN', "Overscan corrected")
1259 else:
1260 if badAmp:
1261 self.log.warn("Amplifier %s is bad.", amp.getName())
1262 overscanResults = None
1264 overscans.append(overscanResults if overscanResults is not None else None)
1265 else:
1266 self.log.info("Skipped OSCAN for %s.", amp.getName())
1268 if self.config.doCrosstalk and self.config.doCrosstalkBeforeAssemble:
1269 self.log.info("Applying crosstalk correction.")
1270 self.crosstalk.run(ccdExposure, crosstalkSources=crosstalkSources)
1271 self.debugView(ccdExposure, "doCrosstalk")
1273 if self.config.doAssembleCcd:
1274 self.log.info("Assembling CCD from amplifiers.")
1275 ccdExposure = self.assembleCcd.assembleCcd(ccdExposure)
1277 if self.config.expectWcs and not ccdExposure.getWcs():
1278 self.log.warn("No WCS found in input exposure.")
1279 self.debugView(ccdExposure, "doAssembleCcd")
1281 ossThumb = None
1282 if self.config.qa.doThumbnailOss:
1283 ossThumb = isrQa.makeThumbnail(ccdExposure, isrQaConfig=self.config.qa)
1285 if self.config.doBias:
1286 self.log.info("Applying bias correction.")
1287 isrFunctions.biasCorrection(ccdExposure.getMaskedImage(), bias.getMaskedImage(),
1288 trimToFit=self.config.doTrimToMatchCalib)
1289 self.debugView(ccdExposure, "doBias")
1291 if self.config.doVariance:
1292 for amp, overscanResults in zip(ccd, overscans):
1293 if ccdExposure.getBBox().contains(amp.getBBox()):
1294 self.log.debug("Constructing variance map for amplifer %s.", amp.getName())
1295 ampExposure = ccdExposure.Factory(ccdExposure, amp.getBBox())
1296 if overscanResults is not None:
1297 self.updateVariance(ampExposure, amp,
1298 overscanImage=overscanResults.overscanImage)
1299 else:
1300 self.updateVariance(ampExposure, amp,
1301 overscanImage=None)
1302 if self.config.qa is not None and self.config.qa.saveStats is True:
1303 qaStats = afwMath.makeStatistics(ampExposure.getVariance(),
1304 afwMath.MEDIAN | afwMath.STDEVCLIP)
1305 self.metadata.set(f"ISR VARIANCE {amp.getName()} MEDIAN",
1306 qaStats.getValue(afwMath.MEDIAN))
1307 self.metadata.set(f"ISR VARIANCE {amp.getName()} STDEV",
1308 qaStats.getValue(afwMath.STDEVCLIP))
1309 self.log.debug(" Variance stats for amplifer %s: %f +/- %f.",
1310 amp.getName(), qaStats.getValue(afwMath.MEDIAN),
1311 qaStats.getValue(afwMath.STDEVCLIP))
1313 if self.doLinearize(ccd):
1314 self.log.info("Applying linearizer.")
1315 linearizer.applyLinearity(image=ccdExposure.getMaskedImage().getImage(),
1316 detector=ccd, log=self.log)
1318 if self.config.doCrosstalk and not self.config.doCrosstalkBeforeAssemble:
1319 self.log.info("Applying crosstalk correction.")
1320 self.crosstalk.run(ccdExposure, crosstalkSources=crosstalkSources, isTrimmed=True)
1321 self.debugView(ccdExposure, "doCrosstalk")
1323 # Masking block. Optionally mask known defects, NAN pixels, widen trails, and do
1324 # anything else the camera needs. Saturated and suspect pixels have already been masked.
1325 if self.config.doDefect:
1326 self.log.info("Masking defects.")
1327 self.maskDefect(ccdExposure, defects)
1329 if self.config.numEdgeSuspect > 0:
1330 self.log.info("Masking edges as SUSPECT.")
1331 self.maskEdges(ccdExposure, numEdgePixels=self.config.numEdgeSuspect,
1332 maskPlane="SUSPECT")
1334 if self.config.doNanMasking:
1335 self.log.info("Masking NAN value pixels.")
1336 self.maskNan(ccdExposure)
1338 if self.config.doWidenSaturationTrails:
1339 self.log.info("Widening saturation trails.")
1340 isrFunctions.widenSaturationTrails(ccdExposure.getMaskedImage().getMask())
1342 if self.config.doCameraSpecificMasking:
1343 self.log.info("Masking regions for camera specific reasons.")
1344 self.masking.run(ccdExposure)
1346 if self.config.doBrighterFatter:
1347 # We need to apply flats and darks before we can interpolate, and we
1348 # need to interpolate before we do B-F, but we do B-F without the
1349 # flats and darks applied so we can work in units of electrons or holes.
1350 # This context manager applies and then removes the darks and flats.
1351 #
1352 # We also do not want to interpolate values here, so operate on temporary
1353 # images so we can apply only the BF-correction and roll back the
1354 # interpolation.
1355 interpExp = ccdExposure.clone()
1356 with self.flatContext(interpExp, flat, dark):
1357 isrFunctions.interpolateFromMask(
1358 maskedImage=interpExp.getMaskedImage(),
1359 fwhm=self.config.fwhm,
1360 growSaturatedFootprints=self.config.growSaturationFootprintSize,
1361 maskNameList=self.config.maskListToInterpolate
1362 )
1363 bfExp = interpExp.clone()
1365 self.log.info("Applying brighter fatter correction using kernel type %s / gains %s.",
1366 type(bfKernel), type(bfGains))
1367 bfResults = isrFunctions.brighterFatterCorrection(bfExp, bfKernel,
1368 self.config.brighterFatterMaxIter,
1369 self.config.brighterFatterThreshold,
1370 self.config.brighterFatterApplyGain,
1371 bfGains)
1372 if bfResults[1] == self.config.brighterFatterMaxIter:
1373 self.log.warn("Brighter fatter correction did not converge, final difference %f.",
1374 bfResults[0])
1375 else:
1376 self.log.info("Finished brighter fatter correction in %d iterations.",
1377 bfResults[1])
1378 image = ccdExposure.getMaskedImage().getImage()
1379 bfCorr = bfExp.getMaskedImage().getImage()
1380 bfCorr -= interpExp.getMaskedImage().getImage()
1381 image += bfCorr
1383 # Applying the brighter-fatter correction applies a
1384 # convolution to the science image. At the edges this
1385 # convolution may not have sufficient valid pixels to
1386 # produce a valid correction. Mark pixels within the size
1387 # of the brighter-fatter kernel as EDGE to warn of this
1388 # fact.
1389 self.log.info("Ensuring image edges are masked as SUSPECT to the brighter-fatter kernel size.")
1390 self.maskEdges(ccdExposure, numEdgePixels=numpy.max(bfKernel.shape) // 2,
1391 maskPlane="EDGE")
1393 if self.config.brighterFatterMaskGrowSize > 0:
1394 self.log.info("Growing masks to account for brighter-fatter kernel convolution.")
1395 for maskPlane in self.config.maskListToInterpolate:
1396 isrFunctions.growMasks(ccdExposure.getMask(),
1397 radius=self.config.brighterFatterMaskGrowSize,
1398 maskNameList=maskPlane,
1399 maskValue=maskPlane)
1401 self.debugView(ccdExposure, "doBrighterFatter")
1403 if self.config.doDark:
1404 self.log.info("Applying dark correction.")
1405 self.darkCorrection(ccdExposure, dark)
1406 self.debugView(ccdExposure, "doDark")
1408 if self.config.doFringe and not self.config.fringeAfterFlat:
1409 self.log.info("Applying fringe correction before flat.")
1410 self.fringe.run(ccdExposure, **fringes.getDict())
1411 self.debugView(ccdExposure, "doFringe")
1413 if self.config.doStrayLight and self.strayLight.check(ccdExposure):
1414 self.log.info("Checking strayLight correction.")
1415 self.strayLight.run(ccdExposure, strayLightData)
1416 self.debugView(ccdExposure, "doStrayLight")
1418 if self.config.doFlat:
1419 self.log.info("Applying flat correction.")
1420 self.flatCorrection(ccdExposure, flat)
1421 self.debugView(ccdExposure, "doFlat")
1423 if self.config.doApplyGains:
1424 self.log.info("Applying gain correction instead of flat.")
1425 isrFunctions.applyGains(ccdExposure, self.config.normalizeGains)
1427 if self.config.doFringe and self.config.fringeAfterFlat:
1428 self.log.info("Applying fringe correction after flat.")
1429 self.fringe.run(ccdExposure, **fringes.getDict())
1431 if self.config.doVignette:
1432 self.log.info("Constructing Vignette polygon.")
1433 self.vignettePolygon = self.vignette.run(ccdExposure)
1435 if self.config.vignette.doWriteVignettePolygon:
1436 self.setValidPolygonIntersect(ccdExposure, self.vignettePolygon)
1438 if self.config.doAttachTransmissionCurve:
1439 self.log.info("Adding transmission curves.")
1440 isrFunctions.attachTransmissionCurve(ccdExposure, opticsTransmission=opticsTransmission,
1441 filterTransmission=filterTransmission,
1442 sensorTransmission=sensorTransmission,
1443 atmosphereTransmission=atmosphereTransmission)
1445 flattenedThumb = None
1446 if self.config.qa.doThumbnailFlattened:
1447 flattenedThumb = isrQa.makeThumbnail(ccdExposure, isrQaConfig=self.config.qa)
1449 if self.config.doIlluminationCorrection and filterName in self.config.illumFilters:
1450 self.log.info("Performing illumination correction.")
1451 isrFunctions.illuminationCorrection(ccdExposure.getMaskedImage(),
1452 illumMaskedImage, illumScale=self.config.illumScale,
1453 trimToFit=self.config.doTrimToMatchCalib)
1455 preInterpExp = None
1456 if self.config.doSaveInterpPixels:
1457 preInterpExp = ccdExposure.clone()
1459 # Reset and interpolate bad pixels.
1460 #
1461 # Large contiguous bad regions (which should have the BAD mask
1462 # bit set) should have their values set to the image median.
1463 # This group should include defects and bad amplifiers. As the
1464 # area covered by these defects are large, there's little
1465 # reason to expect that interpolation would provide a more
1466 # useful value.
1467 #
1468 # Smaller defects can be safely interpolated after the larger
1469 # regions have had their pixel values reset. This ensures
1470 # that the remaining defects adjacent to bad amplifiers (as an
1471 # example) do not attempt to interpolate extreme values.
1472 if self.config.doSetBadRegions:
1473 badPixelCount, badPixelValue = isrFunctions.setBadRegions(ccdExposure)
1474 if badPixelCount > 0:
1475 self.log.info("Set %d BAD pixels to %f.", badPixelCount, badPixelValue)
1477 if self.config.doInterpolate:
1478 self.log.info("Interpolating masked pixels.")
1479 isrFunctions.interpolateFromMask(
1480 maskedImage=ccdExposure.getMaskedImage(),
1481 fwhm=self.config.fwhm,
1482 growSaturatedFootprints=self.config.growSaturationFootprintSize,
1483 maskNameList=list(self.config.maskListToInterpolate)
1484 )
1486 self.roughZeroPoint(ccdExposure)
1488 if self.config.doMeasureBackground:
1489 self.log.info("Measuring background level.")
1490 self.measureBackground(ccdExposure, self.config.qa)
1492 if self.config.qa is not None and self.config.qa.saveStats is True:
1493 for amp in ccd:
1494 ampExposure = ccdExposure.Factory(ccdExposure, amp.getBBox())
1495 qaStats = afwMath.makeStatistics(ampExposure.getImage(),
1496 afwMath.MEDIAN | afwMath.STDEVCLIP)
1497 self.metadata.set("ISR BACKGROUND {} MEDIAN".format(amp.getName()),
1498 qaStats.getValue(afwMath.MEDIAN))
1499 self.metadata.set("ISR BACKGROUND {} STDEV".format(amp.getName()),
1500 qaStats.getValue(afwMath.STDEVCLIP))
1501 self.log.debug(" Background stats for amplifer %s: %f +/- %f",
1502 amp.getName(), qaStats.getValue(afwMath.MEDIAN),
1503 qaStats.getValue(afwMath.STDEVCLIP))
1505 self.debugView(ccdExposure, "postISRCCD")
1507 return pipeBase.Struct(
1508 exposure=ccdExposure,
1509 ossThumb=ossThumb,
1510 flattenedThumb=flattenedThumb,
1512 preInterpolatedExposure=preInterpExp,
1513 outputExposure=ccdExposure,
1514 outputOssThumbnail=ossThumb,
1515 outputFlattenedThumbnail=flattenedThumb,
1516 )
1518 @pipeBase.timeMethod
1519 def runDataRef(self, sensorRef):
1520 """Perform instrument signature removal on a ButlerDataRef of a Sensor.
1522 This method contains the `CmdLineTask` interface to the ISR
1523 processing. All IO is handled here, freeing the `run()` method
1524 to manage only pixel-level calculations. The steps performed
1525 are:
1526 - Read in necessary detrending/isr/calibration data.
1527 - Process raw exposure in `run()`.
1528 - Persist the ISR-corrected exposure as "postISRCCD" if
1529 config.doWrite=True.
1531 Parameters
1532 ----------
1533 sensorRef : `daf.persistence.butlerSubset.ButlerDataRef`
1534 DataRef of the detector data to be processed
1536 Returns
1537 -------
1538 result : `lsst.pipe.base.Struct`
1539 Result struct with component:
1540 - ``exposure`` : `afw.image.Exposure`
1541 The fully ISR corrected exposure.
1543 Raises
1544 ------
1545 RuntimeError
1546 Raised if a configuration option is set to True, but the
1547 required calibration data does not exist.
1549 """
1550 self.log.info("Performing ISR on sensor %s.", sensorRef.dataId)
1552 ccdExposure = sensorRef.get(self.config.datasetType)
1554 camera = sensorRef.get("camera")
1555 isrData = self.readIsrData(sensorRef, ccdExposure)
1557 result = self.run(ccdExposure, camera=camera, **isrData.getDict())
1559 if self.config.doWrite:
1560 sensorRef.put(result.exposure, "postISRCCD")
1561 if result.preInterpolatedExposure is not None:
1562 sensorRef.put(result.preInterpolatedExposure, "postISRCCD_uninterpolated")
1563 if result.ossThumb is not None:
1564 isrQa.writeThumbnail(sensorRef, result.ossThumb, "ossThumb")
1565 if result.flattenedThumb is not None:
1566 isrQa.writeThumbnail(sensorRef, result.flattenedThumb, "flattenedThumb")
1568 return result
1570 def getIsrExposure(self, dataRef, datasetType, dateObs=None, immediate=True):
1571 """!Retrieve a calibration dataset for removing instrument signature.
1573 Parameters
1574 ----------
1576 dataRef : `daf.persistence.butlerSubset.ButlerDataRef`
1577 DataRef of the detector data to find calibration datasets
1578 for.
1579 datasetType : `str`
1580 Type of dataset to retrieve (e.g. 'bias', 'flat', etc).
1581 dateObs : `str`, optional
1582 Date of the observation. Used to correct butler failures
1583 when using fallback filters.
1584 immediate : `Bool`
1585 If True, disable butler proxies to enable error handling
1586 within this routine.
1588 Returns
1589 -------
1590 exposure : `lsst.afw.image.Exposure`
1591 Requested calibration frame.
1593 Raises
1594 ------
1595 RuntimeError
1596 Raised if no matching calibration frame can be found.
1597 """
1598 try:
1599 exp = dataRef.get(datasetType, immediate=immediate)
1600 except Exception as exc1:
1601 if not self.config.fallbackFilterName:
1602 raise RuntimeError("Unable to retrieve %s for %s: %s." % (datasetType, dataRef.dataId, exc1))
1603 try:
1604 if self.config.useFallbackDate and dateObs:
1605 exp = dataRef.get(datasetType, filter=self.config.fallbackFilterName,
1606 dateObs=dateObs, immediate=immediate)
1607 else:
1608 exp = dataRef.get(datasetType, filter=self.config.fallbackFilterName, immediate=immediate)
1609 except Exception as exc2:
1610 raise RuntimeError("Unable to retrieve %s for %s, even with fallback filter %s: %s AND %s." %
1611 (datasetType, dataRef.dataId, self.config.fallbackFilterName, exc1, exc2))
1612 self.log.warn("Using fallback calibration from filter %s.", self.config.fallbackFilterName)
1614 if self.config.doAssembleIsrExposures:
1615 exp = self.assembleCcd.assembleCcd(exp)
1616 return exp
1618 def ensureExposure(self, inputExp, camera, detectorNum):
1619 """Ensure that the data returned by Butler is a fully constructed exposure.
1621 ISR requires exposure-level image data for historical reasons, so if we did
1622 not recieve that from Butler, construct it from what we have, modifying the
1623 input in place.
1625 Parameters
1626 ----------
1627 inputExp : `lsst.afw.image.Exposure`, `lsst.afw.image.DecoratedImageU`, or
1628 `lsst.afw.image.ImageF`
1629 The input data structure obtained from Butler.
1630 camera : `lsst.afw.cameraGeom.camera`
1631 The camera associated with the image. Used to find the appropriate
1632 detector.
1633 detectorNum : `int`
1634 The detector this exposure should match.
1636 Returns
1637 -------
1638 inputExp : `lsst.afw.image.Exposure`
1639 The re-constructed exposure, with appropriate detector parameters.
1641 Raises
1642 ------
1643 TypeError
1644 Raised if the input data cannot be used to construct an exposure.
1645 """
1646 if isinstance(inputExp, afwImage.DecoratedImageU):
1647 inputExp = afwImage.makeExposure(afwImage.makeMaskedImage(inputExp))
1648 elif isinstance(inputExp, afwImage.ImageF):
1649 inputExp = afwImage.makeExposure(afwImage.makeMaskedImage(inputExp))
1650 elif isinstance(inputExp, afwImage.MaskedImageF):
1651 inputExp = afwImage.makeExposure(inputExp)
1652 elif isinstance(inputExp, afwImage.Exposure):
1653 pass
1654 elif inputExp is None:
1655 # Assume this will be caught by the setup if it is a problem.
1656 return inputExp
1657 else:
1658 raise TypeError("Input Exposure is not known type in isrTask.ensureExposure: %s." %
1659 (type(inputExp), ))
1661 if inputExp.getDetector() is None:
1662 inputExp.setDetector(camera[detectorNum])
1664 return inputExp
1666 def convertIntToFloat(self, exposure):
1667 """Convert exposure image from uint16 to float.
1669 If the exposure does not need to be converted, the input is
1670 immediately returned. For exposures that are converted to use
1671 floating point pixels, the variance is set to unity and the
1672 mask to zero.
1674 Parameters
1675 ----------
1676 exposure : `lsst.afw.image.Exposure`
1677 The raw exposure to be converted.
1679 Returns
1680 -------
1681 newexposure : `lsst.afw.image.Exposure`
1682 The input ``exposure``, converted to floating point pixels.
1684 Raises
1685 ------
1686 RuntimeError
1687 Raised if the exposure type cannot be converted to float.
1689 """
1690 if isinstance(exposure, afwImage.ExposureF):
1691 # Nothing to be done
1692 self.log.debug("Exposure already of type float.")
1693 return exposure
1694 if not hasattr(exposure, "convertF"):
1695 raise RuntimeError("Unable to convert exposure (%s) to float." % type(exposure))
1697 newexposure = exposure.convertF()
1698 newexposure.variance[:] = 1
1699 newexposure.mask[:] = 0x0
1701 return newexposure
1703 def maskAmplifier(self, ccdExposure, amp, defects):
1704 """Identify bad amplifiers, saturated and suspect pixels.
1706 Parameters
1707 ----------
1708 ccdExposure : `lsst.afw.image.Exposure`
1709 Input exposure to be masked.
1710 amp : `lsst.afw.table.AmpInfoCatalog`
1711 Catalog of parameters defining the amplifier on this
1712 exposure to mask.
1713 defects : `lsst.meas.algorithms.Defects`
1714 List of defects. Used to determine if the entire
1715 amplifier is bad.
1717 Returns
1718 -------
1719 badAmp : `Bool`
1720 If this is true, the entire amplifier area is covered by
1721 defects and unusable.
1723 """
1724 maskedImage = ccdExposure.getMaskedImage()
1726 badAmp = False
1728 # Check if entire amp region is defined as a defect (need to use amp.getBBox() for correct
1729 # comparison with current defects definition.
1730 if defects is not None:
1731 badAmp = bool(sum([v.getBBox().contains(amp.getBBox()) for v in defects]))
1733 # In the case of a bad amp, we will set mask to "BAD" (here use amp.getRawBBox() for correct
1734 # association with pixels in current ccdExposure).
1735 if badAmp:
1736 dataView = afwImage.MaskedImageF(maskedImage, amp.getRawBBox(),
1737 afwImage.PARENT)
1738 maskView = dataView.getMask()
1739 maskView |= maskView.getPlaneBitMask("BAD")
1740 del maskView
1741 return badAmp
1743 # Mask remaining defects after assembleCcd() to allow for defects that cross amplifier boundaries.
1744 # Saturation and suspect pixels can be masked now, though.
1745 limits = dict()
1746 if self.config.doSaturation and not badAmp:
1747 limits.update({self.config.saturatedMaskName: amp.getSaturation()})
1748 if self.config.doSuspect and not badAmp:
1749 limits.update({self.config.suspectMaskName: amp.getSuspectLevel()})
1750 if math.isfinite(self.config.saturation):
1751 limits.update({self.config.saturatedMaskName: self.config.saturation})
1753 for maskName, maskThreshold in limits.items():
1754 if not math.isnan(maskThreshold):
1755 dataView = maskedImage.Factory(maskedImage, amp.getRawBBox())
1756 isrFunctions.makeThresholdMask(
1757 maskedImage=dataView,
1758 threshold=maskThreshold,
1759 growFootprints=0,
1760 maskName=maskName
1761 )
1763 # Determine if we've fully masked this amplifier with SUSPECT and SAT pixels.
1764 maskView = afwImage.Mask(maskedImage.getMask(), amp.getRawDataBBox(),
1765 afwImage.PARENT)
1766 maskVal = maskView.getPlaneBitMask([self.config.saturatedMaskName,
1767 self.config.suspectMaskName])
1768 if numpy.all(maskView.getArray() & maskVal > 0):
1769 badAmp = True
1770 maskView |= maskView.getPlaneBitMask("BAD")
1772 return badAmp
1774 def overscanCorrection(self, ccdExposure, amp):
1775 """Apply overscan correction in place.
1777 This method does initial pixel rejection of the overscan
1778 region. The overscan can also be optionally segmented to
1779 allow for discontinuous overscan responses to be fit
1780 separately. The actual overscan subtraction is performed by
1781 the `lsst.ip.isr.isrFunctions.overscanCorrection` function,
1782 which is called here after the amplifier is preprocessed.
1784 Parameters
1785 ----------
1786 ccdExposure : `lsst.afw.image.Exposure`
1787 Exposure to have overscan correction performed.
1788 amp : `lsst.afw.table.AmpInfoCatalog`
1789 The amplifier to consider while correcting the overscan.
1791 Returns
1792 -------
1793 overscanResults : `lsst.pipe.base.Struct`
1794 Result struct with components:
1795 - ``imageFit`` : scalar or `lsst.afw.image.Image`
1796 Value or fit subtracted from the amplifier image data.
1797 - ``overscanFit`` : scalar or `lsst.afw.image.Image`
1798 Value or fit subtracted from the overscan image data.
1799 - ``overscanImage`` : `lsst.afw.image.Image`
1800 Image of the overscan region with the overscan
1801 correction applied. This quantity is used to estimate
1802 the amplifier read noise empirically.
1804 Raises
1805 ------
1806 RuntimeError
1807 Raised if the ``amp`` does not contain raw pixel information.
1809 See Also
1810 --------
1811 lsst.ip.isr.isrFunctions.overscanCorrection
1812 """
1813 if not amp.getHasRawInfo():
1814 raise RuntimeError("This method must be executed on an amp with raw information.")
1816 if amp.getRawHorizontalOverscanBBox().isEmpty():
1817 self.log.info("ISR_OSCAN: No overscan region. Not performing overscan correction.")
1818 return None
1820 statControl = afwMath.StatisticsControl()
1821 statControl.setAndMask(ccdExposure.mask.getPlaneBitMask("SAT"))
1823 # Determine the bounding boxes
1824 dataBBox = amp.getRawDataBBox()
1825 oscanBBox = amp.getRawHorizontalOverscanBBox()
1826 dx0 = 0
1827 dx1 = 0
1829 prescanBBox = amp.getRawPrescanBBox()
1830 if (oscanBBox.getBeginX() > prescanBBox.getBeginX()): # amp is at the right
1831 dx0 += self.config.overscanNumLeadingColumnsToSkip
1832 dx1 -= self.config.overscanNumTrailingColumnsToSkip
1833 else:
1834 dx0 += self.config.overscanNumTrailingColumnsToSkip
1835 dx1 -= self.config.overscanNumLeadingColumnsToSkip
1837 # Determine if we need to work on subregions of the amplifier and overscan.
1838 imageBBoxes = []
1839 overscanBBoxes = []
1841 if ((self.config.overscanBiasJump and
1842 self.config.overscanBiasJumpLocation) and
1843 (ccdExposure.getMetadata().exists(self.config.overscanBiasJumpKeyword) and
1844 ccdExposure.getMetadata().getScalar(self.config.overscanBiasJumpKeyword) in
1845 self.config.overscanBiasJumpDevices)):
1846 if amp.getReadoutCorner() in (ReadoutCorner.LL, ReadoutCorner.LR):
1847 yLower = self.config.overscanBiasJumpLocation
1848 yUpper = dataBBox.getHeight() - yLower
1849 else:
1850 yUpper = self.config.overscanBiasJumpLocation
1851 yLower = dataBBox.getHeight() - yUpper
1853 imageBBoxes.append(lsst.geom.Box2I(dataBBox.getBegin(),
1854 lsst.geom.Extent2I(dataBBox.getWidth(), yLower)))
1855 overscanBBoxes.append(lsst.geom.Box2I(oscanBBox.getBegin() +
1856 lsst.geom.Extent2I(dx0, 0),
1857 lsst.geom.Extent2I(oscanBBox.getWidth() - dx0 + dx1,
1858 yLower)))
1860 imageBBoxes.append(lsst.geom.Box2I(dataBBox.getBegin() + lsst.geom.Extent2I(0, yLower),
1861 lsst.geom.Extent2I(dataBBox.getWidth(), yUpper)))
1862 overscanBBoxes.append(lsst.geom.Box2I(oscanBBox.getBegin() + lsst.geom.Extent2I(dx0, yLower),
1863 lsst.geom.Extent2I(oscanBBox.getWidth() - dx0 + dx1,
1864 yUpper)))
1865 else:
1866 imageBBoxes.append(lsst.geom.Box2I(dataBBox.getBegin(),
1867 lsst.geom.Extent2I(dataBBox.getWidth(), dataBBox.getHeight())))
1868 overscanBBoxes.append(lsst.geom.Box2I(oscanBBox.getBegin() + lsst.geom.Extent2I(dx0, 0),
1869 lsst.geom.Extent2I(oscanBBox.getWidth() - dx0 + dx1,
1870 oscanBBox.getHeight())))
1872 # Perform overscan correction on subregions, ensuring saturated pixels are masked.
1873 for imageBBox, overscanBBox in zip(imageBBoxes, overscanBBoxes):
1874 ampImage = ccdExposure.maskedImage[imageBBox]
1875 overscanImage = ccdExposure.maskedImage[overscanBBox]
1877 overscanArray = overscanImage.image.array
1878 median = numpy.ma.median(numpy.ma.masked_where(overscanImage.mask.array, overscanArray))
1879 bad = numpy.where(numpy.abs(overscanArray - median) > self.config.overscanMaxDev)
1880 overscanImage.mask.array[bad] = overscanImage.mask.getPlaneBitMask("SAT")
1882 statControl = afwMath.StatisticsControl()
1883 statControl.setAndMask(ccdExposure.mask.getPlaneBitMask("SAT"))
1885 overscanResults = isrFunctions.overscanCorrection(ampMaskedImage=ampImage,
1886 overscanImage=overscanImage,
1887 fitType=self.config.overscanFitType,
1888 order=self.config.overscanOrder,
1889 collapseRej=self.config.overscanNumSigmaClip,
1890 statControl=statControl,
1891 overscanIsInt=self.config.overscanIsInt
1892 )
1894 # Measure average overscan levels and record them in the metadata.
1895 levelStat = afwMath.MEDIAN
1896 sigmaStat = afwMath.STDEVCLIP
1898 sctrl = afwMath.StatisticsControl(self.config.qa.flatness.clipSigma,
1899 self.config.qa.flatness.nIter)
1900 metadata = ccdExposure.getMetadata()
1901 ampNum = amp.getName()
1902 if self.config.overscanFitType in ("MEDIAN", "MEAN", "MEANCLIP"):
1903 metadata.set("ISR_OSCAN_LEVEL%s" % ampNum, overscanResults.overscanFit)
1904 metadata.set("ISR_OSCAN_SIGMA%s" % ampNum, 0.0)
1905 else:
1906 stats = afwMath.makeStatistics(overscanResults.overscanFit, levelStat | sigmaStat, sctrl)
1907 metadata.set("ISR_OSCAN_LEVEL%s" % ampNum, stats.getValue(levelStat))
1908 metadata.set("ISR_OSCAN_SIGMA%s" % ampNum, stats.getValue(sigmaStat))
1910 return overscanResults
1912 def updateVariance(self, ampExposure, amp, overscanImage=None):
1913 """Set the variance plane using the amplifier gain and read noise
1915 The read noise is calculated from the ``overscanImage`` if the
1916 ``doEmpiricalReadNoise`` option is set in the configuration; otherwise
1917 the value from the amplifier data is used.
1919 Parameters
1920 ----------
1921 ampExposure : `lsst.afw.image.Exposure`
1922 Exposure to process.
1923 amp : `lsst.afw.table.AmpInfoRecord` or `FakeAmp`
1924 Amplifier detector data.
1925 overscanImage : `lsst.afw.image.MaskedImage`, optional.
1926 Image of overscan, required only for empirical read noise.
1928 See also
1929 --------
1930 lsst.ip.isr.isrFunctions.updateVariance
1931 """
1932 maskPlanes = [self.config.saturatedMaskName, self.config.suspectMaskName]
1933 gain = amp.getGain()
1935 if math.isnan(gain):
1936 gain = 1.0
1937 self.log.warn("Gain set to NAN! Updating to 1.0 to generate Poisson variance.")
1938 elif gain <= 0:
1939 patchedGain = 1.0
1940 self.log.warn("Gain for amp %s == %g <= 0; setting to %f.",
1941 amp.getName(), gain, patchedGain)
1942 gain = patchedGain
1944 if self.config.doEmpiricalReadNoise and overscanImage is None:
1945 self.log.info("Overscan is none for EmpiricalReadNoise.")
1947 if self.config.doEmpiricalReadNoise and overscanImage is not None:
1948 stats = afwMath.StatisticsControl()
1949 stats.setAndMask(overscanImage.mask.getPlaneBitMask(maskPlanes))
1950 readNoise = afwMath.makeStatistics(overscanImage, afwMath.STDEVCLIP, stats).getValue()
1951 self.log.info("Calculated empirical read noise for amp %s: %f.",
1952 amp.getName(), readNoise)
1953 else:
1954 readNoise = amp.getReadNoise()
1956 isrFunctions.updateVariance(
1957 maskedImage=ampExposure.getMaskedImage(),
1958 gain=gain,
1959 readNoise=readNoise,
1960 )
1962 def darkCorrection(self, exposure, darkExposure, invert=False):
1963 """!Apply dark correction in place.
1965 Parameters
1966 ----------
1967 exposure : `lsst.afw.image.Exposure`
1968 Exposure to process.
1969 darkExposure : `lsst.afw.image.Exposure`
1970 Dark exposure of the same size as ``exposure``.
1971 invert : `Bool`, optional
1972 If True, re-add the dark to an already corrected image.
1974 Raises
1975 ------
1976 RuntimeError
1977 Raised if either ``exposure`` or ``darkExposure`` do not
1978 have their dark time defined.
1980 See Also
1981 --------
1982 lsst.ip.isr.isrFunctions.darkCorrection
1983 """
1984 expScale = exposure.getInfo().getVisitInfo().getDarkTime()
1985 if math.isnan(expScale):
1986 raise RuntimeError("Exposure darktime is NAN.")
1987 if darkExposure.getInfo().getVisitInfo() is not None \
1988 and not math.isnan(darkExposure.getInfo().getVisitInfo().getDarkTime()):
1989 darkScale = darkExposure.getInfo().getVisitInfo().getDarkTime()
1990 else:
1991 # DM-17444: darkExposure.getInfo.getVisitInfo() is None
1992 # so getDarkTime() does not exist.
1993 self.log.warn("darkExposure.getInfo().getVisitInfo() does not exist. Using darkScale = 1.0.")
1994 darkScale = 1.0
1996 isrFunctions.darkCorrection(
1997 maskedImage=exposure.getMaskedImage(),
1998 darkMaskedImage=darkExposure.getMaskedImage(),
1999 expScale=expScale,
2000 darkScale=darkScale,
2001 invert=invert,
2002 trimToFit=self.config.doTrimToMatchCalib
2003 )
2005 def doLinearize(self, detector):
2006 """!Check if linearization is needed for the detector cameraGeom.
2008 Checks config.doLinearize and the linearity type of the first
2009 amplifier.
2011 Parameters
2012 ----------
2013 detector : `lsst.afw.cameraGeom.Detector`
2014 Detector to get linearity type from.
2016 Returns
2017 -------
2018 doLinearize : `Bool`
2019 If True, linearization should be performed.
2020 """
2021 return self.config.doLinearize and \
2022 detector.getAmplifiers()[0].getLinearityType() != NullLinearityType
2024 def flatCorrection(self, exposure, flatExposure, invert=False):
2025 """!Apply flat correction in place.
2027 Parameters
2028 ----------
2029 exposure : `lsst.afw.image.Exposure`
2030 Exposure to process.
2031 flatExposure : `lsst.afw.image.Exposure`
2032 Flat exposure of the same size as ``exposure``.
2033 invert : `Bool`, optional
2034 If True, unflatten an already flattened image.
2036 See Also
2037 --------
2038 lsst.ip.isr.isrFunctions.flatCorrection
2039 """
2040 isrFunctions.flatCorrection(
2041 maskedImage=exposure.getMaskedImage(),
2042 flatMaskedImage=flatExposure.getMaskedImage(),
2043 scalingType=self.config.flatScalingType,
2044 userScale=self.config.flatUserScale,
2045 invert=invert,
2046 trimToFit=self.config.doTrimToMatchCalib
2047 )
2049 def saturationDetection(self, exposure, amp):
2050 """!Detect saturated pixels and mask them using mask plane config.saturatedMaskName, in place.
2052 Parameters
2053 ----------
2054 exposure : `lsst.afw.image.Exposure`
2055 Exposure to process. Only the amplifier DataSec is processed.
2056 amp : `lsst.afw.table.AmpInfoCatalog`
2057 Amplifier detector data.
2059 See Also
2060 --------
2061 lsst.ip.isr.isrFunctions.makeThresholdMask
2062 """
2063 if not math.isnan(amp.getSaturation()):
2064 maskedImage = exposure.getMaskedImage()
2065 dataView = maskedImage.Factory(maskedImage, amp.getRawBBox())
2066 isrFunctions.makeThresholdMask(
2067 maskedImage=dataView,
2068 threshold=amp.getSaturation(),
2069 growFootprints=0,
2070 maskName=self.config.saturatedMaskName,
2071 )
2073 def saturationInterpolation(self, exposure):
2074 """!Interpolate over saturated pixels, in place.
2076 This method should be called after `saturationDetection`, to
2077 ensure that the saturated pixels have been identified in the
2078 SAT mask. It should also be called after `assembleCcd`, since
2079 saturated regions may cross amplifier boundaries.
2081 Parameters
2082 ----------
2083 exposure : `lsst.afw.image.Exposure`
2084 Exposure to process.
2086 See Also
2087 --------
2088 lsst.ip.isr.isrTask.saturationDetection
2089 lsst.ip.isr.isrFunctions.interpolateFromMask
2090 """
2091 isrFunctions.interpolateFromMask(
2092 maskedImage=exposure.getMaskedImage(),
2093 fwhm=self.config.fwhm,
2094 growSaturatedFootprints=self.config.growSaturationFootprintSize,
2095 maskNameList=list(self.config.saturatedMaskName),
2096 )
2098 def suspectDetection(self, exposure, amp):
2099 """!Detect suspect pixels and mask them using mask plane config.suspectMaskName, in place.
2101 Parameters
2102 ----------
2103 exposure : `lsst.afw.image.Exposure`
2104 Exposure to process. Only the amplifier DataSec is processed.
2105 amp : `lsst.afw.table.AmpInfoCatalog`
2106 Amplifier detector data.
2108 See Also
2109 --------
2110 lsst.ip.isr.isrFunctions.makeThresholdMask
2112 Notes
2113 -----
2114 Suspect pixels are pixels whose value is greater than amp.getSuspectLevel().
2115 This is intended to indicate pixels that may be affected by unknown systematics;
2116 for example if non-linearity corrections above a certain level are unstable
2117 then that would be a useful value for suspectLevel. A value of `nan` indicates
2118 that no such level exists and no pixels are to be masked as suspicious.
2119 """
2120 suspectLevel = amp.getSuspectLevel()
2121 if math.isnan(suspectLevel):
2122 return
2124 maskedImage = exposure.getMaskedImage()
2125 dataView = maskedImage.Factory(maskedImage, amp.getRawBBox())
2126 isrFunctions.makeThresholdMask(
2127 maskedImage=dataView,
2128 threshold=suspectLevel,
2129 growFootprints=0,
2130 maskName=self.config.suspectMaskName,
2131 )
2133 def maskDefect(self, exposure, defectBaseList):
2134 """!Mask defects using mask plane "BAD", in place.
2136 Parameters
2137 ----------
2138 exposure : `lsst.afw.image.Exposure`
2139 Exposure to process.
2140 defectBaseList : `lsst.meas.algorithms.Defects` or `list` of
2141 `lsst.afw.image.DefectBase`.
2142 List of defects to mask.
2144 Notes
2145 -----
2146 Call this after CCD assembly, since defects may cross amplifier boundaries.
2147 """
2148 maskedImage = exposure.getMaskedImage()
2149 if not isinstance(defectBaseList, Defects):
2150 # Promotes DefectBase to Defect
2151 defectList = Defects(defectBaseList)
2152 else:
2153 defectList = defectBaseList
2154 defectList.maskPixels(maskedImage, maskName="BAD")
2156 def maskEdges(self, exposure, numEdgePixels=0, maskPlane="SUSPECT"):
2157 """!Mask edge pixels with applicable mask plane.
2159 Parameters
2160 ----------
2161 exposure : `lsst.afw.image.Exposure`
2162 Exposure to process.
2163 numEdgePixels : `int`, optional
2164 Number of edge pixels to mask.
2165 maskPlane : `str`, optional
2166 Mask plane name to use.
2167 """
2168 maskedImage = exposure.getMaskedImage()
2169 maskBitMask = maskedImage.getMask().getPlaneBitMask(maskPlane)
2171 if numEdgePixels > 0:
2172 goodBBox = maskedImage.getBBox()
2173 # This makes a bbox numEdgeSuspect pixels smaller than the image on each side
2174 goodBBox.grow(-numEdgePixels)
2175 # Mask pixels outside goodBBox
2176 SourceDetectionTask.setEdgeBits(
2177 maskedImage,
2178 goodBBox,
2179 maskBitMask
2180 )
2182 def maskAndInterpolateDefects(self, exposure, defectBaseList):
2183 """Mask and interpolate defects using mask plane "BAD", in place.
2185 Parameters
2186 ----------
2187 exposure : `lsst.afw.image.Exposure`
2188 Exposure to process.
2189 defectBaseList : `lsst.meas.algorithms.Defects` or `list` of
2190 `lsst.afw.image.DefectBase`.
2191 List of defects to mask and interpolate.
2193 See Also
2194 --------
2195 lsst.ip.isr.isrTask.maskDefect()
2196 """
2197 self.maskDefect(exposure, defectBaseList)
2198 self.maskEdges(exposure, numEdgePixels=self.config.numEdgeSuspect,
2199 maskPlane="SUSPECT")
2200 isrFunctions.interpolateFromMask(
2201 maskedImage=exposure.getMaskedImage(),
2202 fwhm=self.config.fwhm,
2203 growSaturatedFootprints=0,
2204 maskNameList=["BAD"],
2205 )
2207 def maskNan(self, exposure):
2208 """Mask NaNs using mask plane "UNMASKEDNAN", in place.
2210 Parameters
2211 ----------
2212 exposure : `lsst.afw.image.Exposure`
2213 Exposure to process.
2215 Notes
2216 -----
2217 We mask over all NaNs, including those that are masked with
2218 other bits (because those may or may not be interpolated over
2219 later, and we want to remove all NaNs). Despite this
2220 behaviour, the "UNMASKEDNAN" mask plane is used to preserve
2221 the historical name.
2222 """
2223 maskedImage = exposure.getMaskedImage()
2225 # Find and mask NaNs
2226 maskedImage.getMask().addMaskPlane("UNMASKEDNAN")
2227 maskVal = maskedImage.getMask().getPlaneBitMask("UNMASKEDNAN")
2228 numNans = maskNans(maskedImage, maskVal)
2229 self.metadata.set("NUMNANS", numNans)
2230 if numNans > 0:
2231 self.log.warn("There were %d unmasked NaNs.", numNans)
2233 def maskAndInterpolateNan(self, exposure):
2234 """"Mask and interpolate NaNs using mask plane "UNMASKEDNAN", in place.
2236 Parameters
2237 ----------
2238 exposure : `lsst.afw.image.Exposure`
2239 Exposure to process.
2241 See Also
2242 --------
2243 lsst.ip.isr.isrTask.maskNan()
2244 """
2245 self.maskNan(exposure)
2246 isrFunctions.interpolateFromMask(
2247 maskedImage=exposure.getMaskedImage(),
2248 fwhm=self.config.fwhm,
2249 growSaturatedFootprints=0,
2250 maskNameList=["UNMASKEDNAN"],
2251 )
2253 def measureBackground(self, exposure, IsrQaConfig=None):
2254 """Measure the image background in subgrids, for quality control purposes.
2256 Parameters
2257 ----------
2258 exposure : `lsst.afw.image.Exposure`
2259 Exposure to process.
2260 IsrQaConfig : `lsst.ip.isr.isrQa.IsrQaConfig`
2261 Configuration object containing parameters on which background
2262 statistics and subgrids to use.
2263 """
2264 if IsrQaConfig is not None:
2265 statsControl = afwMath.StatisticsControl(IsrQaConfig.flatness.clipSigma,
2266 IsrQaConfig.flatness.nIter)
2267 maskVal = exposure.getMaskedImage().getMask().getPlaneBitMask(["BAD", "SAT", "DETECTED"])
2268 statsControl.setAndMask(maskVal)
2269 maskedImage = exposure.getMaskedImage()
2270 stats = afwMath.makeStatistics(maskedImage, afwMath.MEDIAN | afwMath.STDEVCLIP, statsControl)
2271 skyLevel = stats.getValue(afwMath.MEDIAN)
2272 skySigma = stats.getValue(afwMath.STDEVCLIP)
2273 self.log.info("Flattened sky level: %f +/- %f.", skyLevel, skySigma)
2274 metadata = exposure.getMetadata()
2275 metadata.set('SKYLEVEL', skyLevel)
2276 metadata.set('SKYSIGMA', skySigma)
2278 # calcluating flatlevel over the subgrids
2279 stat = afwMath.MEANCLIP if IsrQaConfig.flatness.doClip else afwMath.MEAN
2280 meshXHalf = int(IsrQaConfig.flatness.meshX/2.)
2281 meshYHalf = int(IsrQaConfig.flatness.meshY/2.)
2282 nX = int((exposure.getWidth() + meshXHalf) / IsrQaConfig.flatness.meshX)
2283 nY = int((exposure.getHeight() + meshYHalf) / IsrQaConfig.flatness.meshY)
2284 skyLevels = numpy.zeros((nX, nY))
2286 for j in range(nY):
2287 yc = meshYHalf + j * IsrQaConfig.flatness.meshY
2288 for i in range(nX):
2289 xc = meshXHalf + i * IsrQaConfig.flatness.meshX
2291 xLLC = xc - meshXHalf
2292 yLLC = yc - meshYHalf
2293 xURC = xc + meshXHalf - 1
2294 yURC = yc + meshYHalf - 1
2296 bbox = lsst.geom.Box2I(lsst.geom.Point2I(xLLC, yLLC), lsst.geom.Point2I(xURC, yURC))
2297 miMesh = maskedImage.Factory(exposure.getMaskedImage(), bbox, afwImage.LOCAL)
2299 skyLevels[i, j] = afwMath.makeStatistics(miMesh, stat, statsControl).getValue()
2301 good = numpy.where(numpy.isfinite(skyLevels))
2302 skyMedian = numpy.median(skyLevels[good])
2303 flatness = (skyLevels[good] - skyMedian) / skyMedian
2304 flatness_rms = numpy.std(flatness)
2305 flatness_pp = flatness.max() - flatness.min() if len(flatness) > 0 else numpy.nan
2307 self.log.info("Measuring sky levels in %dx%d grids: %f.", nX, nY, skyMedian)
2308 self.log.info("Sky flatness in %dx%d grids - pp: %f rms: %f.",
2309 nX, nY, flatness_pp, flatness_rms)
2311 metadata.set('FLATNESS_PP', float(flatness_pp))
2312 metadata.set('FLATNESS_RMS', float(flatness_rms))
2313 metadata.set('FLATNESS_NGRIDS', '%dx%d' % (nX, nY))
2314 metadata.set('FLATNESS_MESHX', IsrQaConfig.flatness.meshX)
2315 metadata.set('FLATNESS_MESHY', IsrQaConfig.flatness.meshY)
2317 def roughZeroPoint(self, exposure):
2318 """Set an approximate magnitude zero point for the exposure.
2320 Parameters
2321 ----------
2322 exposure : `lsst.afw.image.Exposure`
2323 Exposure to process.
2324 """
2325 filterName = afwImage.Filter(exposure.getFilter().getId()).getName() # Canonical name for filter
2326 if filterName in self.config.fluxMag0T1:
2327 fluxMag0 = self.config.fluxMag0T1[filterName]
2328 else:
2329 self.log.warn("No rough magnitude zero point set for filter %s.", filterName)
2330 fluxMag0 = self.config.defaultFluxMag0T1
2332 expTime = exposure.getInfo().getVisitInfo().getExposureTime()
2333 if not expTime > 0: # handle NaN as well as <= 0
2334 self.log.warn("Non-positive exposure time; skipping rough zero point.")
2335 return
2337 self.log.info("Setting rough magnitude zero point: %f", 2.5*math.log10(fluxMag0*expTime))
2338 exposure.setPhotoCalib(afwImage.makePhotoCalibFromCalibZeroPoint(fluxMag0*expTime, 0.0))
2340 def setValidPolygonIntersect(self, ccdExposure, fpPolygon):
2341 """!Set the valid polygon as the intersection of fpPolygon and the ccd corners.
2343 Parameters
2344 ----------
2345 ccdExposure : `lsst.afw.image.Exposure`
2346 Exposure to process.
2347 fpPolygon : `lsst.afw.geom.Polygon`
2348 Polygon in focal plane coordinates.
2349 """
2350 # Get ccd corners in focal plane coordinates
2351 ccd = ccdExposure.getDetector()
2352 fpCorners = ccd.getCorners(FOCAL_PLANE)
2353 ccdPolygon = Polygon(fpCorners)
2355 # Get intersection of ccd corners with fpPolygon
2356 intersect = ccdPolygon.intersectionSingle(fpPolygon)
2358 # Transform back to pixel positions and build new polygon
2359 ccdPoints = ccd.transform(intersect, FOCAL_PLANE, PIXELS)
2360 validPolygon = Polygon(ccdPoints)
2361 ccdExposure.getInfo().setValidPolygon(validPolygon)
2363 @contextmanager
2364 def flatContext(self, exp, flat, dark=None):
2365 """Context manager that applies and removes flats and darks,
2366 if the task is configured to apply them.
2368 Parameters
2369 ----------
2370 exp : `lsst.afw.image.Exposure`
2371 Exposure to process.
2372 flat : `lsst.afw.image.Exposure`
2373 Flat exposure the same size as ``exp``.
2374 dark : `lsst.afw.image.Exposure`, optional
2375 Dark exposure the same size as ``exp``.
2377 Yields
2378 ------
2379 exp : `lsst.afw.image.Exposure`
2380 The flat and dark corrected exposure.
2381 """
2382 if self.config.doDark and dark is not None:
2383 self.darkCorrection(exp, dark)
2384 if self.config.doFlat:
2385 self.flatCorrection(exp, flat)
2386 try:
2387 yield exp
2388 finally:
2389 if self.config.doFlat:
2390 self.flatCorrection(exp, flat, invert=True)
2391 if self.config.doDark and dark is not None:
2392 self.darkCorrection(exp, dark, invert=True)
2394 def debugView(self, exposure, stepname):
2395 """Utility function to examine ISR exposure at different stages.
2397 Parameters
2398 ----------
2399 exposure : `lsst.afw.image.Exposure`
2400 Exposure to view.
2401 stepname : `str`
2402 State of processing to view.
2403 """
2404 frame = getDebugFrame(self._display, stepname)
2405 if frame:
2406 display = getDisplay(frame)
2407 display.scale('asinh', 'zscale')
2408 display.mtv(exposure)
2409 prompt = "Press Enter to continue [c]... "
2410 while True:
2411 ans = input(prompt).lower()
2412 if ans in ("", "c",):
2413 break
2416class FakeAmp(object):
2417 """A Detector-like object that supports returning gain and saturation level
2419 This is used when the input exposure does not have a detector.
2421 Parameters
2422 ----------
2423 exposure : `lsst.afw.image.Exposure`
2424 Exposure to generate a fake amplifier for.
2425 config : `lsst.ip.isr.isrTaskConfig`
2426 Configuration to apply to the fake amplifier.
2427 """
2429 def __init__(self, exposure, config):
2430 self._bbox = exposure.getBBox(afwImage.LOCAL)
2431 self._RawHorizontalOverscanBBox = lsst.geom.Box2I()
2432 self._gain = config.gain
2433 self._readNoise = config.readNoise
2434 self._saturation = config.saturation
2436 def getBBox(self):
2437 return self._bbox
2439 def getRawBBox(self):
2440 return self._bbox
2442 def getHasRawInfo(self):
2443 return True # but see getRawHorizontalOverscanBBox()
2445 def getRawHorizontalOverscanBBox(self):
2446 return self._RawHorizontalOverscanBBox
2448 def getGain(self):
2449 return self._gain
2451 def getReadNoise(self):
2452 return self._readNoise
2454 def getSaturation(self):
2455 return self._saturation
2457 def getSuspectLevel(self):
2458 return float("NaN")
2461class RunIsrConfig(pexConfig.Config):
2462 isr = pexConfig.ConfigurableField(target=IsrTask, doc="Instrument signature removal")
2465class RunIsrTask(pipeBase.CmdLineTask):
2466 """Task to wrap the default IsrTask to allow it to be retargeted.
2468 The standard IsrTask can be called directly from a command line
2469 program, but doing so removes the ability of the task to be
2470 retargeted. As most cameras override some set of the IsrTask
2471 methods, this would remove those data-specific methods in the
2472 output post-ISR images. This wrapping class fixes the issue,
2473 allowing identical post-ISR images to be generated by both the
2474 processCcd and isrTask code.
2475 """
2476 ConfigClass = RunIsrConfig
2477 _DefaultName = "runIsr"
2479 def __init__(self, *args, **kwargs):
2480 super().__init__(*args, **kwargs)
2481 self.makeSubtask("isr")
2483 def runDataRef(self, dataRef):
2484 """
2485 Parameters
2486 ----------
2487 dataRef : `lsst.daf.persistence.ButlerDataRef`
2488 data reference of the detector data to be processed
2490 Returns
2491 -------
2492 result : `pipeBase.Struct`
2493 Result struct with component:
2495 - exposure : `lsst.afw.image.Exposure`
2496 Post-ISR processed exposure.
2497 """
2498 return self.isr.runDataRef(dataRef)