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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 .overscan import OverscanCorrectionTask
54from .straylight import StrayLightTask
55from .vignette import VignetteTask
58__all__ = ["IsrTask", "IsrTaskConfig", "RunIsrTask", "RunIsrConfig"]
61class IsrTaskConnections(pipeBase.PipelineTaskConnections,
62 dimensions={"instrument", "exposure", "detector"},
63 defaultTemplates={}):
64 ccdExposure = cT.Input(
65 name="raw",
66 doc="Input exposure to process.",
67 storageClass="Exposure",
68 dimensions=["instrument", "detector", "exposure"],
69 )
70 camera = cT.PrerequisiteInput(
71 name="camera",
72 storageClass="Camera",
73 doc="Input camera to construct complete exposures.",
74 dimensions=["instrument", "calibration_label"],
75 )
76 bias = cT.PrerequisiteInput(
77 name="bias",
78 doc="Input bias calibration.",
79 storageClass="ExposureF",
80 dimensions=["instrument", "calibration_label", "detector"],
81 )
82 dark = cT.PrerequisiteInput(
83 name='dark',
84 doc="Input dark calibration.",
85 storageClass="ExposureF",
86 dimensions=["instrument", "calibration_label", "detector"],
87 )
88 flat = cT.PrerequisiteInput(
89 name="flat",
90 doc="Input flat calibration.",
91 storageClass="ExposureF",
92 dimensions=["instrument", "physical_filter", "calibration_label", "detector"],
93 )
94 fringes = cT.PrerequisiteInput(
95 name="fringe",
96 doc="Input fringe calibration.",
97 storageClass="ExposureF",
98 dimensions=["instrument", "physical_filter", "calibration_label", "detector"],
99 )
100 strayLightData = cT.PrerequisiteInput(
101 name='yBackground',
102 doc="Input stray light calibration.",
103 storageClass="StrayLightData",
104 dimensions=["instrument", "physical_filter", "calibration_label", "detector"],
105 )
106 bfKernel = cT.PrerequisiteInput(
107 name='bfKernel',
108 doc="Input brighter-fatter kernel.",
109 storageClass="NumpyArray",
110 dimensions=["instrument", "calibration_label"],
111 )
112 newBFKernel = cT.PrerequisiteInput(
113 name='brighterFatterKernel',
114 doc="Newer complete kernel + gain solutions.",
115 storageClass="BrighterFatterKernel",
116 dimensions=["instrument", "calibration_label", "detector"],
117 )
118 defects = cT.PrerequisiteInput(
119 name='defects',
120 doc="Input defect tables.",
121 storageClass="Defects",
122 dimensions=["instrument", "calibration_label", "detector"],
123 )
124 opticsTransmission = cT.PrerequisiteInput(
125 name="transmission_optics",
126 storageClass="TransmissionCurve",
127 doc="Transmission curve due to the optics.",
128 dimensions=["instrument", "calibration_label"],
129 )
130 filterTransmission = cT.PrerequisiteInput(
131 name="transmission_filter",
132 storageClass="TransmissionCurve",
133 doc="Transmission curve due to the filter.",
134 dimensions=["instrument", "physical_filter", "calibration_label"],
135 )
136 sensorTransmission = cT.PrerequisiteInput(
137 name="transmission_sensor",
138 storageClass="TransmissionCurve",
139 doc="Transmission curve due to the sensor.",
140 dimensions=["instrument", "calibration_label", "detector"],
141 )
142 atmosphereTransmission = cT.PrerequisiteInput(
143 name="transmission_atmosphere",
144 storageClass="TransmissionCurve",
145 doc="Transmission curve due to the atmosphere.",
146 dimensions=["instrument"],
147 )
148 illumMaskedImage = cT.PrerequisiteInput(
149 name="illum",
150 doc="Input illumination correction.",
151 storageClass="MaskedImageF",
152 dimensions=["instrument", "physical_filter", "calibration_label", "detector"],
153 )
155 outputExposure = cT.Output(
156 name='postISRCCD',
157 doc="Output ISR processed exposure.",
158 storageClass="ExposureF",
159 dimensions=["instrument", "exposure", "detector"],
160 )
161 preInterpExposure = cT.Output(
162 name='preInterpISRCCD',
163 doc="Output ISR processed exposure, with pixels left uninterpolated.",
164 storageClass="ExposureF",
165 dimensions=["instrument", "exposure", "detector"],
166 )
167 outputOssThumbnail = cT.Output(
168 name="OssThumb",
169 doc="Output Overscan-subtracted thumbnail image.",
170 storageClass="Thumbnail",
171 dimensions=["instrument", "exposure", "detector"],
172 )
173 outputFlattenedThumbnail = cT.Output(
174 name="FlattenedThumb",
175 doc="Output flat-corrected thumbnail image.",
176 storageClass="Thumbnail",
177 dimensions=["instrument", "exposure", "detector"],
178 )
180 def __init__(self, *, config=None):
181 super().__init__(config=config)
183 if config.doBias is not True:
184 self.prerequisiteInputs.discard("bias")
185 if config.doLinearize is not True:
186 self.prerequisiteInputs.discard("linearizer")
187 if config.doCrosstalk is not True:
188 self.prerequisiteInputs.discard("crosstalkSources")
189 if config.doBrighterFatter is not True:
190 self.prerequisiteInputs.discard("bfKernel")
191 self.prerequisiteInputs.discard("newBFKernel")
192 if config.doDefect is not True:
193 self.prerequisiteInputs.discard("defects")
194 if config.doDark is not True:
195 self.prerequisiteInputs.discard("dark")
196 if config.doFlat is not True:
197 self.prerequisiteInputs.discard("flat")
198 if config.doAttachTransmissionCurve is not True:
199 self.prerequisiteInputs.discard("opticsTransmission")
200 self.prerequisiteInputs.discard("filterTransmission")
201 self.prerequisiteInputs.discard("sensorTransmission")
202 self.prerequisiteInputs.discard("atmosphereTransmission")
203 if config.doUseOpticsTransmission is not True:
204 self.prerequisiteInputs.discard("opticsTransmission")
205 if config.doUseFilterTransmission is not True:
206 self.prerequisiteInputs.discard("filterTransmission")
207 if config.doUseSensorTransmission is not True:
208 self.prerequisiteInputs.discard("sensorTransmission")
209 if config.doUseAtmosphereTransmission is not True:
210 self.prerequisiteInputs.discard("atmosphereTransmission")
211 if config.doIlluminationCorrection is not True:
212 self.prerequisiteInputs.discard("illumMaskedImage")
214 if config.doWrite is not True:
215 self.outputs.discard("outputExposure")
216 self.outputs.discard("preInterpExposure")
217 self.outputs.discard("outputFlattenedThumbnail")
218 self.outputs.discard("outputOssThumbnail")
219 if config.doSaveInterpPixels is not True:
220 self.outputs.discard("preInterpExposure")
221 if config.qa.doThumbnailOss is not True:
222 self.outputs.discard("outputOssThumbnail")
223 if config.qa.doThumbnailFlattened is not True:
224 self.outputs.discard("outputFlattenedThumbnail")
227class IsrTaskConfig(pipeBase.PipelineTaskConfig,
228 pipelineConnections=IsrTaskConnections):
229 """Configuration parameters for IsrTask.
231 Items are grouped in the order in which they are executed by the task.
232 """
233 datasetType = pexConfig.Field(
234 dtype=str,
235 doc="Dataset type for input data; users will typically leave this alone, "
236 "but camera-specific ISR tasks will override it",
237 default="raw",
238 )
240 fallbackFilterName = pexConfig.Field(
241 dtype=str,
242 doc="Fallback default filter name for calibrations.",
243 optional=True
244 )
245 useFallbackDate = pexConfig.Field(
246 dtype=bool,
247 doc="Pass observation date when using fallback filter.",
248 default=False,
249 )
250 expectWcs = pexConfig.Field(
251 dtype=bool,
252 default=True,
253 doc="Expect input science images to have a WCS (set False for e.g. spectrographs)."
254 )
255 fwhm = pexConfig.Field(
256 dtype=float,
257 doc="FWHM of PSF in arcseconds.",
258 default=1.0,
259 )
260 qa = pexConfig.ConfigField(
261 dtype=isrQa.IsrQaConfig,
262 doc="QA related configuration options.",
263 )
265 # Image conversion configuration
266 doConvertIntToFloat = pexConfig.Field(
267 dtype=bool,
268 doc="Convert integer raw images to floating point values?",
269 default=True,
270 )
272 # Saturated pixel handling.
273 doSaturation = pexConfig.Field(
274 dtype=bool,
275 doc="Mask saturated pixels? NB: this is totally independent of the"
276 " interpolation option - this is ONLY setting the bits in the mask."
277 " To have them interpolated make sure doSaturationInterpolation=True",
278 default=True,
279 )
280 saturatedMaskName = pexConfig.Field(
281 dtype=str,
282 doc="Name of mask plane to use in saturation detection and interpolation",
283 default="SAT",
284 )
285 saturation = pexConfig.Field(
286 dtype=float,
287 doc="The saturation level to use if no Detector is present in the Exposure (ignored if NaN)",
288 default=float("NaN"),
289 )
290 growSaturationFootprintSize = pexConfig.Field(
291 dtype=int,
292 doc="Number of pixels by which to grow the saturation footprints",
293 default=1,
294 )
296 # Suspect pixel handling.
297 doSuspect = pexConfig.Field(
298 dtype=bool,
299 doc="Mask suspect pixels?",
300 default=False,
301 )
302 suspectMaskName = pexConfig.Field(
303 dtype=str,
304 doc="Name of mask plane to use for suspect pixels",
305 default="SUSPECT",
306 )
307 numEdgeSuspect = pexConfig.Field(
308 dtype=int,
309 doc="Number of edge pixels to be flagged as untrustworthy.",
310 default=0,
311 )
313 # Initial masking options.
314 doSetBadRegions = pexConfig.Field(
315 dtype=bool,
316 doc="Should we set the level of all BAD patches of the chip to the chip's average value?",
317 default=True,
318 )
319 badStatistic = pexConfig.ChoiceField(
320 dtype=str,
321 doc="How to estimate the average value for BAD regions.",
322 default='MEANCLIP',
323 allowed={
324 "MEANCLIP": "Correct using the (clipped) mean of good data",
325 "MEDIAN": "Correct using the median of the good data",
326 },
327 )
329 # Overscan subtraction configuration.
330 doOverscan = pexConfig.Field(
331 dtype=bool,
332 doc="Do overscan subtraction?",
333 default=True,
334 )
335 overscan = pexConfig.ConfigurableField(
336 target=OverscanCorrectionTask,
337 doc="Overscan subtraction task for image segments.",
338 )
340 overscanFitType = pexConfig.ChoiceField(
341 dtype=str,
342 doc="The method for fitting the overscan bias level.",
343 default='MEDIAN',
344 allowed={
345 "POLY": "Fit ordinary polynomial to the longest axis of the overscan region",
346 "CHEB": "Fit Chebyshev polynomial to the longest axis of the overscan region",
347 "LEG": "Fit Legendre polynomial to the longest axis of the overscan region",
348 "NATURAL_SPLINE": "Fit natural spline to the longest axis of the overscan region",
349 "CUBIC_SPLINE": "Fit cubic spline to the longest axis of the overscan region",
350 "AKIMA_SPLINE": "Fit Akima spline to the longest axis of the overscan region",
351 "MEAN": "Correct using the mean of the overscan region",
352 "MEANCLIP": "Correct using a clipped mean of the overscan region",
353 "MEDIAN": "Correct using the median of the overscan region",
354 "MEDIAN_PER_ROW": "Correct using the median per row of the overscan region",
355 },
356 deprecated=("Please configure overscan via the OverscanCorrectionConfig interface." +
357 " This option will no longer be used, and will be removed after v20.")
358 )
359 overscanOrder = pexConfig.Field(
360 dtype=int,
361 doc=("Order of polynomial or to fit if overscan fit type is a polynomial, " +
362 "or number of spline knots if overscan fit type is a spline."),
363 default=1,
364 deprecated=("Please configure overscan via the OverscanCorrectionConfig interface." +
365 " This option will no longer be used, and will be removed after v20.")
366 )
367 overscanNumSigmaClip = pexConfig.Field(
368 dtype=float,
369 doc="Rejection threshold (sigma) for collapsing overscan before fit",
370 default=3.0,
371 deprecated=("Please configure overscan via the OverscanCorrectionConfig interface." +
372 " This option will no longer be used, and will be removed after v20.")
373 )
374 overscanIsInt = pexConfig.Field(
375 dtype=bool,
376 doc="Treat overscan as an integer image for purposes of overscan.FitType=MEDIAN" +
377 " and overscan.FitType=MEDIAN_PER_ROW.",
378 default=True,
379 deprecated=("Please configure overscan via the OverscanCorrectionConfig interface." +
380 " This option will no longer be used, and will be removed after v20.")
381 )
382 # These options do not get deprecated, as they define how we slice up the image data.
383 overscanNumLeadingColumnsToSkip = pexConfig.Field(
384 dtype=int,
385 doc="Number of columns to skip in overscan, i.e. those closest to amplifier",
386 default=0,
387 )
388 overscanNumTrailingColumnsToSkip = pexConfig.Field(
389 dtype=int,
390 doc="Number of columns to skip in overscan, i.e. those farthest from amplifier",
391 default=0,
392 )
393 overscanMaxDev = pexConfig.Field( 393 ↛ exitline 393 didn't jump to the function exit
394 dtype=float,
395 doc="Maximum deviation from the median for overscan",
396 default=1000.0, check=lambda x: x > 0
397 )
398 overscanBiasJump = pexConfig.Field(
399 dtype=bool,
400 doc="Fit the overscan in a piecewise-fashion to correct for bias jumps?",
401 default=False,
402 )
403 overscanBiasJumpKeyword = pexConfig.Field(
404 dtype=str,
405 doc="Header keyword containing information about devices.",
406 default="NO_SUCH_KEY",
407 )
408 overscanBiasJumpDevices = pexConfig.ListField(
409 dtype=str,
410 doc="List of devices that need piecewise overscan correction.",
411 default=(),
412 )
413 overscanBiasJumpLocation = pexConfig.Field(
414 dtype=int,
415 doc="Location of bias jump along y-axis.",
416 default=0,
417 )
419 # Amplifier to CCD assembly configuration
420 doAssembleCcd = pexConfig.Field(
421 dtype=bool,
422 default=True,
423 doc="Assemble amp-level exposures into a ccd-level exposure?"
424 )
425 assembleCcd = pexConfig.ConfigurableField(
426 target=AssembleCcdTask,
427 doc="CCD assembly task",
428 )
430 # General calibration configuration.
431 doAssembleIsrExposures = pexConfig.Field(
432 dtype=bool,
433 default=False,
434 doc="Assemble amp-level calibration exposures into ccd-level exposure?"
435 )
436 doTrimToMatchCalib = pexConfig.Field(
437 dtype=bool,
438 default=False,
439 doc="Trim raw data to match calibration bounding boxes?"
440 )
442 # Bias subtraction.
443 doBias = pexConfig.Field(
444 dtype=bool,
445 doc="Apply bias frame correction?",
446 default=True,
447 )
448 biasDataProductName = pexConfig.Field(
449 dtype=str,
450 doc="Name of the bias data product",
451 default="bias",
452 )
454 # Variance construction
455 doVariance = pexConfig.Field(
456 dtype=bool,
457 doc="Calculate variance?",
458 default=True
459 )
460 gain = pexConfig.Field(
461 dtype=float,
462 doc="The gain to use if no Detector is present in the Exposure (ignored if NaN)",
463 default=float("NaN"),
464 )
465 readNoise = pexConfig.Field(
466 dtype=float,
467 doc="The read noise to use if no Detector is present in the Exposure",
468 default=0.0,
469 )
470 doEmpiricalReadNoise = pexConfig.Field(
471 dtype=bool,
472 default=False,
473 doc="Calculate empirical read noise instead of value from AmpInfo data?"
474 )
476 # Linearization.
477 doLinearize = pexConfig.Field(
478 dtype=bool,
479 doc="Correct for nonlinearity of the detector's response?",
480 default=True,
481 )
483 # Crosstalk.
484 doCrosstalk = pexConfig.Field(
485 dtype=bool,
486 doc="Apply intra-CCD crosstalk correction?",
487 default=False,
488 )
489 doCrosstalkBeforeAssemble = pexConfig.Field(
490 dtype=bool,
491 doc="Apply crosstalk correction before CCD assembly, and before trimming?",
492 default=False,
493 )
494 crosstalk = pexConfig.ConfigurableField(
495 target=CrosstalkTask,
496 doc="Intra-CCD crosstalk correction",
497 )
499 # Masking options.
500 doDefect = pexConfig.Field(
501 dtype=bool,
502 doc="Apply correction for CCD defects, e.g. hot pixels?",
503 default=True,
504 )
505 doNanMasking = pexConfig.Field(
506 dtype=bool,
507 doc="Mask NAN pixels?",
508 default=True,
509 )
510 doWidenSaturationTrails = pexConfig.Field(
511 dtype=bool,
512 doc="Widen bleed trails based on their width?",
513 default=True
514 )
516 # Brighter-Fatter correction.
517 doBrighterFatter = pexConfig.Field(
518 dtype=bool,
519 default=False,
520 doc="Apply the brighter fatter correction"
521 )
522 brighterFatterLevel = pexConfig.ChoiceField(
523 dtype=str,
524 default="DETECTOR",
525 doc="The level at which to correct for brighter-fatter.",
526 allowed={
527 "AMP": "Every amplifier treated separately.",
528 "DETECTOR": "One kernel per detector",
529 }
530 )
531 brighterFatterMaxIter = pexConfig.Field(
532 dtype=int,
533 default=10,
534 doc="Maximum number of iterations for the brighter fatter correction"
535 )
536 brighterFatterThreshold = pexConfig.Field(
537 dtype=float,
538 default=1000,
539 doc="Threshold used to stop iterating the brighter fatter correction. It is the "
540 " absolute value of the difference between the current corrected image and the one"
541 " from the previous iteration summed over all the pixels."
542 )
543 brighterFatterApplyGain = pexConfig.Field(
544 dtype=bool,
545 default=True,
546 doc="Should the gain be applied when applying the brighter fatter correction?"
547 )
548 brighterFatterMaskGrowSize = pexConfig.Field(
549 dtype=int,
550 default=0,
551 doc="Number of pixels to grow the masks listed in config.maskListToInterpolate "
552 " when brighter-fatter correction is applied."
553 )
555 # Dark subtraction.
556 doDark = pexConfig.Field(
557 dtype=bool,
558 doc="Apply dark frame correction?",
559 default=True,
560 )
561 darkDataProductName = pexConfig.Field(
562 dtype=str,
563 doc="Name of the dark data product",
564 default="dark",
565 )
567 # Camera-specific stray light removal.
568 doStrayLight = pexConfig.Field(
569 dtype=bool,
570 doc="Subtract stray light in the y-band (due to encoder LEDs)?",
571 default=False,
572 )
573 strayLight = pexConfig.ConfigurableField(
574 target=StrayLightTask,
575 doc="y-band stray light correction"
576 )
578 # Flat correction.
579 doFlat = pexConfig.Field(
580 dtype=bool,
581 doc="Apply flat field correction?",
582 default=True,
583 )
584 flatDataProductName = pexConfig.Field(
585 dtype=str,
586 doc="Name of the flat data product",
587 default="flat",
588 )
589 flatScalingType = pexConfig.ChoiceField(
590 dtype=str,
591 doc="The method for scaling the flat on the fly.",
592 default='USER',
593 allowed={
594 "USER": "Scale by flatUserScale",
595 "MEAN": "Scale by the inverse of the mean",
596 "MEDIAN": "Scale by the inverse of the median",
597 },
598 )
599 flatUserScale = pexConfig.Field(
600 dtype=float,
601 doc="If flatScalingType is 'USER' then scale flat by this amount; ignored otherwise",
602 default=1.0,
603 )
604 doTweakFlat = pexConfig.Field(
605 dtype=bool,
606 doc="Tweak flats to match observed amplifier ratios?",
607 default=False
608 )
610 # Amplifier normalization based on gains instead of using flats configuration.
611 doApplyGains = pexConfig.Field(
612 dtype=bool,
613 doc="Correct the amplifiers for their gains instead of applying flat correction",
614 default=False,
615 )
616 normalizeGains = pexConfig.Field(
617 dtype=bool,
618 doc="Normalize all the amplifiers in each CCD to have the same median value.",
619 default=False,
620 )
622 # Fringe correction.
623 doFringe = pexConfig.Field(
624 dtype=bool,
625 doc="Apply fringe correction?",
626 default=True,
627 )
628 fringe = pexConfig.ConfigurableField(
629 target=FringeTask,
630 doc="Fringe subtraction task",
631 )
632 fringeAfterFlat = pexConfig.Field(
633 dtype=bool,
634 doc="Do fringe subtraction after flat-fielding?",
635 default=True,
636 )
638 # Initial CCD-level background statistics options.
639 doMeasureBackground = pexConfig.Field(
640 dtype=bool,
641 doc="Measure the background level on the reduced image?",
642 default=False,
643 )
645 # Camera-specific masking configuration.
646 doCameraSpecificMasking = pexConfig.Field(
647 dtype=bool,
648 doc="Mask camera-specific bad regions?",
649 default=False,
650 )
651 masking = pexConfig.ConfigurableField(
652 target=MaskingTask,
653 doc="Masking task."
654 )
656 # Interpolation options.
658 doInterpolate = pexConfig.Field(
659 dtype=bool,
660 doc="Interpolate masked pixels?",
661 default=True,
662 )
663 doSaturationInterpolation = pexConfig.Field(
664 dtype=bool,
665 doc="Perform interpolation over pixels masked as saturated?"
666 " NB: This is independent of doSaturation; if that is False this plane"
667 " will likely be blank, resulting in a no-op here.",
668 default=True,
669 )
670 doNanInterpolation = pexConfig.Field(
671 dtype=bool,
672 doc="Perform interpolation over pixels masked as NaN?"
673 " NB: This is independent of doNanMasking; if that is False this plane"
674 " will likely be blank, resulting in a no-op here.",
675 default=True,
676 )
677 doNanInterpAfterFlat = pexConfig.Field(
678 dtype=bool,
679 doc=("If True, ensure we interpolate NaNs after flat-fielding, even if we "
680 "also have to interpolate them before flat-fielding."),
681 default=False,
682 )
683 maskListToInterpolate = pexConfig.ListField(
684 dtype=str,
685 doc="List of mask planes that should be interpolated.",
686 default=['SAT', 'BAD', 'UNMASKEDNAN'],
687 )
688 doSaveInterpPixels = pexConfig.Field(
689 dtype=bool,
690 doc="Save a copy of the pre-interpolated pixel values?",
691 default=False,
692 )
694 # Default photometric calibration options.
695 fluxMag0T1 = pexConfig.DictField(
696 keytype=str,
697 itemtype=float,
698 doc="The approximate flux of a zero-magnitude object in a one-second exposure, per filter.",
699 default=dict((f, pow(10.0, 0.4*m)) for f, m in (("Unknown", 28.0),
700 ))
701 )
702 defaultFluxMag0T1 = pexConfig.Field(
703 dtype=float,
704 doc="Default value for fluxMag0T1 (for an unrecognized filter).",
705 default=pow(10.0, 0.4*28.0)
706 )
708 # Vignette correction configuration.
709 doVignette = pexConfig.Field(
710 dtype=bool,
711 doc="Apply vignetting parameters?",
712 default=False,
713 )
714 vignette = pexConfig.ConfigurableField(
715 target=VignetteTask,
716 doc="Vignetting task.",
717 )
719 # Transmission curve configuration.
720 doAttachTransmissionCurve = pexConfig.Field(
721 dtype=bool,
722 default=False,
723 doc="Construct and attach a wavelength-dependent throughput curve for this CCD image?"
724 )
725 doUseOpticsTransmission = pexConfig.Field(
726 dtype=bool,
727 default=True,
728 doc="Load and use transmission_optics (if doAttachTransmissionCurve is True)?"
729 )
730 doUseFilterTransmission = pexConfig.Field(
731 dtype=bool,
732 default=True,
733 doc="Load and use transmission_filter (if doAttachTransmissionCurve is True)?"
734 )
735 doUseSensorTransmission = pexConfig.Field(
736 dtype=bool,
737 default=True,
738 doc="Load and use transmission_sensor (if doAttachTransmissionCurve is True)?"
739 )
740 doUseAtmosphereTransmission = pexConfig.Field(
741 dtype=bool,
742 default=True,
743 doc="Load and use transmission_atmosphere (if doAttachTransmissionCurve is True)?"
744 )
746 # Illumination correction.
747 doIlluminationCorrection = pexConfig.Field(
748 dtype=bool,
749 default=False,
750 doc="Perform illumination correction?"
751 )
752 illuminationCorrectionDataProductName = pexConfig.Field(
753 dtype=str,
754 doc="Name of the illumination correction data product.",
755 default="illumcor",
756 )
757 illumScale = pexConfig.Field(
758 dtype=float,
759 doc="Scale factor for the illumination correction.",
760 default=1.0,
761 )
762 illumFilters = pexConfig.ListField(
763 dtype=str,
764 default=[],
765 doc="Only perform illumination correction for these filters."
766 )
768 # Write the outputs to disk. If ISR is run as a subtask, this may not be needed.
769 doWrite = pexConfig.Field(
770 dtype=bool,
771 doc="Persist postISRCCD?",
772 default=True,
773 )
775 def validate(self):
776 super().validate()
777 if self.doFlat and self.doApplyGains:
778 raise ValueError("You may not specify both doFlat and doApplyGains")
779 if self.doSaturationInterpolation and "SAT" not in self.maskListToInterpolate:
780 self.config.maskListToInterpolate.append("SAT")
781 if self.doNanInterpolation and "UNMASKEDNAN" not in self.maskListToInterpolate:
782 self.config.maskListToInterpolate.append("UNMASKEDNAN")
785class IsrTask(pipeBase.PipelineTask, pipeBase.CmdLineTask):
786 """Apply common instrument signature correction algorithms to a raw frame.
788 The process for correcting imaging data is very similar from
789 camera to camera. This task provides a vanilla implementation of
790 doing these corrections, including the ability to turn certain
791 corrections off if they are not needed. The inputs to the primary
792 method, `run()`, are a raw exposure to be corrected and the
793 calibration data products. The raw input is a single chip sized
794 mosaic of all amps including overscans and other non-science
795 pixels. The method `runDataRef()` identifies and defines the
796 calibration data products, and is intended for use by a
797 `lsst.pipe.base.cmdLineTask.CmdLineTask` and takes as input only a
798 `daf.persistence.butlerSubset.ButlerDataRef`. This task may be
799 subclassed for different camera, although the most camera specific
800 methods have been split into subtasks that can be redirected
801 appropriately.
803 The __init__ method sets up the subtasks for ISR processing, using
804 the defaults from `lsst.ip.isr`.
806 Parameters
807 ----------
808 args : `list`
809 Positional arguments passed to the Task constructor. None used at this time.
810 kwargs : `dict`, optional
811 Keyword arguments passed on to the Task constructor. None used at this time.
812 """
813 ConfigClass = IsrTaskConfig
814 _DefaultName = "isr"
816 def __init__(self, **kwargs):
817 super().__init__(**kwargs)
818 self.makeSubtask("assembleCcd")
819 self.makeSubtask("crosstalk")
820 self.makeSubtask("strayLight")
821 self.makeSubtask("fringe")
822 self.makeSubtask("masking")
823 self.makeSubtask("overscan")
824 self.makeSubtask("vignette")
826 def runQuantum(self, butlerQC, inputRefs, outputRefs):
827 inputs = butlerQC.get(inputRefs)
829 try:
830 inputs['detectorNum'] = inputRefs.ccdExposure.dataId['detector']
831 except Exception as e:
832 raise ValueError("Failure to find valid detectorNum value for Dataset %s: %s." %
833 (inputRefs, e))
835 inputs['isGen3'] = True
837 detector = inputs['ccdExposure'].getDetector()
839 if self.doLinearize(detector) is True:
840 if 'linearizer' in inputs and isinstance(inputs['linearizer'], dict):
841 linearizer = linearize.Linearizer(detector=detector, log=self.log)
842 linearizer.fromYaml(inputs['linearizer'])
843 else:
844 linearizer = linearize.Linearizer(table=inputs.get('linearizer', None), detector=detector,
845 log=self.log)
846 inputs['linearizer'] = linearizer
848 if self.config.doDefect is True:
849 if "defects" in inputs and inputs['defects'] is not None:
850 # defects is loaded as a BaseCatalog with columns x0, y0, width, height.
851 # masking expects a list of defects defined by their bounding box
852 if not isinstance(inputs["defects"], Defects):
853 inputs["defects"] = Defects.fromTable(inputs["defects"])
855 # Load the correct style of brighter fatter kernel, and repack
856 # the information as a numpy array.
857 if self.config.doBrighterFatter:
858 brighterFatterKernel = inputs.pop('newBFKernel', None)
859 if brighterFatterKernel is None:
860 brighterFatterKernel = inputs.get('bfKernel', None)
862 if brighterFatterKernel is not None and not isinstance(brighterFatterKernel, numpy.ndarray):
863 detId = detector.getId()
864 inputs['bfGains'] = brighterFatterKernel.gain
865 # If the kernel is not an ndarray, it's the cp_pipe version
866 # so extract the kernel for this detector, or raise an error
867 if self.config.brighterFatterLevel == 'DETECTOR':
868 if brighterFatterKernel.detectorKernel:
869 inputs['bfKernel'] = brighterFatterKernel.detectorKernel[detId]
870 elif brighterFatterKernel.detectorKernelFromAmpKernels:
871 inputs['bfKernel'] = brighterFatterKernel.detectorKernelFromAmpKernels[detId]
872 else:
873 raise RuntimeError("Failed to extract kernel from new-style BF kernel.")
874 else:
875 # TODO DM-15631 for implementing this
876 raise NotImplementedError("Per-amplifier brighter-fatter correction not implemented")
878 # Broken: DM-17169
879 # ci_hsc does not use crosstalkSources, as it's intra-CCD CT only. This needs to be
880 # fixed for non-HSC cameras in the future.
881 # inputs['crosstalkSources'] = (self.crosstalk.prepCrosstalk(inputsIds['ccdExposure'])
882 # if self.config.doCrosstalk else None)
884 if self.config.doFringe is True and self.fringe.checkFilter(inputs['ccdExposure']):
885 expId = inputs['ccdExposure'].getInfo().getVisitInfo().getExposureId()
886 inputs['fringes'] = self.fringe.loadFringes(inputs['fringes'],
887 expId=expId,
888 assembler=self.assembleCcd
889 if self.config.doAssembleIsrExposures else None)
890 else:
891 inputs['fringes'] = pipeBase.Struct(fringes=None)
893 if self.config.doStrayLight is True and self.strayLight.checkFilter(inputs['ccdExposure']):
894 if 'strayLightData' not in inputs:
895 inputs['strayLightData'] = None
897 outputs = self.run(**inputs)
898 butlerQC.put(outputs, outputRefs)
900 def readIsrData(self, dataRef, rawExposure):
901 """!Retrieve necessary frames for instrument signature removal.
903 Pre-fetching all required ISR data products limits the IO
904 required by the ISR. Any conflict between the calibration data
905 available and that needed for ISR is also detected prior to
906 doing processing, allowing it to fail quickly.
908 Parameters
909 ----------
910 dataRef : `daf.persistence.butlerSubset.ButlerDataRef`
911 Butler reference of the detector data to be processed
912 rawExposure : `afw.image.Exposure`
913 The raw exposure that will later be corrected with the
914 retrieved calibration data; should not be modified in this
915 method.
917 Returns
918 -------
919 result : `lsst.pipe.base.Struct`
920 Result struct with components (which may be `None`):
921 - ``bias``: bias calibration frame (`afw.image.Exposure`)
922 - ``linearizer``: functor for linearization (`ip.isr.linearize.LinearizeBase`)
923 - ``crosstalkSources``: list of possible crosstalk sources (`list`)
924 - ``dark``: dark calibration frame (`afw.image.Exposure`)
925 - ``flat``: flat calibration frame (`afw.image.Exposure`)
926 - ``bfKernel``: Brighter-Fatter kernel (`numpy.ndarray`)
927 - ``defects``: list of defects (`lsst.meas.algorithms.Defects`)
928 - ``fringes``: `lsst.pipe.base.Struct` with components:
929 - ``fringes``: fringe calibration frame (`afw.image.Exposure`)
930 - ``seed``: random seed derived from the ccdExposureId for random
931 number generator (`uint32`).
932 - ``opticsTransmission``: `lsst.afw.image.TransmissionCurve`
933 A ``TransmissionCurve`` that represents the throughput of the optics,
934 to be evaluated in focal-plane coordinates.
935 - ``filterTransmission`` : `lsst.afw.image.TransmissionCurve`
936 A ``TransmissionCurve`` that represents the throughput of the filter
937 itself, to be evaluated in focal-plane coordinates.
938 - ``sensorTransmission`` : `lsst.afw.image.TransmissionCurve`
939 A ``TransmissionCurve`` that represents the throughput of the sensor
940 itself, to be evaluated in post-assembly trimmed detector coordinates.
941 - ``atmosphereTransmission`` : `lsst.afw.image.TransmissionCurve`
942 A ``TransmissionCurve`` that represents the throughput of the
943 atmosphere, assumed to be spatially constant.
944 - ``strayLightData`` : `object`
945 An opaque object containing calibration information for
946 stray-light correction. If `None`, no correction will be
947 performed.
948 - ``illumMaskedImage`` : illumination correction image (`lsst.afw.image.MaskedImage`)
950 Raises
951 ------
952 NotImplementedError :
953 Raised if a per-amplifier brighter-fatter kernel is requested by the configuration.
954 """
955 try:
956 dateObs = rawExposure.getInfo().getVisitInfo().getDate()
957 dateObs = dateObs.toPython().isoformat()
958 except RuntimeError:
959 self.log.warn("Unable to identify dateObs for rawExposure.")
960 dateObs = None
962 ccd = rawExposure.getDetector()
963 filterName = afwImage.Filter(rawExposure.getFilter().getId()).getName() # Canonical name for filter
964 rawExposure.mask.addMaskPlane("UNMASKEDNAN") # needed to match pre DM-15862 processing.
965 biasExposure = (self.getIsrExposure(dataRef, self.config.biasDataProductName)
966 if self.config.doBias else None)
967 # immediate=True required for functors and linearizers are functors; see ticket DM-6515
968 linearizer = (dataRef.get("linearizer", immediate=True)
969 if self.doLinearize(ccd) else None)
970 if linearizer is not None and not isinstance(linearizer, numpy.ndarray):
971 linearizer.log = self.log
972 if isinstance(linearizer, numpy.ndarray):
973 linearizer = linearize.Linearizer(table=linearizer, detector=ccd)
974 crosstalkSources = (self.crosstalk.prepCrosstalk(dataRef)
975 if self.config.doCrosstalk else None)
976 darkExposure = (self.getIsrExposure(dataRef, self.config.darkDataProductName)
977 if self.config.doDark else None)
978 flatExposure = (self.getIsrExposure(dataRef, self.config.flatDataProductName,
979 dateObs=dateObs)
980 if self.config.doFlat else None)
982 brighterFatterKernel = None
983 brighterFatterGains = None
984 if self.config.doBrighterFatter is True:
985 try:
986 # Use the new-style cp_pipe version of the kernel if it exists
987 # If using a new-style kernel, always use the self-consistent
988 # gains, i.e. the ones inside the kernel object itself
989 brighterFatterKernel = dataRef.get("brighterFatterKernel")
990 brighterFatterGains = brighterFatterKernel.gain
991 self.log.info("New style bright-fatter kernel (brighterFatterKernel) loaded")
992 except NoResults:
993 try: # Fall back to the old-style numpy-ndarray style kernel if necessary.
994 brighterFatterKernel = dataRef.get("bfKernel")
995 self.log.info("Old style bright-fatter kernel (np.array) loaded")
996 except NoResults:
997 brighterFatterKernel = None
998 if brighterFatterKernel is not None and not isinstance(brighterFatterKernel, numpy.ndarray):
999 # If the kernel is not an ndarray, it's the cp_pipe version
1000 # so extract the kernel for this detector, or raise an error
1001 if self.config.brighterFatterLevel == 'DETECTOR':
1002 if brighterFatterKernel.detectorKernel:
1003 brighterFatterKernel = brighterFatterKernel.detectorKernel[ccd.getId()]
1004 elif brighterFatterKernel.detectorKernelFromAmpKernels:
1005 brighterFatterKernel = brighterFatterKernel.detectorKernelFromAmpKernels[ccd.getId()]
1006 else:
1007 raise RuntimeError("Failed to extract kernel from new-style BF kernel.")
1008 else:
1009 # TODO DM-15631 for implementing this
1010 raise NotImplementedError("Per-amplifier brighter-fatter correction not implemented")
1012 defectList = (dataRef.get("defects")
1013 if self.config.doDefect else None)
1014 fringeStruct = (self.fringe.readFringes(dataRef, assembler=self.assembleCcd
1015 if self.config.doAssembleIsrExposures else None)
1016 if self.config.doFringe and self.fringe.checkFilter(rawExposure)
1017 else pipeBase.Struct(fringes=None))
1019 if self.config.doAttachTransmissionCurve:
1020 opticsTransmission = (dataRef.get("transmission_optics")
1021 if self.config.doUseOpticsTransmission else None)
1022 filterTransmission = (dataRef.get("transmission_filter")
1023 if self.config.doUseFilterTransmission else None)
1024 sensorTransmission = (dataRef.get("transmission_sensor")
1025 if self.config.doUseSensorTransmission else None)
1026 atmosphereTransmission = (dataRef.get("transmission_atmosphere")
1027 if self.config.doUseAtmosphereTransmission else None)
1028 else:
1029 opticsTransmission = None
1030 filterTransmission = None
1031 sensorTransmission = None
1032 atmosphereTransmission = None
1034 if self.config.doStrayLight:
1035 strayLightData = self.strayLight.readIsrData(dataRef, rawExposure)
1036 else:
1037 strayLightData = None
1039 illumMaskedImage = (self.getIsrExposure(dataRef,
1040 self.config.illuminationCorrectionDataProductName).getMaskedImage()
1041 if (self.config.doIlluminationCorrection and
1042 filterName in self.config.illumFilters)
1043 else None)
1045 # Struct should include only kwargs to run()
1046 return pipeBase.Struct(bias=biasExposure,
1047 linearizer=linearizer,
1048 crosstalkSources=crosstalkSources,
1049 dark=darkExposure,
1050 flat=flatExposure,
1051 bfKernel=brighterFatterKernel,
1052 bfGains=brighterFatterGains,
1053 defects=defectList,
1054 fringes=fringeStruct,
1055 opticsTransmission=opticsTransmission,
1056 filterTransmission=filterTransmission,
1057 sensorTransmission=sensorTransmission,
1058 atmosphereTransmission=atmosphereTransmission,
1059 strayLightData=strayLightData,
1060 illumMaskedImage=illumMaskedImage
1061 )
1063 @pipeBase.timeMethod
1064 def run(self, ccdExposure, camera=None, bias=None, linearizer=None, crosstalkSources=None,
1065 dark=None, flat=None, bfKernel=None, bfGains=None, defects=None,
1066 fringes=pipeBase.Struct(fringes=None), opticsTransmission=None, filterTransmission=None,
1067 sensorTransmission=None, atmosphereTransmission=None,
1068 detectorNum=None, strayLightData=None, illumMaskedImage=None,
1069 isGen3=False,
1070 ):
1071 """!Perform instrument signature removal on an exposure.
1073 Steps included in the ISR processing, in order performed, are:
1074 - saturation and suspect pixel masking
1075 - overscan subtraction
1076 - CCD assembly of individual amplifiers
1077 - bias subtraction
1078 - variance image construction
1079 - linearization of non-linear response
1080 - crosstalk masking
1081 - brighter-fatter correction
1082 - dark subtraction
1083 - fringe correction
1084 - stray light subtraction
1085 - flat correction
1086 - masking of known defects and camera specific features
1087 - vignette calculation
1088 - appending transmission curve and distortion model
1090 Parameters
1091 ----------
1092 ccdExposure : `lsst.afw.image.Exposure`
1093 The raw exposure that is to be run through ISR. The
1094 exposure is modified by this method.
1095 camera : `lsst.afw.cameraGeom.Camera`, optional
1096 The camera geometry for this exposure. Required if ``isGen3`` is
1097 `True` and one or more of ``ccdExposure``, ``bias``, ``dark``, or
1098 ``flat`` does not have an associated detector.
1099 bias : `lsst.afw.image.Exposure`, optional
1100 Bias calibration frame.
1101 linearizer : `lsst.ip.isr.linearize.LinearizeBase`, optional
1102 Functor for linearization.
1103 crosstalkSources : `list`, optional
1104 List of possible crosstalk sources.
1105 dark : `lsst.afw.image.Exposure`, optional
1106 Dark calibration frame.
1107 flat : `lsst.afw.image.Exposure`, optional
1108 Flat calibration frame.
1109 bfKernel : `numpy.ndarray`, optional
1110 Brighter-fatter kernel.
1111 bfGains : `dict` of `float`, optional
1112 Gains used to override the detector's nominal gains for the
1113 brighter-fatter correction. A dict keyed by amplifier name for
1114 the detector in question.
1115 defects : `lsst.meas.algorithms.Defects`, optional
1116 List of defects.
1117 fringes : `lsst.pipe.base.Struct`, optional
1118 Struct containing the fringe correction data, with
1119 elements:
1120 - ``fringes``: fringe calibration frame (`afw.image.Exposure`)
1121 - ``seed``: random seed derived from the ccdExposureId for random
1122 number generator (`uint32`)
1123 opticsTransmission: `lsst.afw.image.TransmissionCurve`, optional
1124 A ``TransmissionCurve`` that represents the throughput of the optics,
1125 to be evaluated in focal-plane coordinates.
1126 filterTransmission : `lsst.afw.image.TransmissionCurve`
1127 A ``TransmissionCurve`` that represents the throughput of the filter
1128 itself, to be evaluated in focal-plane coordinates.
1129 sensorTransmission : `lsst.afw.image.TransmissionCurve`
1130 A ``TransmissionCurve`` that represents the throughput of the sensor
1131 itself, to be evaluated in post-assembly trimmed detector coordinates.
1132 atmosphereTransmission : `lsst.afw.image.TransmissionCurve`
1133 A ``TransmissionCurve`` that represents the throughput of the
1134 atmosphere, assumed to be spatially constant.
1135 detectorNum : `int`, optional
1136 The integer number for the detector to process.
1137 isGen3 : bool, optional
1138 Flag this call to run() as using the Gen3 butler environment.
1139 strayLightData : `object`, optional
1140 Opaque object containing calibration information for stray-light
1141 correction. If `None`, no correction will be performed.
1142 illumMaskedImage : `lsst.afw.image.MaskedImage`, optional
1143 Illumination correction image.
1145 Returns
1146 -------
1147 result : `lsst.pipe.base.Struct`
1148 Result struct with component:
1149 - ``exposure`` : `afw.image.Exposure`
1150 The fully ISR corrected exposure.
1151 - ``outputExposure`` : `afw.image.Exposure`
1152 An alias for `exposure`
1153 - ``ossThumb`` : `numpy.ndarray`
1154 Thumbnail image of the exposure after overscan subtraction.
1155 - ``flattenedThumb`` : `numpy.ndarray`
1156 Thumbnail image of the exposure after flat-field correction.
1158 Raises
1159 ------
1160 RuntimeError
1161 Raised if a configuration option is set to True, but the
1162 required calibration data has not been specified.
1164 Notes
1165 -----
1166 The current processed exposure can be viewed by setting the
1167 appropriate lsstDebug entries in the `debug.display`
1168 dictionary. The names of these entries correspond to some of
1169 the IsrTaskConfig Boolean options, with the value denoting the
1170 frame to use. The exposure is shown inside the matching
1171 option check and after the processing of that step has
1172 finished. The steps with debug points are:
1174 doAssembleCcd
1175 doBias
1176 doCrosstalk
1177 doBrighterFatter
1178 doDark
1179 doFringe
1180 doStrayLight
1181 doFlat
1183 In addition, setting the "postISRCCD" entry displays the
1184 exposure after all ISR processing has finished.
1186 """
1188 if isGen3 is True:
1189 # Gen3 currently cannot automatically do configuration overrides.
1190 # DM-15257 looks to discuss this issue.
1191 # Configure input exposures;
1192 if detectorNum is None:
1193 raise RuntimeError("Must supply the detectorNum if running as Gen3.")
1195 ccdExposure = self.ensureExposure(ccdExposure, camera, detectorNum)
1196 bias = self.ensureExposure(bias, camera, detectorNum)
1197 dark = self.ensureExposure(dark, camera, detectorNum)
1198 flat = self.ensureExposure(flat, camera, detectorNum)
1199 else:
1200 if isinstance(ccdExposure, ButlerDataRef):
1201 return self.runDataRef(ccdExposure)
1203 ccd = ccdExposure.getDetector()
1204 filterName = afwImage.Filter(ccdExposure.getFilter().getId()).getName() # Canonical name for filter
1206 if not ccd:
1207 assert not self.config.doAssembleCcd, "You need a Detector to run assembleCcd."
1208 ccd = [FakeAmp(ccdExposure, self.config)]
1210 # Validate Input
1211 if self.config.doBias and bias is None:
1212 raise RuntimeError("Must supply a bias exposure if config.doBias=True.")
1213 if self.doLinearize(ccd) and linearizer is None:
1214 raise RuntimeError("Must supply a linearizer if config.doLinearize=True for this detector.")
1215 if self.config.doBrighterFatter and bfKernel is None:
1216 raise RuntimeError("Must supply a kernel if config.doBrighterFatter=True.")
1217 if self.config.doDark and dark is None:
1218 raise RuntimeError("Must supply a dark exposure if config.doDark=True.")
1219 if self.config.doFlat and flat is None:
1220 raise RuntimeError("Must supply a flat exposure if config.doFlat=True.")
1221 if self.config.doDefect and defects is None:
1222 raise RuntimeError("Must supply defects if config.doDefect=True.")
1223 if (self.config.doFringe and filterName in self.fringe.config.filters and
1224 fringes.fringes is None):
1225 # The `fringes` object needs to be a pipeBase.Struct, as
1226 # we use it as a `dict` for the parameters of
1227 # `FringeTask.run()`. The `fringes.fringes` `list` may
1228 # not be `None` if `doFringe=True`. Otherwise, raise.
1229 raise RuntimeError("Must supply fringe exposure as a pipeBase.Struct.")
1230 if (self.config.doIlluminationCorrection and filterName in self.config.illumFilters and
1231 illumMaskedImage is None):
1232 raise RuntimeError("Must supply an illumcor if config.doIlluminationCorrection=True.")
1234 # Begin ISR processing.
1235 if self.config.doConvertIntToFloat:
1236 self.log.info("Converting exposure to floating point values.")
1237 ccdExposure = self.convertIntToFloat(ccdExposure)
1239 # Amplifier level processing.
1240 overscans = []
1241 for amp in ccd:
1242 # if ccdExposure is one amp, check for coverage to prevent performing ops multiple times
1243 if ccdExposure.getBBox().contains(amp.getBBox()):
1244 # Check for fully masked bad amplifiers, and generate masks for SUSPECT and SATURATED values.
1245 badAmp = self.maskAmplifier(ccdExposure, amp, defects)
1247 if self.config.doOverscan and not badAmp:
1248 # Overscan correction on amp-by-amp basis.
1249 overscanResults = self.overscanCorrection(ccdExposure, amp)
1250 self.log.debug("Corrected overscan for amplifier %s.", amp.getName())
1251 if overscanResults is not None and \
1252 self.config.qa is not None and self.config.qa.saveStats is True:
1253 if isinstance(overscanResults.overscanFit, float):
1254 qaMedian = overscanResults.overscanFit
1255 qaStdev = float("NaN")
1256 else:
1257 qaStats = afwMath.makeStatistics(overscanResults.overscanFit,
1258 afwMath.MEDIAN | afwMath.STDEVCLIP)
1259 qaMedian = qaStats.getValue(afwMath.MEDIAN)
1260 qaStdev = qaStats.getValue(afwMath.STDEVCLIP)
1262 self.metadata.set(f"ISR OSCAN {amp.getName()} MEDIAN", qaMedian)
1263 self.metadata.set(f"ISR OSCAN {amp.getName()} STDEV", qaStdev)
1264 self.log.debug(" Overscan stats for amplifer %s: %f +/- %f",
1265 amp.getName(), qaMedian, qaStdev)
1266 ccdExposure.getMetadata().set('OVERSCAN', "Overscan corrected")
1267 else:
1268 if badAmp:
1269 self.log.warn("Amplifier %s is bad.", amp.getName())
1270 overscanResults = None
1272 overscans.append(overscanResults if overscanResults is not None else None)
1273 else:
1274 self.log.info("Skipped OSCAN for %s.", amp.getName())
1276 if self.config.doCrosstalk and self.config.doCrosstalkBeforeAssemble:
1277 self.log.info("Applying crosstalk correction.")
1278 self.crosstalk.run(ccdExposure, crosstalkSources=crosstalkSources)
1279 self.debugView(ccdExposure, "doCrosstalk")
1281 if self.config.doAssembleCcd:
1282 self.log.info("Assembling CCD from amplifiers.")
1283 ccdExposure = self.assembleCcd.assembleCcd(ccdExposure)
1285 if self.config.expectWcs and not ccdExposure.getWcs():
1286 self.log.warn("No WCS found in input exposure.")
1287 self.debugView(ccdExposure, "doAssembleCcd")
1289 ossThumb = None
1290 if self.config.qa.doThumbnailOss:
1291 ossThumb = isrQa.makeThumbnail(ccdExposure, isrQaConfig=self.config.qa)
1293 if self.config.doBias:
1294 self.log.info("Applying bias correction.")
1295 isrFunctions.biasCorrection(ccdExposure.getMaskedImage(), bias.getMaskedImage(),
1296 trimToFit=self.config.doTrimToMatchCalib)
1297 self.debugView(ccdExposure, "doBias")
1299 if self.config.doVariance:
1300 for amp, overscanResults in zip(ccd, overscans):
1301 if ccdExposure.getBBox().contains(amp.getBBox()):
1302 self.log.debug("Constructing variance map for amplifer %s.", amp.getName())
1303 ampExposure = ccdExposure.Factory(ccdExposure, amp.getBBox())
1304 if overscanResults is not None:
1305 self.updateVariance(ampExposure, amp,
1306 overscanImage=overscanResults.overscanImage)
1307 else:
1308 self.updateVariance(ampExposure, amp,
1309 overscanImage=None)
1310 if self.config.qa is not None and self.config.qa.saveStats is True:
1311 qaStats = afwMath.makeStatistics(ampExposure.getVariance(),
1312 afwMath.MEDIAN | afwMath.STDEVCLIP)
1313 self.metadata.set(f"ISR VARIANCE {amp.getName()} MEDIAN",
1314 qaStats.getValue(afwMath.MEDIAN))
1315 self.metadata.set(f"ISR VARIANCE {amp.getName()} STDEV",
1316 qaStats.getValue(afwMath.STDEVCLIP))
1317 self.log.debug(" Variance stats for amplifer %s: %f +/- %f.",
1318 amp.getName(), qaStats.getValue(afwMath.MEDIAN),
1319 qaStats.getValue(afwMath.STDEVCLIP))
1321 if self.doLinearize(ccd):
1322 self.log.info("Applying linearizer.")
1323 linearizer.applyLinearity(image=ccdExposure.getMaskedImage().getImage(),
1324 detector=ccd, log=self.log)
1326 if self.config.doCrosstalk and not self.config.doCrosstalkBeforeAssemble:
1327 self.log.info("Applying crosstalk correction.")
1328 self.crosstalk.run(ccdExposure, crosstalkSources=crosstalkSources, isTrimmed=True)
1329 self.debugView(ccdExposure, "doCrosstalk")
1331 # Masking block. Optionally mask known defects, NAN pixels, widen trails, and do
1332 # anything else the camera needs. Saturated and suspect pixels have already been masked.
1333 if self.config.doDefect:
1334 self.log.info("Masking defects.")
1335 self.maskDefect(ccdExposure, defects)
1337 if self.config.numEdgeSuspect > 0:
1338 self.log.info("Masking edges as SUSPECT.")
1339 self.maskEdges(ccdExposure, numEdgePixels=self.config.numEdgeSuspect,
1340 maskPlane="SUSPECT")
1342 if self.config.doNanMasking:
1343 self.log.info("Masking NAN value pixels.")
1344 self.maskNan(ccdExposure)
1346 if self.config.doWidenSaturationTrails:
1347 self.log.info("Widening saturation trails.")
1348 isrFunctions.widenSaturationTrails(ccdExposure.getMaskedImage().getMask())
1350 if self.config.doCameraSpecificMasking:
1351 self.log.info("Masking regions for camera specific reasons.")
1352 self.masking.run(ccdExposure)
1354 if self.config.doBrighterFatter:
1355 # We need to apply flats and darks before we can interpolate, and we
1356 # need to interpolate before we do B-F, but we do B-F without the
1357 # flats and darks applied so we can work in units of electrons or holes.
1358 # This context manager applies and then removes the darks and flats.
1359 #
1360 # We also do not want to interpolate values here, so operate on temporary
1361 # images so we can apply only the BF-correction and roll back the
1362 # interpolation.
1363 interpExp = ccdExposure.clone()
1364 with self.flatContext(interpExp, flat, dark):
1365 isrFunctions.interpolateFromMask(
1366 maskedImage=interpExp.getMaskedImage(),
1367 fwhm=self.config.fwhm,
1368 growSaturatedFootprints=self.config.growSaturationFootprintSize,
1369 maskNameList=self.config.maskListToInterpolate
1370 )
1371 bfExp = interpExp.clone()
1373 self.log.info("Applying brighter fatter correction using kernel type %s / gains %s.",
1374 type(bfKernel), type(bfGains))
1375 bfResults = isrFunctions.brighterFatterCorrection(bfExp, bfKernel,
1376 self.config.brighterFatterMaxIter,
1377 self.config.brighterFatterThreshold,
1378 self.config.brighterFatterApplyGain,
1379 bfGains)
1380 if bfResults[1] == self.config.brighterFatterMaxIter:
1381 self.log.warn("Brighter fatter correction did not converge, final difference %f.",
1382 bfResults[0])
1383 else:
1384 self.log.info("Finished brighter fatter correction in %d iterations.",
1385 bfResults[1])
1386 image = ccdExposure.getMaskedImage().getImage()
1387 bfCorr = bfExp.getMaskedImage().getImage()
1388 bfCorr -= interpExp.getMaskedImage().getImage()
1389 image += bfCorr
1391 # Applying the brighter-fatter correction applies a
1392 # convolution to the science image. At the edges this
1393 # convolution may not have sufficient valid pixels to
1394 # produce a valid correction. Mark pixels within the size
1395 # of the brighter-fatter kernel as EDGE to warn of this
1396 # fact.
1397 self.log.info("Ensuring image edges are masked as SUSPECT to the brighter-fatter kernel size.")
1398 self.maskEdges(ccdExposure, numEdgePixels=numpy.max(bfKernel.shape) // 2,
1399 maskPlane="EDGE")
1401 if self.config.brighterFatterMaskGrowSize > 0:
1402 self.log.info("Growing masks to account for brighter-fatter kernel convolution.")
1403 for maskPlane in self.config.maskListToInterpolate:
1404 isrFunctions.growMasks(ccdExposure.getMask(),
1405 radius=self.config.brighterFatterMaskGrowSize,
1406 maskNameList=maskPlane,
1407 maskValue=maskPlane)
1409 self.debugView(ccdExposure, "doBrighterFatter")
1411 if self.config.doDark:
1412 self.log.info("Applying dark correction.")
1413 self.darkCorrection(ccdExposure, dark)
1414 self.debugView(ccdExposure, "doDark")
1416 if self.config.doFringe and not self.config.fringeAfterFlat:
1417 self.log.info("Applying fringe correction before flat.")
1418 self.fringe.run(ccdExposure, **fringes.getDict())
1419 self.debugView(ccdExposure, "doFringe")
1421 if self.config.doStrayLight and self.strayLight.check(ccdExposure):
1422 self.log.info("Checking strayLight correction.")
1423 self.strayLight.run(ccdExposure, strayLightData)
1424 self.debugView(ccdExposure, "doStrayLight")
1426 if self.config.doFlat:
1427 self.log.info("Applying flat correction.")
1428 self.flatCorrection(ccdExposure, flat)
1429 self.debugView(ccdExposure, "doFlat")
1431 if self.config.doApplyGains:
1432 self.log.info("Applying gain correction instead of flat.")
1433 isrFunctions.applyGains(ccdExposure, self.config.normalizeGains)
1435 if self.config.doFringe and self.config.fringeAfterFlat:
1436 self.log.info("Applying fringe correction after flat.")
1437 self.fringe.run(ccdExposure, **fringes.getDict())
1439 if self.config.doVignette:
1440 self.log.info("Constructing Vignette polygon.")
1441 self.vignettePolygon = self.vignette.run(ccdExposure)
1443 if self.config.vignette.doWriteVignettePolygon:
1444 self.setValidPolygonIntersect(ccdExposure, self.vignettePolygon)
1446 if self.config.doAttachTransmissionCurve:
1447 self.log.info("Adding transmission curves.")
1448 isrFunctions.attachTransmissionCurve(ccdExposure, opticsTransmission=opticsTransmission,
1449 filterTransmission=filterTransmission,
1450 sensorTransmission=sensorTransmission,
1451 atmosphereTransmission=atmosphereTransmission)
1453 flattenedThumb = None
1454 if self.config.qa.doThumbnailFlattened:
1455 flattenedThumb = isrQa.makeThumbnail(ccdExposure, isrQaConfig=self.config.qa)
1457 if self.config.doIlluminationCorrection and filterName in self.config.illumFilters:
1458 self.log.info("Performing illumination correction.")
1459 isrFunctions.illuminationCorrection(ccdExposure.getMaskedImage(),
1460 illumMaskedImage, illumScale=self.config.illumScale,
1461 trimToFit=self.config.doTrimToMatchCalib)
1463 preInterpExp = None
1464 if self.config.doSaveInterpPixels:
1465 preInterpExp = ccdExposure.clone()
1467 # Reset and interpolate bad pixels.
1468 #
1469 # Large contiguous bad regions (which should have the BAD mask
1470 # bit set) should have their values set to the image median.
1471 # This group should include defects and bad amplifiers. As the
1472 # area covered by these defects are large, there's little
1473 # reason to expect that interpolation would provide a more
1474 # useful value.
1475 #
1476 # Smaller defects can be safely interpolated after the larger
1477 # regions have had their pixel values reset. This ensures
1478 # that the remaining defects adjacent to bad amplifiers (as an
1479 # example) do not attempt to interpolate extreme values.
1480 if self.config.doSetBadRegions:
1481 badPixelCount, badPixelValue = isrFunctions.setBadRegions(ccdExposure)
1482 if badPixelCount > 0:
1483 self.log.info("Set %d BAD pixels to %f.", badPixelCount, badPixelValue)
1485 if self.config.doInterpolate:
1486 self.log.info("Interpolating masked pixels.")
1487 isrFunctions.interpolateFromMask(
1488 maskedImage=ccdExposure.getMaskedImage(),
1489 fwhm=self.config.fwhm,
1490 growSaturatedFootprints=self.config.growSaturationFootprintSize,
1491 maskNameList=list(self.config.maskListToInterpolate)
1492 )
1494 self.roughZeroPoint(ccdExposure)
1496 if self.config.doMeasureBackground:
1497 self.log.info("Measuring background level.")
1498 self.measureBackground(ccdExposure, self.config.qa)
1500 if self.config.qa is not None and self.config.qa.saveStats is True:
1501 for amp in ccd:
1502 ampExposure = ccdExposure.Factory(ccdExposure, amp.getBBox())
1503 qaStats = afwMath.makeStatistics(ampExposure.getImage(),
1504 afwMath.MEDIAN | afwMath.STDEVCLIP)
1505 self.metadata.set("ISR BACKGROUND {} MEDIAN".format(amp.getName()),
1506 qaStats.getValue(afwMath.MEDIAN))
1507 self.metadata.set("ISR BACKGROUND {} STDEV".format(amp.getName()),
1508 qaStats.getValue(afwMath.STDEVCLIP))
1509 self.log.debug(" Background stats for amplifer %s: %f +/- %f",
1510 amp.getName(), qaStats.getValue(afwMath.MEDIAN),
1511 qaStats.getValue(afwMath.STDEVCLIP))
1513 self.debugView(ccdExposure, "postISRCCD")
1515 return pipeBase.Struct(
1516 exposure=ccdExposure,
1517 ossThumb=ossThumb,
1518 flattenedThumb=flattenedThumb,
1520 preInterpolatedExposure=preInterpExp,
1521 outputExposure=ccdExposure,
1522 outputOssThumbnail=ossThumb,
1523 outputFlattenedThumbnail=flattenedThumb,
1524 )
1526 @pipeBase.timeMethod
1527 def runDataRef(self, sensorRef):
1528 """Perform instrument signature removal on a ButlerDataRef of a Sensor.
1530 This method contains the `CmdLineTask` interface to the ISR
1531 processing. All IO is handled here, freeing the `run()` method
1532 to manage only pixel-level calculations. The steps performed
1533 are:
1534 - Read in necessary detrending/isr/calibration data.
1535 - Process raw exposure in `run()`.
1536 - Persist the ISR-corrected exposure as "postISRCCD" if
1537 config.doWrite=True.
1539 Parameters
1540 ----------
1541 sensorRef : `daf.persistence.butlerSubset.ButlerDataRef`
1542 DataRef of the detector data to be processed
1544 Returns
1545 -------
1546 result : `lsst.pipe.base.Struct`
1547 Result struct with component:
1548 - ``exposure`` : `afw.image.Exposure`
1549 The fully ISR corrected exposure.
1551 Raises
1552 ------
1553 RuntimeError
1554 Raised if a configuration option is set to True, but the
1555 required calibration data does not exist.
1557 """
1558 self.log.info("Performing ISR on sensor %s.", sensorRef.dataId)
1560 ccdExposure = sensorRef.get(self.config.datasetType)
1562 camera = sensorRef.get("camera")
1563 isrData = self.readIsrData(sensorRef, ccdExposure)
1565 result = self.run(ccdExposure, camera=camera, **isrData.getDict())
1567 if self.config.doWrite:
1568 sensorRef.put(result.exposure, "postISRCCD")
1569 if result.preInterpolatedExposure is not None:
1570 sensorRef.put(result.preInterpolatedExposure, "postISRCCD_uninterpolated")
1571 if result.ossThumb is not None:
1572 isrQa.writeThumbnail(sensorRef, result.ossThumb, "ossThumb")
1573 if result.flattenedThumb is not None:
1574 isrQa.writeThumbnail(sensorRef, result.flattenedThumb, "flattenedThumb")
1576 return result
1578 def getIsrExposure(self, dataRef, datasetType, dateObs=None, immediate=True):
1579 """!Retrieve a calibration dataset for removing instrument signature.
1581 Parameters
1582 ----------
1584 dataRef : `daf.persistence.butlerSubset.ButlerDataRef`
1585 DataRef of the detector data to find calibration datasets
1586 for.
1587 datasetType : `str`
1588 Type of dataset to retrieve (e.g. 'bias', 'flat', etc).
1589 dateObs : `str`, optional
1590 Date of the observation. Used to correct butler failures
1591 when using fallback filters.
1592 immediate : `Bool`
1593 If True, disable butler proxies to enable error handling
1594 within this routine.
1596 Returns
1597 -------
1598 exposure : `lsst.afw.image.Exposure`
1599 Requested calibration frame.
1601 Raises
1602 ------
1603 RuntimeError
1604 Raised if no matching calibration frame can be found.
1605 """
1606 try:
1607 exp = dataRef.get(datasetType, immediate=immediate)
1608 except Exception as exc1:
1609 if not self.config.fallbackFilterName:
1610 raise RuntimeError("Unable to retrieve %s for %s: %s." % (datasetType, dataRef.dataId, exc1))
1611 try:
1612 if self.config.useFallbackDate and dateObs:
1613 exp = dataRef.get(datasetType, filter=self.config.fallbackFilterName,
1614 dateObs=dateObs, immediate=immediate)
1615 else:
1616 exp = dataRef.get(datasetType, filter=self.config.fallbackFilterName, immediate=immediate)
1617 except Exception as exc2:
1618 raise RuntimeError("Unable to retrieve %s for %s, even with fallback filter %s: %s AND %s." %
1619 (datasetType, dataRef.dataId, self.config.fallbackFilterName, exc1, exc2))
1620 self.log.warn("Using fallback calibration from filter %s.", self.config.fallbackFilterName)
1622 if self.config.doAssembleIsrExposures:
1623 exp = self.assembleCcd.assembleCcd(exp)
1624 return exp
1626 def ensureExposure(self, inputExp, camera, detectorNum):
1627 """Ensure that the data returned by Butler is a fully constructed exposure.
1629 ISR requires exposure-level image data for historical reasons, so if we did
1630 not recieve that from Butler, construct it from what we have, modifying the
1631 input in place.
1633 Parameters
1634 ----------
1635 inputExp : `lsst.afw.image.Exposure`, `lsst.afw.image.DecoratedImageU`, or
1636 `lsst.afw.image.ImageF`
1637 The input data structure obtained from Butler.
1638 camera : `lsst.afw.cameraGeom.camera`
1639 The camera associated with the image. Used to find the appropriate
1640 detector.
1641 detectorNum : `int`
1642 The detector this exposure should match.
1644 Returns
1645 -------
1646 inputExp : `lsst.afw.image.Exposure`
1647 The re-constructed exposure, with appropriate detector parameters.
1649 Raises
1650 ------
1651 TypeError
1652 Raised if the input data cannot be used to construct an exposure.
1653 """
1654 if isinstance(inputExp, afwImage.DecoratedImageU):
1655 inputExp = afwImage.makeExposure(afwImage.makeMaskedImage(inputExp))
1656 elif isinstance(inputExp, afwImage.ImageF):
1657 inputExp = afwImage.makeExposure(afwImage.makeMaskedImage(inputExp))
1658 elif isinstance(inputExp, afwImage.MaskedImageF):
1659 inputExp = afwImage.makeExposure(inputExp)
1660 elif isinstance(inputExp, afwImage.Exposure):
1661 pass
1662 elif inputExp is None:
1663 # Assume this will be caught by the setup if it is a problem.
1664 return inputExp
1665 else:
1666 raise TypeError("Input Exposure is not known type in isrTask.ensureExposure: %s." %
1667 (type(inputExp), ))
1669 if inputExp.getDetector() is None:
1670 inputExp.setDetector(camera[detectorNum])
1672 return inputExp
1674 def convertIntToFloat(self, exposure):
1675 """Convert exposure image from uint16 to float.
1677 If the exposure does not need to be converted, the input is
1678 immediately returned. For exposures that are converted to use
1679 floating point pixels, the variance is set to unity and the
1680 mask to zero.
1682 Parameters
1683 ----------
1684 exposure : `lsst.afw.image.Exposure`
1685 The raw exposure to be converted.
1687 Returns
1688 -------
1689 newexposure : `lsst.afw.image.Exposure`
1690 The input ``exposure``, converted to floating point pixels.
1692 Raises
1693 ------
1694 RuntimeError
1695 Raised if the exposure type cannot be converted to float.
1697 """
1698 if isinstance(exposure, afwImage.ExposureF):
1699 # Nothing to be done
1700 self.log.debug("Exposure already of type float.")
1701 return exposure
1702 if not hasattr(exposure, "convertF"):
1703 raise RuntimeError("Unable to convert exposure (%s) to float." % type(exposure))
1705 newexposure = exposure.convertF()
1706 newexposure.variance[:] = 1
1707 newexposure.mask[:] = 0x0
1709 return newexposure
1711 def maskAmplifier(self, ccdExposure, amp, defects):
1712 """Identify bad amplifiers, saturated and suspect pixels.
1714 Parameters
1715 ----------
1716 ccdExposure : `lsst.afw.image.Exposure`
1717 Input exposure to be masked.
1718 amp : `lsst.afw.table.AmpInfoCatalog`
1719 Catalog of parameters defining the amplifier on this
1720 exposure to mask.
1721 defects : `lsst.meas.algorithms.Defects`
1722 List of defects. Used to determine if the entire
1723 amplifier is bad.
1725 Returns
1726 -------
1727 badAmp : `Bool`
1728 If this is true, the entire amplifier area is covered by
1729 defects and unusable.
1731 """
1732 maskedImage = ccdExposure.getMaskedImage()
1734 badAmp = False
1736 # Check if entire amp region is defined as a defect (need to use amp.getBBox() for correct
1737 # comparison with current defects definition.
1738 if defects is not None:
1739 badAmp = bool(sum([v.getBBox().contains(amp.getBBox()) for v in defects]))
1741 # In the case of a bad amp, we will set mask to "BAD" (here use amp.getRawBBox() for correct
1742 # association with pixels in current ccdExposure).
1743 if badAmp:
1744 dataView = afwImage.MaskedImageF(maskedImage, amp.getRawBBox(),
1745 afwImage.PARENT)
1746 maskView = dataView.getMask()
1747 maskView |= maskView.getPlaneBitMask("BAD")
1748 del maskView
1749 return badAmp
1751 # Mask remaining defects after assembleCcd() to allow for defects that cross amplifier boundaries.
1752 # Saturation and suspect pixels can be masked now, though.
1753 limits = dict()
1754 if self.config.doSaturation and not badAmp:
1755 limits.update({self.config.saturatedMaskName: amp.getSaturation()})
1756 if self.config.doSuspect and not badAmp:
1757 limits.update({self.config.suspectMaskName: amp.getSuspectLevel()})
1758 if math.isfinite(self.config.saturation):
1759 limits.update({self.config.saturatedMaskName: self.config.saturation})
1761 for maskName, maskThreshold in limits.items():
1762 if not math.isnan(maskThreshold):
1763 dataView = maskedImage.Factory(maskedImage, amp.getRawBBox())
1764 isrFunctions.makeThresholdMask(
1765 maskedImage=dataView,
1766 threshold=maskThreshold,
1767 growFootprints=0,
1768 maskName=maskName
1769 )
1771 # Determine if we've fully masked this amplifier with SUSPECT and SAT pixels.
1772 maskView = afwImage.Mask(maskedImage.getMask(), amp.getRawDataBBox(),
1773 afwImage.PARENT)
1774 maskVal = maskView.getPlaneBitMask([self.config.saturatedMaskName,
1775 self.config.suspectMaskName])
1776 if numpy.all(maskView.getArray() & maskVal > 0):
1777 badAmp = True
1778 maskView |= maskView.getPlaneBitMask("BAD")
1780 return badAmp
1782 def overscanCorrection(self, ccdExposure, amp):
1783 """Apply overscan correction in place.
1785 This method does initial pixel rejection of the overscan
1786 region. The overscan can also be optionally segmented to
1787 allow for discontinuous overscan responses to be fit
1788 separately. The actual overscan subtraction is performed by
1789 the `lsst.ip.isr.isrFunctions.overscanCorrection` function,
1790 which is called here after the amplifier is preprocessed.
1792 Parameters
1793 ----------
1794 ccdExposure : `lsst.afw.image.Exposure`
1795 Exposure to have overscan correction performed.
1796 amp : `lsst.afw.table.AmpInfoCatalog`
1797 The amplifier to consider while correcting the overscan.
1799 Returns
1800 -------
1801 overscanResults : `lsst.pipe.base.Struct`
1802 Result struct with components:
1803 - ``imageFit`` : scalar or `lsst.afw.image.Image`
1804 Value or fit subtracted from the amplifier image data.
1805 - ``overscanFit`` : scalar or `lsst.afw.image.Image`
1806 Value or fit subtracted from the overscan image data.
1807 - ``overscanImage`` : `lsst.afw.image.Image`
1808 Image of the overscan region with the overscan
1809 correction applied. This quantity is used to estimate
1810 the amplifier read noise empirically.
1812 Raises
1813 ------
1814 RuntimeError
1815 Raised if the ``amp`` does not contain raw pixel information.
1817 See Also
1818 --------
1819 lsst.ip.isr.isrFunctions.overscanCorrection
1820 """
1821 if not amp.getHasRawInfo():
1822 raise RuntimeError("This method must be executed on an amp with raw information.")
1824 if amp.getRawHorizontalOverscanBBox().isEmpty():
1825 self.log.info("ISR_OSCAN: No overscan region. Not performing overscan correction.")
1826 return None
1828 statControl = afwMath.StatisticsControl()
1829 statControl.setAndMask(ccdExposure.mask.getPlaneBitMask("SAT"))
1831 # Determine the bounding boxes
1832 dataBBox = amp.getRawDataBBox()
1833 oscanBBox = amp.getRawHorizontalOverscanBBox()
1834 dx0 = 0
1835 dx1 = 0
1837 prescanBBox = amp.getRawPrescanBBox()
1838 if (oscanBBox.getBeginX() > prescanBBox.getBeginX()): # amp is at the right
1839 dx0 += self.config.overscanNumLeadingColumnsToSkip
1840 dx1 -= self.config.overscanNumTrailingColumnsToSkip
1841 else:
1842 dx0 += self.config.overscanNumTrailingColumnsToSkip
1843 dx1 -= self.config.overscanNumLeadingColumnsToSkip
1845 # Determine if we need to work on subregions of the amplifier and overscan.
1846 imageBBoxes = []
1847 overscanBBoxes = []
1849 if ((self.config.overscanBiasJump and
1850 self.config.overscanBiasJumpLocation) and
1851 (ccdExposure.getMetadata().exists(self.config.overscanBiasJumpKeyword) and
1852 ccdExposure.getMetadata().getScalar(self.config.overscanBiasJumpKeyword) in
1853 self.config.overscanBiasJumpDevices)):
1854 if amp.getReadoutCorner() in (ReadoutCorner.LL, ReadoutCorner.LR):
1855 yLower = self.config.overscanBiasJumpLocation
1856 yUpper = dataBBox.getHeight() - yLower
1857 else:
1858 yUpper = self.config.overscanBiasJumpLocation
1859 yLower = dataBBox.getHeight() - yUpper
1861 imageBBoxes.append(lsst.geom.Box2I(dataBBox.getBegin(),
1862 lsst.geom.Extent2I(dataBBox.getWidth(), yLower)))
1863 overscanBBoxes.append(lsst.geom.Box2I(oscanBBox.getBegin() +
1864 lsst.geom.Extent2I(dx0, 0),
1865 lsst.geom.Extent2I(oscanBBox.getWidth() - dx0 + dx1,
1866 yLower)))
1868 imageBBoxes.append(lsst.geom.Box2I(dataBBox.getBegin() + lsst.geom.Extent2I(0, yLower),
1869 lsst.geom.Extent2I(dataBBox.getWidth(), yUpper)))
1870 overscanBBoxes.append(lsst.geom.Box2I(oscanBBox.getBegin() + lsst.geom.Extent2I(dx0, yLower),
1871 lsst.geom.Extent2I(oscanBBox.getWidth() - dx0 + dx1,
1872 yUpper)))
1873 else:
1874 imageBBoxes.append(lsst.geom.Box2I(dataBBox.getBegin(),
1875 lsst.geom.Extent2I(dataBBox.getWidth(), dataBBox.getHeight())))
1876 overscanBBoxes.append(lsst.geom.Box2I(oscanBBox.getBegin() + lsst.geom.Extent2I(dx0, 0),
1877 lsst.geom.Extent2I(oscanBBox.getWidth() - dx0 + dx1,
1878 oscanBBox.getHeight())))
1880 # Perform overscan correction on subregions, ensuring saturated pixels are masked.
1881 for imageBBox, overscanBBox in zip(imageBBoxes, overscanBBoxes):
1882 ampImage = ccdExposure.maskedImage[imageBBox]
1883 overscanImage = ccdExposure.maskedImage[overscanBBox]
1885 overscanArray = overscanImage.image.array
1886 median = numpy.ma.median(numpy.ma.masked_where(overscanImage.mask.array, overscanArray))
1887 bad = numpy.where(numpy.abs(overscanArray - median) > self.config.overscanMaxDev)
1888 overscanImage.mask.array[bad] = overscanImage.mask.getPlaneBitMask("SAT")
1890 statControl = afwMath.StatisticsControl()
1891 statControl.setAndMask(ccdExposure.mask.getPlaneBitMask("SAT"))
1893 overscanResults = self.overscan.run(ampImage.getImage(), overscanImage)
1895 # Measure average overscan levels and record them in the metadata.
1896 levelStat = afwMath.MEDIAN
1897 sigmaStat = afwMath.STDEVCLIP
1899 sctrl = afwMath.StatisticsControl(self.config.qa.flatness.clipSigma,
1900 self.config.qa.flatness.nIter)
1901 metadata = ccdExposure.getMetadata()
1902 ampNum = amp.getName()
1903 # if self.config.overscanFitType in ("MEDIAN", "MEAN", "MEANCLIP"):
1904 if isinstance(overscanResults.overscanFit, float):
1905 metadata.set("ISR_OSCAN_LEVEL%s" % ampNum, overscanResults.overscanFit)
1906 metadata.set("ISR_OSCAN_SIGMA%s" % ampNum, 0.0)
1907 else:
1908 stats = afwMath.makeStatistics(overscanResults.overscanFit, levelStat | sigmaStat, sctrl)
1909 metadata.set("ISR_OSCAN_LEVEL%s" % ampNum, stats.getValue(levelStat))
1910 metadata.set("ISR_OSCAN_SIGMA%s" % ampNum, stats.getValue(sigmaStat))
1912 return overscanResults
1914 def updateVariance(self, ampExposure, amp, overscanImage=None):
1915 """Set the variance plane using the amplifier gain and read noise
1917 The read noise is calculated from the ``overscanImage`` if the
1918 ``doEmpiricalReadNoise`` option is set in the configuration; otherwise
1919 the value from the amplifier data is used.
1921 Parameters
1922 ----------
1923 ampExposure : `lsst.afw.image.Exposure`
1924 Exposure to process.
1925 amp : `lsst.afw.table.AmpInfoRecord` or `FakeAmp`
1926 Amplifier detector data.
1927 overscanImage : `lsst.afw.image.MaskedImage`, optional.
1928 Image of overscan, required only for empirical read noise.
1930 See also
1931 --------
1932 lsst.ip.isr.isrFunctions.updateVariance
1933 """
1934 maskPlanes = [self.config.saturatedMaskName, self.config.suspectMaskName]
1935 gain = amp.getGain()
1937 if math.isnan(gain):
1938 gain = 1.0
1939 self.log.warn("Gain set to NAN! Updating to 1.0 to generate Poisson variance.")
1940 elif gain <= 0:
1941 patchedGain = 1.0
1942 self.log.warn("Gain for amp %s == %g <= 0; setting to %f.",
1943 amp.getName(), gain, patchedGain)
1944 gain = patchedGain
1946 if self.config.doEmpiricalReadNoise and overscanImage is None:
1947 self.log.info("Overscan is none for EmpiricalReadNoise.")
1949 if self.config.doEmpiricalReadNoise and overscanImage is not None:
1950 stats = afwMath.StatisticsControl()
1951 stats.setAndMask(overscanImage.mask.getPlaneBitMask(maskPlanes))
1952 readNoise = afwMath.makeStatistics(overscanImage, afwMath.STDEVCLIP, stats).getValue()
1953 self.log.info("Calculated empirical read noise for amp %s: %f.",
1954 amp.getName(), readNoise)
1955 else:
1956 readNoise = amp.getReadNoise()
1958 isrFunctions.updateVariance(
1959 maskedImage=ampExposure.getMaskedImage(),
1960 gain=gain,
1961 readNoise=readNoise,
1962 )
1964 def darkCorrection(self, exposure, darkExposure, invert=False):
1965 """!Apply dark correction in place.
1967 Parameters
1968 ----------
1969 exposure : `lsst.afw.image.Exposure`
1970 Exposure to process.
1971 darkExposure : `lsst.afw.image.Exposure`
1972 Dark exposure of the same size as ``exposure``.
1973 invert : `Bool`, optional
1974 If True, re-add the dark to an already corrected image.
1976 Raises
1977 ------
1978 RuntimeError
1979 Raised if either ``exposure`` or ``darkExposure`` do not
1980 have their dark time defined.
1982 See Also
1983 --------
1984 lsst.ip.isr.isrFunctions.darkCorrection
1985 """
1986 expScale = exposure.getInfo().getVisitInfo().getDarkTime()
1987 if math.isnan(expScale):
1988 raise RuntimeError("Exposure darktime is NAN.")
1989 if darkExposure.getInfo().getVisitInfo() is not None \
1990 and not math.isnan(darkExposure.getInfo().getVisitInfo().getDarkTime()):
1991 darkScale = darkExposure.getInfo().getVisitInfo().getDarkTime()
1992 else:
1993 # DM-17444: darkExposure.getInfo.getVisitInfo() is None
1994 # so getDarkTime() does not exist.
1995 self.log.warn("darkExposure.getInfo().getVisitInfo() does not exist. Using darkScale = 1.0.")
1996 darkScale = 1.0
1998 isrFunctions.darkCorrection(
1999 maskedImage=exposure.getMaskedImage(),
2000 darkMaskedImage=darkExposure.getMaskedImage(),
2001 expScale=expScale,
2002 darkScale=darkScale,
2003 invert=invert,
2004 trimToFit=self.config.doTrimToMatchCalib
2005 )
2007 def doLinearize(self, detector):
2008 """!Check if linearization is needed for the detector cameraGeom.
2010 Checks config.doLinearize and the linearity type of the first
2011 amplifier.
2013 Parameters
2014 ----------
2015 detector : `lsst.afw.cameraGeom.Detector`
2016 Detector to get linearity type from.
2018 Returns
2019 -------
2020 doLinearize : `Bool`
2021 If True, linearization should be performed.
2022 """
2023 return self.config.doLinearize and \
2024 detector.getAmplifiers()[0].getLinearityType() != NullLinearityType
2026 def flatCorrection(self, exposure, flatExposure, invert=False):
2027 """!Apply flat correction in place.
2029 Parameters
2030 ----------
2031 exposure : `lsst.afw.image.Exposure`
2032 Exposure to process.
2033 flatExposure : `lsst.afw.image.Exposure`
2034 Flat exposure of the same size as ``exposure``.
2035 invert : `Bool`, optional
2036 If True, unflatten an already flattened image.
2038 See Also
2039 --------
2040 lsst.ip.isr.isrFunctions.flatCorrection
2041 """
2042 isrFunctions.flatCorrection(
2043 maskedImage=exposure.getMaskedImage(),
2044 flatMaskedImage=flatExposure.getMaskedImage(),
2045 scalingType=self.config.flatScalingType,
2046 userScale=self.config.flatUserScale,
2047 invert=invert,
2048 trimToFit=self.config.doTrimToMatchCalib
2049 )
2051 def saturationDetection(self, exposure, amp):
2052 """!Detect saturated pixels and mask them using mask plane config.saturatedMaskName, in place.
2054 Parameters
2055 ----------
2056 exposure : `lsst.afw.image.Exposure`
2057 Exposure to process. Only the amplifier DataSec is processed.
2058 amp : `lsst.afw.table.AmpInfoCatalog`
2059 Amplifier detector data.
2061 See Also
2062 --------
2063 lsst.ip.isr.isrFunctions.makeThresholdMask
2064 """
2065 if not math.isnan(amp.getSaturation()):
2066 maskedImage = exposure.getMaskedImage()
2067 dataView = maskedImage.Factory(maskedImage, amp.getRawBBox())
2068 isrFunctions.makeThresholdMask(
2069 maskedImage=dataView,
2070 threshold=amp.getSaturation(),
2071 growFootprints=0,
2072 maskName=self.config.saturatedMaskName,
2073 )
2075 def saturationInterpolation(self, exposure):
2076 """!Interpolate over saturated pixels, in place.
2078 This method should be called after `saturationDetection`, to
2079 ensure that the saturated pixels have been identified in the
2080 SAT mask. It should also be called after `assembleCcd`, since
2081 saturated regions may cross amplifier boundaries.
2083 Parameters
2084 ----------
2085 exposure : `lsst.afw.image.Exposure`
2086 Exposure to process.
2088 See Also
2089 --------
2090 lsst.ip.isr.isrTask.saturationDetection
2091 lsst.ip.isr.isrFunctions.interpolateFromMask
2092 """
2093 isrFunctions.interpolateFromMask(
2094 maskedImage=exposure.getMaskedImage(),
2095 fwhm=self.config.fwhm,
2096 growSaturatedFootprints=self.config.growSaturationFootprintSize,
2097 maskNameList=list(self.config.saturatedMaskName),
2098 )
2100 def suspectDetection(self, exposure, amp):
2101 """!Detect suspect pixels and mask them using mask plane config.suspectMaskName, in place.
2103 Parameters
2104 ----------
2105 exposure : `lsst.afw.image.Exposure`
2106 Exposure to process. Only the amplifier DataSec is processed.
2107 amp : `lsst.afw.table.AmpInfoCatalog`
2108 Amplifier detector data.
2110 See Also
2111 --------
2112 lsst.ip.isr.isrFunctions.makeThresholdMask
2114 Notes
2115 -----
2116 Suspect pixels are pixels whose value is greater than amp.getSuspectLevel().
2117 This is intended to indicate pixels that may be affected by unknown systematics;
2118 for example if non-linearity corrections above a certain level are unstable
2119 then that would be a useful value for suspectLevel. A value of `nan` indicates
2120 that no such level exists and no pixels are to be masked as suspicious.
2121 """
2122 suspectLevel = amp.getSuspectLevel()
2123 if math.isnan(suspectLevel):
2124 return
2126 maskedImage = exposure.getMaskedImage()
2127 dataView = maskedImage.Factory(maskedImage, amp.getRawBBox())
2128 isrFunctions.makeThresholdMask(
2129 maskedImage=dataView,
2130 threshold=suspectLevel,
2131 growFootprints=0,
2132 maskName=self.config.suspectMaskName,
2133 )
2135 def maskDefect(self, exposure, defectBaseList):
2136 """!Mask defects using mask plane "BAD", in place.
2138 Parameters
2139 ----------
2140 exposure : `lsst.afw.image.Exposure`
2141 Exposure to process.
2142 defectBaseList : `lsst.meas.algorithms.Defects` or `list` of
2143 `lsst.afw.image.DefectBase`.
2144 List of defects to mask.
2146 Notes
2147 -----
2148 Call this after CCD assembly, since defects may cross amplifier boundaries.
2149 """
2150 maskedImage = exposure.getMaskedImage()
2151 if not isinstance(defectBaseList, Defects):
2152 # Promotes DefectBase to Defect
2153 defectList = Defects(defectBaseList)
2154 else:
2155 defectList = defectBaseList
2156 defectList.maskPixels(maskedImage, maskName="BAD")
2158 def maskEdges(self, exposure, numEdgePixels=0, maskPlane="SUSPECT"):
2159 """!Mask edge pixels with applicable mask plane.
2161 Parameters
2162 ----------
2163 exposure : `lsst.afw.image.Exposure`
2164 Exposure to process.
2165 numEdgePixels : `int`, optional
2166 Number of edge pixels to mask.
2167 maskPlane : `str`, optional
2168 Mask plane name to use.
2169 """
2170 maskedImage = exposure.getMaskedImage()
2171 maskBitMask = maskedImage.getMask().getPlaneBitMask(maskPlane)
2173 if numEdgePixels > 0:
2174 goodBBox = maskedImage.getBBox()
2175 # This makes a bbox numEdgeSuspect pixels smaller than the image on each side
2176 goodBBox.grow(-numEdgePixels)
2177 # Mask pixels outside goodBBox
2178 SourceDetectionTask.setEdgeBits(
2179 maskedImage,
2180 goodBBox,
2181 maskBitMask
2182 )
2184 def maskAndInterpolateDefects(self, exposure, defectBaseList):
2185 """Mask and interpolate defects using mask plane "BAD", in place.
2187 Parameters
2188 ----------
2189 exposure : `lsst.afw.image.Exposure`
2190 Exposure to process.
2191 defectBaseList : `lsst.meas.algorithms.Defects` or `list` of
2192 `lsst.afw.image.DefectBase`.
2193 List of defects to mask and interpolate.
2195 See Also
2196 --------
2197 lsst.ip.isr.isrTask.maskDefect()
2198 """
2199 self.maskDefect(exposure, defectBaseList)
2200 self.maskEdges(exposure, numEdgePixels=self.config.numEdgeSuspect,
2201 maskPlane="SUSPECT")
2202 isrFunctions.interpolateFromMask(
2203 maskedImage=exposure.getMaskedImage(),
2204 fwhm=self.config.fwhm,
2205 growSaturatedFootprints=0,
2206 maskNameList=["BAD"],
2207 )
2209 def maskNan(self, exposure):
2210 """Mask NaNs using mask plane "UNMASKEDNAN", in place.
2212 Parameters
2213 ----------
2214 exposure : `lsst.afw.image.Exposure`
2215 Exposure to process.
2217 Notes
2218 -----
2219 We mask over all NaNs, including those that are masked with
2220 other bits (because those may or may not be interpolated over
2221 later, and we want to remove all NaNs). Despite this
2222 behaviour, the "UNMASKEDNAN" mask plane is used to preserve
2223 the historical name.
2224 """
2225 maskedImage = exposure.getMaskedImage()
2227 # Find and mask NaNs
2228 maskedImage.getMask().addMaskPlane("UNMASKEDNAN")
2229 maskVal = maskedImage.getMask().getPlaneBitMask("UNMASKEDNAN")
2230 numNans = maskNans(maskedImage, maskVal)
2231 self.metadata.set("NUMNANS", numNans)
2232 if numNans > 0:
2233 self.log.warn("There were %d unmasked NaNs.", numNans)
2235 def maskAndInterpolateNan(self, exposure):
2236 """"Mask and interpolate NaNs using mask plane "UNMASKEDNAN", in place.
2238 Parameters
2239 ----------
2240 exposure : `lsst.afw.image.Exposure`
2241 Exposure to process.
2243 See Also
2244 --------
2245 lsst.ip.isr.isrTask.maskNan()
2246 """
2247 self.maskNan(exposure)
2248 isrFunctions.interpolateFromMask(
2249 maskedImage=exposure.getMaskedImage(),
2250 fwhm=self.config.fwhm,
2251 growSaturatedFootprints=0,
2252 maskNameList=["UNMASKEDNAN"],
2253 )
2255 def measureBackground(self, exposure, IsrQaConfig=None):
2256 """Measure the image background in subgrids, for quality control purposes.
2258 Parameters
2259 ----------
2260 exposure : `lsst.afw.image.Exposure`
2261 Exposure to process.
2262 IsrQaConfig : `lsst.ip.isr.isrQa.IsrQaConfig`
2263 Configuration object containing parameters on which background
2264 statistics and subgrids to use.
2265 """
2266 if IsrQaConfig is not None:
2267 statsControl = afwMath.StatisticsControl(IsrQaConfig.flatness.clipSigma,
2268 IsrQaConfig.flatness.nIter)
2269 maskVal = exposure.getMaskedImage().getMask().getPlaneBitMask(["BAD", "SAT", "DETECTED"])
2270 statsControl.setAndMask(maskVal)
2271 maskedImage = exposure.getMaskedImage()
2272 stats = afwMath.makeStatistics(maskedImage, afwMath.MEDIAN | afwMath.STDEVCLIP, statsControl)
2273 skyLevel = stats.getValue(afwMath.MEDIAN)
2274 skySigma = stats.getValue(afwMath.STDEVCLIP)
2275 self.log.info("Flattened sky level: %f +/- %f.", skyLevel, skySigma)
2276 metadata = exposure.getMetadata()
2277 metadata.set('SKYLEVEL', skyLevel)
2278 metadata.set('SKYSIGMA', skySigma)
2280 # calcluating flatlevel over the subgrids
2281 stat = afwMath.MEANCLIP if IsrQaConfig.flatness.doClip else afwMath.MEAN
2282 meshXHalf = int(IsrQaConfig.flatness.meshX/2.)
2283 meshYHalf = int(IsrQaConfig.flatness.meshY/2.)
2284 nX = int((exposure.getWidth() + meshXHalf) / IsrQaConfig.flatness.meshX)
2285 nY = int((exposure.getHeight() + meshYHalf) / IsrQaConfig.flatness.meshY)
2286 skyLevels = numpy.zeros((nX, nY))
2288 for j in range(nY):
2289 yc = meshYHalf + j * IsrQaConfig.flatness.meshY
2290 for i in range(nX):
2291 xc = meshXHalf + i * IsrQaConfig.flatness.meshX
2293 xLLC = xc - meshXHalf
2294 yLLC = yc - meshYHalf
2295 xURC = xc + meshXHalf - 1
2296 yURC = yc + meshYHalf - 1
2298 bbox = lsst.geom.Box2I(lsst.geom.Point2I(xLLC, yLLC), lsst.geom.Point2I(xURC, yURC))
2299 miMesh = maskedImage.Factory(exposure.getMaskedImage(), bbox, afwImage.LOCAL)
2301 skyLevels[i, j] = afwMath.makeStatistics(miMesh, stat, statsControl).getValue()
2303 good = numpy.where(numpy.isfinite(skyLevels))
2304 skyMedian = numpy.median(skyLevels[good])
2305 flatness = (skyLevels[good] - skyMedian) / skyMedian
2306 flatness_rms = numpy.std(flatness)
2307 flatness_pp = flatness.max() - flatness.min() if len(flatness) > 0 else numpy.nan
2309 self.log.info("Measuring sky levels in %dx%d grids: %f.", nX, nY, skyMedian)
2310 self.log.info("Sky flatness in %dx%d grids - pp: %f rms: %f.",
2311 nX, nY, flatness_pp, flatness_rms)
2313 metadata.set('FLATNESS_PP', float(flatness_pp))
2314 metadata.set('FLATNESS_RMS', float(flatness_rms))
2315 metadata.set('FLATNESS_NGRIDS', '%dx%d' % (nX, nY))
2316 metadata.set('FLATNESS_MESHX', IsrQaConfig.flatness.meshX)
2317 metadata.set('FLATNESS_MESHY', IsrQaConfig.flatness.meshY)
2319 def roughZeroPoint(self, exposure):
2320 """Set an approximate magnitude zero point for the exposure.
2322 Parameters
2323 ----------
2324 exposure : `lsst.afw.image.Exposure`
2325 Exposure to process.
2326 """
2327 filterName = afwImage.Filter(exposure.getFilter().getId()).getName() # Canonical name for filter
2328 if filterName in self.config.fluxMag0T1:
2329 fluxMag0 = self.config.fluxMag0T1[filterName]
2330 else:
2331 self.log.warn("No rough magnitude zero point set for filter %s.", filterName)
2332 fluxMag0 = self.config.defaultFluxMag0T1
2334 expTime = exposure.getInfo().getVisitInfo().getExposureTime()
2335 if not expTime > 0: # handle NaN as well as <= 0
2336 self.log.warn("Non-positive exposure time; skipping rough zero point.")
2337 return
2339 self.log.info("Setting rough magnitude zero point: %f", 2.5*math.log10(fluxMag0*expTime))
2340 exposure.setPhotoCalib(afwImage.makePhotoCalibFromCalibZeroPoint(fluxMag0*expTime, 0.0))
2342 def setValidPolygonIntersect(self, ccdExposure, fpPolygon):
2343 """!Set the valid polygon as the intersection of fpPolygon and the ccd corners.
2345 Parameters
2346 ----------
2347 ccdExposure : `lsst.afw.image.Exposure`
2348 Exposure to process.
2349 fpPolygon : `lsst.afw.geom.Polygon`
2350 Polygon in focal plane coordinates.
2351 """
2352 # Get ccd corners in focal plane coordinates
2353 ccd = ccdExposure.getDetector()
2354 fpCorners = ccd.getCorners(FOCAL_PLANE)
2355 ccdPolygon = Polygon(fpCorners)
2357 # Get intersection of ccd corners with fpPolygon
2358 intersect = ccdPolygon.intersectionSingle(fpPolygon)
2360 # Transform back to pixel positions and build new polygon
2361 ccdPoints = ccd.transform(intersect, FOCAL_PLANE, PIXELS)
2362 validPolygon = Polygon(ccdPoints)
2363 ccdExposure.getInfo().setValidPolygon(validPolygon)
2365 @contextmanager
2366 def flatContext(self, exp, flat, dark=None):
2367 """Context manager that applies and removes flats and darks,
2368 if the task is configured to apply them.
2370 Parameters
2371 ----------
2372 exp : `lsst.afw.image.Exposure`
2373 Exposure to process.
2374 flat : `lsst.afw.image.Exposure`
2375 Flat exposure the same size as ``exp``.
2376 dark : `lsst.afw.image.Exposure`, optional
2377 Dark exposure the same size as ``exp``.
2379 Yields
2380 ------
2381 exp : `lsst.afw.image.Exposure`
2382 The flat and dark corrected exposure.
2383 """
2384 if self.config.doDark and dark is not None:
2385 self.darkCorrection(exp, dark)
2386 if self.config.doFlat:
2387 self.flatCorrection(exp, flat)
2388 try:
2389 yield exp
2390 finally:
2391 if self.config.doFlat:
2392 self.flatCorrection(exp, flat, invert=True)
2393 if self.config.doDark and dark is not None:
2394 self.darkCorrection(exp, dark, invert=True)
2396 def debugView(self, exposure, stepname):
2397 """Utility function to examine ISR exposure at different stages.
2399 Parameters
2400 ----------
2401 exposure : `lsst.afw.image.Exposure`
2402 Exposure to view.
2403 stepname : `str`
2404 State of processing to view.
2405 """
2406 frame = getDebugFrame(self._display, stepname)
2407 if frame:
2408 display = getDisplay(frame)
2409 display.scale('asinh', 'zscale')
2410 display.mtv(exposure)
2411 prompt = "Press Enter to continue [c]... "
2412 while True:
2413 ans = input(prompt).lower()
2414 if ans in ("", "c",):
2415 break
2418class FakeAmp(object):
2419 """A Detector-like object that supports returning gain and saturation level
2421 This is used when the input exposure does not have a detector.
2423 Parameters
2424 ----------
2425 exposure : `lsst.afw.image.Exposure`
2426 Exposure to generate a fake amplifier for.
2427 config : `lsst.ip.isr.isrTaskConfig`
2428 Configuration to apply to the fake amplifier.
2429 """
2431 def __init__(self, exposure, config):
2432 self._bbox = exposure.getBBox(afwImage.LOCAL)
2433 self._RawHorizontalOverscanBBox = lsst.geom.Box2I()
2434 self._gain = config.gain
2435 self._readNoise = config.readNoise
2436 self._saturation = config.saturation
2438 def getBBox(self):
2439 return self._bbox
2441 def getRawBBox(self):
2442 return self._bbox
2444 def getHasRawInfo(self):
2445 return True # but see getRawHorizontalOverscanBBox()
2447 def getRawHorizontalOverscanBBox(self):
2448 return self._RawHorizontalOverscanBBox
2450 def getGain(self):
2451 return self._gain
2453 def getReadNoise(self):
2454 return self._readNoise
2456 def getSaturation(self):
2457 return self._saturation
2459 def getSuspectLevel(self):
2460 return float("NaN")
2463class RunIsrConfig(pexConfig.Config):
2464 isr = pexConfig.ConfigurableField(target=IsrTask, doc="Instrument signature removal")
2467class RunIsrTask(pipeBase.CmdLineTask):
2468 """Task to wrap the default IsrTask to allow it to be retargeted.
2470 The standard IsrTask can be called directly from a command line
2471 program, but doing so removes the ability of the task to be
2472 retargeted. As most cameras override some set of the IsrTask
2473 methods, this would remove those data-specific methods in the
2474 output post-ISR images. This wrapping class fixes the issue,
2475 allowing identical post-ISR images to be generated by both the
2476 processCcd and isrTask code.
2477 """
2478 ConfigClass = RunIsrConfig
2479 _DefaultName = "runIsr"
2481 def __init__(self, *args, **kwargs):
2482 super().__init__(*args, **kwargs)
2483 self.makeSubtask("isr")
2485 def runDataRef(self, dataRef):
2486 """
2487 Parameters
2488 ----------
2489 dataRef : `lsst.daf.persistence.ButlerDataRef`
2490 data reference of the detector data to be processed
2492 Returns
2493 -------
2494 result : `pipeBase.Struct`
2495 Result struct with component:
2497 - exposure : `lsst.afw.image.Exposure`
2498 Post-ISR processed exposure.
2499 """
2500 return self.isr.runDataRef(dataRef)