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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
43from . import isrFunctions
44from . import isrQa
45from . import linearize
46from .defects import Defects
48from .assembleCcdTask import AssembleCcdTask
49from .crosstalk import CrosstalkTask, CrosstalkCalib
50from .fringe import FringeTask
51from .isr import maskNans
52from .masking import MaskingTask
53from .overscan import OverscanCorrectionTask
54from .straylight import StrayLightTask
55from .vignette import VignetteTask
56from lsst.daf.butler import DimensionGraph
59__all__ = ["IsrTask", "IsrTaskConfig", "RunIsrTask", "RunIsrConfig"]
62def crosstalkSourceLookup(datasetType, registry, quantumDataId, collections):
63 """Lookup function to identify crosstalkSource entries.
65 This should return an empty list under most circumstances. Only
66 when inter-chip crosstalk has been identified should this be
67 populated.
69 This will be unused until DM-25348 resolves the quantum graph
70 generation issue.
72 Parameters
73 ----------
74 datasetType : `str`
75 Dataset to lookup.
76 registry : `lsst.daf.butler.Registry`
77 Butler registry to query.
78 quantumDataId : `lsst.daf.butler.ExpandedDataCoordinate`
79 Data id to transform to identify crosstalkSources. The
80 ``detector`` entry will be stripped.
81 collections : `lsst.daf.butler.CollectionSearch`
82 Collections to search through.
84 Returns
85 -------
86 results : `list` [`lsst.daf.butler.DatasetRef`]
87 List of datasets that match the query that will be used as
88 crosstalkSources.
89 """
90 newDataId = quantumDataId.subset(DimensionGraph(registry.dimensions, names=["instrument", "exposure"]))
91 results = list(registry.queryDatasets(datasetType,
92 collections=collections,
93 dataId=newDataId,
94 findFirst=True,
95 ).expanded())
96 return results
99class IsrTaskConnections(pipeBase.PipelineTaskConnections,
100 dimensions={"instrument", "exposure", "detector"},
101 defaultTemplates={}):
102 ccdExposure = cT.Input(
103 name="raw",
104 doc="Input exposure to process.",
105 storageClass="Exposure",
106 dimensions=["instrument", "exposure", "detector"],
107 )
108 camera = cT.PrerequisiteInput(
109 name="camera",
110 storageClass="Camera",
111 doc="Input camera to construct complete exposures.",
112 dimensions=["instrument"],
113 isCalibration=True,
114 )
116 crosstalk = cT.PrerequisiteInput(
117 name="crosstalk",
118 doc="Input crosstalk object",
119 storageClass="CrosstalkCalib",
120 dimensions=["instrument", "detector"],
121 isCalibration=True,
122 )
123 # TODO: DM-25348. This does not work yet to correctly load
124 # possible crosstalk sources.
125 crosstalkSources = cT.PrerequisiteInput(
126 name="isrOverscanCorrected",
127 doc="Overscan corrected input images.",
128 storageClass="Exposure",
129 dimensions=["instrument", "exposure", "detector"],
130 deferLoad=True,
131 multiple=True,
132 lookupFunction=crosstalkSourceLookup,
133 )
134 bias = cT.PrerequisiteInput(
135 name="bias",
136 doc="Input bias calibration.",
137 storageClass="ExposureF",
138 dimensions=["instrument", "detector"],
139 isCalibration=True,
140 )
141 dark = cT.PrerequisiteInput(
142 name='dark',
143 doc="Input dark calibration.",
144 storageClass="ExposureF",
145 dimensions=["instrument", "detector"],
146 isCalibration=True,
147 )
148 flat = cT.PrerequisiteInput(
149 name="flat",
150 doc="Input flat calibration.",
151 storageClass="ExposureF",
152 dimensions=["instrument", "physical_filter", "detector"],
153 isCalibration=True,
154 )
155 fringes = cT.PrerequisiteInput(
156 name="fringe",
157 doc="Input fringe calibration.",
158 storageClass="ExposureF",
159 dimensions=["instrument", "physical_filter", "detector"],
160 isCalibration=True,
161 )
162 strayLightData = cT.PrerequisiteInput(
163 name='yBackground',
164 doc="Input stray light calibration.",
165 storageClass="StrayLightData",
166 dimensions=["instrument", "physical_filter", "detector"],
167 isCalibration=True,
168 )
169 bfKernel = cT.PrerequisiteInput(
170 name='bfKernel',
171 doc="Input brighter-fatter kernel.",
172 storageClass="NumpyArray",
173 dimensions=["instrument"],
174 isCalibration=True,
175 )
176 newBFKernel = cT.PrerequisiteInput(
177 name='brighterFatterKernel',
178 doc="Newer complete kernel + gain solutions.",
179 storageClass="BrighterFatterKernel",
180 dimensions=["instrument", "detector"],
181 isCalibration=True,
182 )
183 defects = cT.PrerequisiteInput(
184 name='defects',
185 doc="Input defect tables.",
186 storageClass="Defects",
187 dimensions=["instrument", "detector"],
188 isCalibration=True,
189 )
190 linearizer = cT.PrerequisiteInput(
191 name='linearizer',
192 storageClass="Linearizer",
193 doc="Linearity correction calibration.",
194 dimensions=["instrument", "detector"],
195 isCalibration=True,
196 )
197 opticsTransmission = cT.PrerequisiteInput(
198 name="transmission_optics",
199 storageClass="TransmissionCurve",
200 doc="Transmission curve due to the optics.",
201 dimensions=["instrument"],
202 isCalibration=True,
203 )
204 filterTransmission = cT.PrerequisiteInput(
205 name="transmission_filter",
206 storageClass="TransmissionCurve",
207 doc="Transmission curve due to the filter.",
208 dimensions=["instrument", "physical_filter"],
209 isCalibration=True,
210 )
211 sensorTransmission = cT.PrerequisiteInput(
212 name="transmission_sensor",
213 storageClass="TransmissionCurve",
214 doc="Transmission curve due to the sensor.",
215 dimensions=["instrument", "detector"],
216 isCalibration=True,
217 )
218 atmosphereTransmission = cT.PrerequisiteInput(
219 name="transmission_atmosphere",
220 storageClass="TransmissionCurve",
221 doc="Transmission curve due to the atmosphere.",
222 dimensions=["instrument"],
223 isCalibration=True,
224 )
225 illumMaskedImage = cT.PrerequisiteInput(
226 name="illum",
227 doc="Input illumination correction.",
228 storageClass="MaskedImageF",
229 dimensions=["instrument", "physical_filter", "detector"],
230 isCalibration=True,
231 )
233 outputExposure = cT.Output(
234 name='postISRCCD',
235 doc="Output ISR processed exposure.",
236 storageClass="Exposure",
237 dimensions=["instrument", "exposure", "detector"],
238 )
239 preInterpExposure = cT.Output(
240 name='preInterpISRCCD',
241 doc="Output ISR processed exposure, with pixels left uninterpolated.",
242 storageClass="ExposureF",
243 dimensions=["instrument", "exposure", "detector"],
244 )
245 outputOssThumbnail = cT.Output(
246 name="OssThumb",
247 doc="Output Overscan-subtracted thumbnail image.",
248 storageClass="Thumbnail",
249 dimensions=["instrument", "exposure", "detector"],
250 )
251 outputFlattenedThumbnail = cT.Output(
252 name="FlattenedThumb",
253 doc="Output flat-corrected thumbnail image.",
254 storageClass="Thumbnail",
255 dimensions=["instrument", "exposure", "detector"],
256 )
258 def __init__(self, *, config=None):
259 super().__init__(config=config)
261 if config.doBias is not True:
262 self.prerequisiteInputs.discard("bias")
263 if config.doLinearize is not True:
264 self.prerequisiteInputs.discard("linearizer")
265 if config.doCrosstalk is not True:
266 self.inputs.discard("crosstalkSources")
267 self.prerequisiteInputs.discard("crosstalk")
268 if config.doBrighterFatter is not True:
269 self.prerequisiteInputs.discard("bfKernel")
270 self.prerequisiteInputs.discard("newBFKernel")
271 if config.doDefect is not True:
272 self.prerequisiteInputs.discard("defects")
273 if config.doDark is not True:
274 self.prerequisiteInputs.discard("dark")
275 if config.doFlat is not True:
276 self.prerequisiteInputs.discard("flat")
277 if config.doAttachTransmissionCurve is not True:
278 self.prerequisiteInputs.discard("opticsTransmission")
279 self.prerequisiteInputs.discard("filterTransmission")
280 self.prerequisiteInputs.discard("sensorTransmission")
281 self.prerequisiteInputs.discard("atmosphereTransmission")
282 if config.doUseOpticsTransmission is not True:
283 self.prerequisiteInputs.discard("opticsTransmission")
284 if config.doUseFilterTransmission is not True:
285 self.prerequisiteInputs.discard("filterTransmission")
286 if config.doUseSensorTransmission is not True:
287 self.prerequisiteInputs.discard("sensorTransmission")
288 if config.doUseAtmosphereTransmission is not True:
289 self.prerequisiteInputs.discard("atmosphereTransmission")
290 if config.doIlluminationCorrection is not True:
291 self.prerequisiteInputs.discard("illumMaskedImage")
293 if config.doWrite is not True:
294 self.outputs.discard("outputExposure")
295 self.outputs.discard("preInterpExposure")
296 self.outputs.discard("outputFlattenedThumbnail")
297 self.outputs.discard("outputOssThumbnail")
298 if config.doSaveInterpPixels is not True:
299 self.outputs.discard("preInterpExposure")
300 if config.qa.doThumbnailOss is not True:
301 self.outputs.discard("outputOssThumbnail")
302 if config.qa.doThumbnailFlattened is not True:
303 self.outputs.discard("outputFlattenedThumbnail")
306class IsrTaskConfig(pipeBase.PipelineTaskConfig,
307 pipelineConnections=IsrTaskConnections):
308 """Configuration parameters for IsrTask.
310 Items are grouped in the order in which they are executed by the task.
311 """
312 datasetType = pexConfig.Field(
313 dtype=str,
314 doc="Dataset type for input data; users will typically leave this alone, "
315 "but camera-specific ISR tasks will override it",
316 default="raw",
317 )
319 fallbackFilterName = pexConfig.Field(
320 dtype=str,
321 doc="Fallback default filter name for calibrations.",
322 optional=True
323 )
324 useFallbackDate = pexConfig.Field(
325 dtype=bool,
326 doc="Pass observation date when using fallback filter.",
327 default=False,
328 )
329 expectWcs = pexConfig.Field(
330 dtype=bool,
331 default=True,
332 doc="Expect input science images to have a WCS (set False for e.g. spectrographs)."
333 )
334 fwhm = pexConfig.Field(
335 dtype=float,
336 doc="FWHM of PSF in arcseconds.",
337 default=1.0,
338 )
339 qa = pexConfig.ConfigField(
340 dtype=isrQa.IsrQaConfig,
341 doc="QA related configuration options.",
342 )
344 # Image conversion configuration
345 doConvertIntToFloat = pexConfig.Field(
346 dtype=bool,
347 doc="Convert integer raw images to floating point values?",
348 default=True,
349 )
351 # Saturated pixel handling.
352 doSaturation = pexConfig.Field(
353 dtype=bool,
354 doc="Mask saturated pixels? NB: this is totally independent of the"
355 " interpolation option - this is ONLY setting the bits in the mask."
356 " To have them interpolated make sure doSaturationInterpolation=True",
357 default=True,
358 )
359 saturatedMaskName = pexConfig.Field(
360 dtype=str,
361 doc="Name of mask plane to use in saturation detection and interpolation",
362 default="SAT",
363 )
364 saturation = pexConfig.Field(
365 dtype=float,
366 doc="The saturation level to use if no Detector is present in the Exposure (ignored if NaN)",
367 default=float("NaN"),
368 )
369 growSaturationFootprintSize = pexConfig.Field(
370 dtype=int,
371 doc="Number of pixels by which to grow the saturation footprints",
372 default=1,
373 )
375 # Suspect pixel handling.
376 doSuspect = pexConfig.Field(
377 dtype=bool,
378 doc="Mask suspect pixels?",
379 default=False,
380 )
381 suspectMaskName = pexConfig.Field(
382 dtype=str,
383 doc="Name of mask plane to use for suspect pixels",
384 default="SUSPECT",
385 )
386 numEdgeSuspect = pexConfig.Field(
387 dtype=int,
388 doc="Number of edge pixels to be flagged as untrustworthy.",
389 default=0,
390 )
391 edgeMaskLevel = pexConfig.ChoiceField(
392 dtype=str,
393 doc="Mask edge pixels in which coordinate frame: DETECTOR or AMP?",
394 default="DETECTOR",
395 allowed={
396 'DETECTOR': 'Mask only the edges of the full detector.',
397 'AMP': 'Mask edges of each amplifier.',
398 },
399 )
401 # Initial masking options.
402 doSetBadRegions = pexConfig.Field(
403 dtype=bool,
404 doc="Should we set the level of all BAD patches of the chip to the chip's average value?",
405 default=True,
406 )
407 badStatistic = pexConfig.ChoiceField(
408 dtype=str,
409 doc="How to estimate the average value for BAD regions.",
410 default='MEANCLIP',
411 allowed={
412 "MEANCLIP": "Correct using the (clipped) mean of good data",
413 "MEDIAN": "Correct using the median of the good data",
414 },
415 )
417 # Overscan subtraction configuration.
418 doOverscan = pexConfig.Field(
419 dtype=bool,
420 doc="Do overscan subtraction?",
421 default=True,
422 )
423 overscan = pexConfig.ConfigurableField(
424 target=OverscanCorrectionTask,
425 doc="Overscan subtraction task for image segments.",
426 )
428 overscanFitType = pexConfig.ChoiceField(
429 dtype=str,
430 doc="The method for fitting the overscan bias level.",
431 default='MEDIAN',
432 allowed={
433 "POLY": "Fit ordinary polynomial to the longest axis of the overscan region",
434 "CHEB": "Fit Chebyshev polynomial to the longest axis of the overscan region",
435 "LEG": "Fit Legendre polynomial to the longest axis of the overscan region",
436 "NATURAL_SPLINE": "Fit natural spline to the longest axis of the overscan region",
437 "CUBIC_SPLINE": "Fit cubic spline to the longest axis of the overscan region",
438 "AKIMA_SPLINE": "Fit Akima spline to the longest axis of the overscan region",
439 "MEAN": "Correct using the mean of the overscan region",
440 "MEANCLIP": "Correct using a clipped mean of the overscan region",
441 "MEDIAN": "Correct using the median of the overscan region",
442 "MEDIAN_PER_ROW": "Correct using the median per row of the overscan region",
443 },
444 deprecated=("Please configure overscan via the OverscanCorrectionConfig interface."
445 " This option will no longer be used, and will be removed after v20.")
446 )
447 overscanOrder = pexConfig.Field(
448 dtype=int,
449 doc=("Order of polynomial or to fit if overscan fit type is a polynomial, "
450 "or number of spline knots if overscan fit type is a spline."),
451 default=1,
452 deprecated=("Please configure overscan via the OverscanCorrectionConfig interface."
453 " This option will no longer be used, and will be removed after v20.")
454 )
455 overscanNumSigmaClip = pexConfig.Field(
456 dtype=float,
457 doc="Rejection threshold (sigma) for collapsing overscan before fit",
458 default=3.0,
459 deprecated=("Please configure overscan via the OverscanCorrectionConfig interface."
460 " This option will no longer be used, and will be removed after v20.")
461 )
462 overscanIsInt = pexConfig.Field(
463 dtype=bool,
464 doc="Treat overscan as an integer image for purposes of overscan.FitType=MEDIAN"
465 " and overscan.FitType=MEDIAN_PER_ROW.",
466 default=True,
467 deprecated=("Please configure overscan via the OverscanCorrectionConfig interface."
468 " This option will no longer be used, and will be removed after v20.")
469 )
470 # These options do not get deprecated, as they define how we slice up the image data.
471 overscanNumLeadingColumnsToSkip = pexConfig.Field(
472 dtype=int,
473 doc="Number of columns to skip in overscan, i.e. those closest to amplifier",
474 default=0,
475 )
476 overscanNumTrailingColumnsToSkip = pexConfig.Field(
477 dtype=int,
478 doc="Number of columns to skip in overscan, i.e. those farthest from amplifier",
479 default=0,
480 )
481 overscanMaxDev = pexConfig.Field( 481 ↛ exitline 481 didn't jump to the function exit
482 dtype=float,
483 doc="Maximum deviation from the median for overscan",
484 default=1000.0, check=lambda x: x > 0
485 )
486 overscanBiasJump = pexConfig.Field(
487 dtype=bool,
488 doc="Fit the overscan in a piecewise-fashion to correct for bias jumps?",
489 default=False,
490 )
491 overscanBiasJumpKeyword = pexConfig.Field(
492 dtype=str,
493 doc="Header keyword containing information about devices.",
494 default="NO_SUCH_KEY",
495 )
496 overscanBiasJumpDevices = pexConfig.ListField(
497 dtype=str,
498 doc="List of devices that need piecewise overscan correction.",
499 default=(),
500 )
501 overscanBiasJumpLocation = pexConfig.Field(
502 dtype=int,
503 doc="Location of bias jump along y-axis.",
504 default=0,
505 )
507 # Amplifier to CCD assembly configuration
508 doAssembleCcd = pexConfig.Field(
509 dtype=bool,
510 default=True,
511 doc="Assemble amp-level exposures into a ccd-level exposure?"
512 )
513 assembleCcd = pexConfig.ConfigurableField(
514 target=AssembleCcdTask,
515 doc="CCD assembly task",
516 )
518 # General calibration configuration.
519 doAssembleIsrExposures = pexConfig.Field(
520 dtype=bool,
521 default=False,
522 doc="Assemble amp-level calibration exposures into ccd-level exposure?"
523 )
524 doTrimToMatchCalib = pexConfig.Field(
525 dtype=bool,
526 default=False,
527 doc="Trim raw data to match calibration bounding boxes?"
528 )
530 # Bias subtraction.
531 doBias = pexConfig.Field(
532 dtype=bool,
533 doc="Apply bias frame correction?",
534 default=True,
535 )
536 biasDataProductName = pexConfig.Field(
537 dtype=str,
538 doc="Name of the bias data product",
539 default="bias",
540 )
541 doBiasBeforeOverscan = pexConfig.Field(
542 dtype=bool,
543 doc="Reverse order of overscan and bias correction.",
544 default=False
545 )
547 # Variance construction
548 doVariance = pexConfig.Field(
549 dtype=bool,
550 doc="Calculate variance?",
551 default=True
552 )
553 gain = pexConfig.Field(
554 dtype=float,
555 doc="The gain to use if no Detector is present in the Exposure (ignored if NaN)",
556 default=float("NaN"),
557 )
558 readNoise = pexConfig.Field(
559 dtype=float,
560 doc="The read noise to use if no Detector is present in the Exposure",
561 default=0.0,
562 )
563 doEmpiricalReadNoise = pexConfig.Field(
564 dtype=bool,
565 default=False,
566 doc="Calculate empirical read noise instead of value from AmpInfo data?"
567 )
569 # Linearization.
570 doLinearize = pexConfig.Field(
571 dtype=bool,
572 doc="Correct for nonlinearity of the detector's response?",
573 default=True,
574 )
576 # Crosstalk.
577 doCrosstalk = pexConfig.Field(
578 dtype=bool,
579 doc="Apply intra-CCD crosstalk correction?",
580 default=False,
581 )
582 doCrosstalkBeforeAssemble = pexConfig.Field(
583 dtype=bool,
584 doc="Apply crosstalk correction before CCD assembly, and before trimming?",
585 default=False,
586 )
587 crosstalk = pexConfig.ConfigurableField(
588 target=CrosstalkTask,
589 doc="Intra-CCD crosstalk correction",
590 )
592 # Masking options.
593 doDefect = pexConfig.Field(
594 dtype=bool,
595 doc="Apply correction for CCD defects, e.g. hot pixels?",
596 default=True,
597 )
598 doNanMasking = pexConfig.Field(
599 dtype=bool,
600 doc="Mask non-finite (NAN, inf) pixels?",
601 default=True,
602 )
603 doWidenSaturationTrails = pexConfig.Field(
604 dtype=bool,
605 doc="Widen bleed trails based on their width?",
606 default=True
607 )
609 # Brighter-Fatter correction.
610 doBrighterFatter = pexConfig.Field(
611 dtype=bool,
612 default=False,
613 doc="Apply the brighter-fatter correction?"
614 )
615 brighterFatterLevel = pexConfig.ChoiceField(
616 dtype=str,
617 default="DETECTOR",
618 doc="The level at which to correct for brighter-fatter.",
619 allowed={
620 "AMP": "Every amplifier treated separately.",
621 "DETECTOR": "One kernel per detector",
622 }
623 )
624 brighterFatterMaxIter = pexConfig.Field(
625 dtype=int,
626 default=10,
627 doc="Maximum number of iterations for the brighter-fatter correction"
628 )
629 brighterFatterThreshold = pexConfig.Field(
630 dtype=float,
631 default=1000,
632 doc="Threshold used to stop iterating the brighter-fatter correction. It is the "
633 "absolute value of the difference between the current corrected image and the one "
634 "from the previous iteration summed over all the pixels."
635 )
636 brighterFatterApplyGain = pexConfig.Field(
637 dtype=bool,
638 default=True,
639 doc="Should the gain be applied when applying the brighter-fatter correction?"
640 )
641 brighterFatterMaskListToInterpolate = pexConfig.ListField(
642 dtype=str,
643 doc="List of mask planes that should be interpolated over when applying the brighter-fatter "
644 "correction.",
645 default=["SAT", "BAD", "NO_DATA", "UNMASKEDNAN"],
646 )
647 brighterFatterMaskGrowSize = pexConfig.Field(
648 dtype=int,
649 default=0,
650 doc="Number of pixels to grow the masks listed in config.brighterFatterMaskListToInterpolate "
651 "when brighter-fatter correction is applied."
652 )
654 # Dark subtraction.
655 doDark = pexConfig.Field(
656 dtype=bool,
657 doc="Apply dark frame correction?",
658 default=True,
659 )
660 darkDataProductName = pexConfig.Field(
661 dtype=str,
662 doc="Name of the dark data product",
663 default="dark",
664 )
666 # Camera-specific stray light removal.
667 doStrayLight = pexConfig.Field(
668 dtype=bool,
669 doc="Subtract stray light in the y-band (due to encoder LEDs)?",
670 default=False,
671 )
672 strayLight = pexConfig.ConfigurableField(
673 target=StrayLightTask,
674 doc="y-band stray light correction"
675 )
677 # Flat correction.
678 doFlat = pexConfig.Field(
679 dtype=bool,
680 doc="Apply flat field correction?",
681 default=True,
682 )
683 flatDataProductName = pexConfig.Field(
684 dtype=str,
685 doc="Name of the flat data product",
686 default="flat",
687 )
688 flatScalingType = pexConfig.ChoiceField(
689 dtype=str,
690 doc="The method for scaling the flat on the fly.",
691 default='USER',
692 allowed={
693 "USER": "Scale by flatUserScale",
694 "MEAN": "Scale by the inverse of the mean",
695 "MEDIAN": "Scale by the inverse of the median",
696 },
697 )
698 flatUserScale = pexConfig.Field(
699 dtype=float,
700 doc="If flatScalingType is 'USER' then scale flat by this amount; ignored otherwise",
701 default=1.0,
702 )
703 doTweakFlat = pexConfig.Field(
704 dtype=bool,
705 doc="Tweak flats to match observed amplifier ratios?",
706 default=False
707 )
709 # Amplifier normalization based on gains instead of using flats configuration.
710 doApplyGains = pexConfig.Field(
711 dtype=bool,
712 doc="Correct the amplifiers for their gains instead of applying flat correction",
713 default=False,
714 )
715 normalizeGains = pexConfig.Field(
716 dtype=bool,
717 doc="Normalize all the amplifiers in each CCD to have the same median value.",
718 default=False,
719 )
721 # Fringe correction.
722 doFringe = pexConfig.Field(
723 dtype=bool,
724 doc="Apply fringe correction?",
725 default=True,
726 )
727 fringe = pexConfig.ConfigurableField(
728 target=FringeTask,
729 doc="Fringe subtraction task",
730 )
731 fringeAfterFlat = pexConfig.Field(
732 dtype=bool,
733 doc="Do fringe subtraction after flat-fielding?",
734 default=True,
735 )
737 # Initial CCD-level background statistics options.
738 doMeasureBackground = pexConfig.Field(
739 dtype=bool,
740 doc="Measure the background level on the reduced image?",
741 default=False,
742 )
744 # Camera-specific masking configuration.
745 doCameraSpecificMasking = pexConfig.Field(
746 dtype=bool,
747 doc="Mask camera-specific bad regions?",
748 default=False,
749 )
750 masking = pexConfig.ConfigurableField(
751 target=MaskingTask,
752 doc="Masking task."
753 )
755 # Interpolation options.
757 doInterpolate = pexConfig.Field(
758 dtype=bool,
759 doc="Interpolate masked pixels?",
760 default=True,
761 )
762 doSaturationInterpolation = pexConfig.Field(
763 dtype=bool,
764 doc="Perform interpolation over pixels masked as saturated?"
765 " NB: This is independent of doSaturation; if that is False this plane"
766 " will likely be blank, resulting in a no-op here.",
767 default=True,
768 )
769 doNanInterpolation = pexConfig.Field(
770 dtype=bool,
771 doc="Perform interpolation over pixels masked as NaN?"
772 " NB: This is independent of doNanMasking; if that is False this plane"
773 " will likely be blank, resulting in a no-op here.",
774 default=True,
775 )
776 doNanInterpAfterFlat = pexConfig.Field(
777 dtype=bool,
778 doc=("If True, ensure we interpolate NaNs after flat-fielding, even if we "
779 "also have to interpolate them before flat-fielding."),
780 default=False,
781 )
782 maskListToInterpolate = pexConfig.ListField(
783 dtype=str,
784 doc="List of mask planes that should be interpolated.",
785 default=['SAT', 'BAD'],
786 )
787 doSaveInterpPixels = pexConfig.Field(
788 dtype=bool,
789 doc="Save a copy of the pre-interpolated pixel values?",
790 default=False,
791 )
793 # Default photometric calibration options.
794 fluxMag0T1 = pexConfig.DictField(
795 keytype=str,
796 itemtype=float,
797 doc="The approximate flux of a zero-magnitude object in a one-second exposure, per filter.",
798 default=dict((f, pow(10.0, 0.4*m)) for f, m in (("Unknown", 28.0),
799 ))
800 )
801 defaultFluxMag0T1 = pexConfig.Field(
802 dtype=float,
803 doc="Default value for fluxMag0T1 (for an unrecognized filter).",
804 default=pow(10.0, 0.4*28.0)
805 )
807 # Vignette correction configuration.
808 doVignette = pexConfig.Field(
809 dtype=bool,
810 doc="Apply vignetting parameters?",
811 default=False,
812 )
813 vignette = pexConfig.ConfigurableField(
814 target=VignetteTask,
815 doc="Vignetting task.",
816 )
818 # Transmission curve configuration.
819 doAttachTransmissionCurve = pexConfig.Field(
820 dtype=bool,
821 default=False,
822 doc="Construct and attach a wavelength-dependent throughput curve for this CCD image?"
823 )
824 doUseOpticsTransmission = pexConfig.Field(
825 dtype=bool,
826 default=True,
827 doc="Load and use transmission_optics (if doAttachTransmissionCurve is True)?"
828 )
829 doUseFilterTransmission = pexConfig.Field(
830 dtype=bool,
831 default=True,
832 doc="Load and use transmission_filter (if doAttachTransmissionCurve is True)?"
833 )
834 doUseSensorTransmission = pexConfig.Field(
835 dtype=bool,
836 default=True,
837 doc="Load and use transmission_sensor (if doAttachTransmissionCurve is True)?"
838 )
839 doUseAtmosphereTransmission = pexConfig.Field(
840 dtype=bool,
841 default=True,
842 doc="Load and use transmission_atmosphere (if doAttachTransmissionCurve is True)?"
843 )
845 # Illumination correction.
846 doIlluminationCorrection = pexConfig.Field(
847 dtype=bool,
848 default=False,
849 doc="Perform illumination correction?"
850 )
851 illuminationCorrectionDataProductName = pexConfig.Field(
852 dtype=str,
853 doc="Name of the illumination correction data product.",
854 default="illumcor",
855 )
856 illumScale = pexConfig.Field(
857 dtype=float,
858 doc="Scale factor for the illumination correction.",
859 default=1.0,
860 )
861 illumFilters = pexConfig.ListField(
862 dtype=str,
863 default=[],
864 doc="Only perform illumination correction for these filters."
865 )
867 # Write the outputs to disk. If ISR is run as a subtask, this may not be needed.
868 doWrite = pexConfig.Field(
869 dtype=bool,
870 doc="Persist postISRCCD?",
871 default=True,
872 )
874 def validate(self):
875 super().validate()
876 if self.doFlat and self.doApplyGains:
877 raise ValueError("You may not specify both doFlat and doApplyGains")
878 if self.doBiasBeforeOverscan and self.doTrimToMatchCalib:
879 raise ValueError("You may not specify both doBiasBeforeOverscan and doTrimToMatchCalib")
880 if self.doSaturationInterpolation and self.saturatedMaskName not in self.maskListToInterpolate:
881 self.maskListToInterpolate.append(self.saturatedMaskName)
882 if not self.doSaturationInterpolation and self.saturatedMaskName in self.maskListToInterpolate:
883 self.maskListToInterpolate.remove(self.saturatedMaskName)
884 if self.doNanInterpolation and "UNMASKEDNAN" not in self.maskListToInterpolate:
885 self.maskListToInterpolate.append("UNMASKEDNAN")
888class IsrTask(pipeBase.PipelineTask, pipeBase.CmdLineTask):
889 """Apply common instrument signature correction algorithms to a raw frame.
891 The process for correcting imaging data is very similar from
892 camera to camera. This task provides a vanilla implementation of
893 doing these corrections, including the ability to turn certain
894 corrections off if they are not needed. The inputs to the primary
895 method, `run()`, are a raw exposure to be corrected and the
896 calibration data products. The raw input is a single chip sized
897 mosaic of all amps including overscans and other non-science
898 pixels. The method `runDataRef()` identifies and defines the
899 calibration data products, and is intended for use by a
900 `lsst.pipe.base.cmdLineTask.CmdLineTask` and takes as input only a
901 `daf.persistence.butlerSubset.ButlerDataRef`. This task may be
902 subclassed for different camera, although the most camera specific
903 methods have been split into subtasks that can be redirected
904 appropriately.
906 The __init__ method sets up the subtasks for ISR processing, using
907 the defaults from `lsst.ip.isr`.
909 Parameters
910 ----------
911 args : `list`
912 Positional arguments passed to the Task constructor. None used at this time.
913 kwargs : `dict`, optional
914 Keyword arguments passed on to the Task constructor. None used at this time.
915 """
916 ConfigClass = IsrTaskConfig
917 _DefaultName = "isr"
919 def __init__(self, **kwargs):
920 super().__init__(**kwargs)
921 self.makeSubtask("assembleCcd")
922 self.makeSubtask("crosstalk")
923 self.makeSubtask("strayLight")
924 self.makeSubtask("fringe")
925 self.makeSubtask("masking")
926 self.makeSubtask("overscan")
927 self.makeSubtask("vignette")
929 def runQuantum(self, butlerQC, inputRefs, outputRefs):
930 inputs = butlerQC.get(inputRefs)
932 try:
933 inputs['detectorNum'] = inputRefs.ccdExposure.dataId['detector']
934 except Exception as e:
935 raise ValueError("Failure to find valid detectorNum value for Dataset %s: %s." %
936 (inputRefs, e))
938 inputs['isGen3'] = True
940 detector = inputs['ccdExposure'].getDetector()
942 if self.config.doCrosstalk is True:
943 # Crosstalk sources need to be defined by the pipeline
944 # yaml if they exist.
945 if 'crosstalk' in inputs and inputs['crosstalk'] is not None:
946 if not isinstance(inputs['crosstalk'], CrosstalkCalib):
947 inputs['crosstalk'] = CrosstalkCalib.fromTable(inputs['crosstalk'])
948 else:
949 coeffVector = (self.config.crosstalk.crosstalkValues
950 if self.config.crosstalk.useConfigCoefficients else None)
951 crosstalkCalib = CrosstalkCalib().fromDetector(detector, coeffVector=coeffVector)
952 inputs['crosstalk'] = crosstalkCalib
953 if inputs['crosstalk'].interChip and len(inputs['crosstalk'].interChip) > 0:
954 if 'crosstalkSources' not in inputs:
955 self.log.warn("No crosstalkSources found for chip with interChip terms!")
957 if self.doLinearize(detector) is True:
958 if 'linearizer' in inputs:
959 if isinstance(inputs['linearizer'], dict):
960 linearizer = linearize.Linearizer(detector=detector, log=self.log)
961 linearizer.fromYaml(inputs['linearizer'])
962 self.log.warn("Dictionary linearizers will be deprecated in DM-28741.")
963 elif isinstance(inputs['linearizer'], numpy.ndarray):
964 linearizer = linearize.Linearizer(table=inputs.get('linearizer', None),
965 detector=detector,
966 log=self.log)
967 self.log.warn("Bare lookup table linearizers will be deprecated in DM-28741.")
968 else:
969 linearizer = inputs['linearizer']
970 linearizer.log = self.log
971 inputs['linearizer'] = linearizer
972 else:
973 inputs['linearizer'] = linearize.Linearizer(detector=detector, log=self.log)
974 self.log.warn("Constructing linearizer from cameraGeom information.")
976 if self.config.doDefect is True:
977 if "defects" in inputs and inputs['defects'] is not None:
978 # defects is loaded as a BaseCatalog with columns x0, y0, width, height.
979 # masking expects a list of defects defined by their bounding box
980 if not isinstance(inputs["defects"], Defects):
981 inputs["defects"] = Defects.fromTable(inputs["defects"])
983 # Load the correct style of brighter-fatter kernel, and repack
984 # the information as a numpy array.
985 if self.config.doBrighterFatter:
986 brighterFatterKernel = inputs.pop('newBFKernel', None)
987 if brighterFatterKernel is None:
988 brighterFatterKernel = inputs.get('bfKernel', None)
990 if brighterFatterKernel is not None and not isinstance(brighterFatterKernel, numpy.ndarray):
991 # This is a ISR calib kernel
992 detName = detector.getName()
993 level = brighterFatterKernel.level
995 # This is expected to be a dictionary of amp-wise gains.
996 inputs['bfGains'] = brighterFatterKernel.gain
997 if self.config.brighterFatterLevel == 'DETECTOR':
998 if level == 'DETECTOR':
999 if detName in brighterFatterKernel.detKernels:
1000 inputs['bfKernel'] = brighterFatterKernel.detKernels[detName]
1001 else:
1002 raise RuntimeError("Failed to extract kernel from new-style BF kernel.")
1003 elif level == 'AMP':
1004 self.log.warn("Making DETECTOR level kernel from AMP based brighter fatter kernels.")
1005 brighterFatterKernel.makeDetectorKernelFromAmpwiseKernels(detName)
1006 inputs['bfKernel'] = brighterFatterKernel.detKernels[detName]
1007 elif self.config.brighterFatterLevel == 'AMP':
1008 raise NotImplementedError("Per-amplifier brighter-fatter correction not implemented")
1010 if self.config.doFringe is True and self.fringe.checkFilter(inputs['ccdExposure']):
1011 expId = inputs['ccdExposure'].getInfo().getVisitInfo().getExposureId()
1012 inputs['fringes'] = self.fringe.loadFringes(inputs['fringes'],
1013 expId=expId,
1014 assembler=self.assembleCcd
1015 if self.config.doAssembleIsrExposures else None)
1016 else:
1017 inputs['fringes'] = pipeBase.Struct(fringes=None)
1019 if self.config.doStrayLight is True and self.strayLight.checkFilter(inputs['ccdExposure']):
1020 if 'strayLightData' not in inputs:
1021 inputs['strayLightData'] = None
1023 outputs = self.run(**inputs)
1024 butlerQC.put(outputs, outputRefs)
1026 def readIsrData(self, dataRef, rawExposure):
1027 """Retrieve necessary frames for instrument signature removal.
1029 Pre-fetching all required ISR data products limits the IO
1030 required by the ISR. Any conflict between the calibration data
1031 available and that needed for ISR is also detected prior to
1032 doing processing, allowing it to fail quickly.
1034 Parameters
1035 ----------
1036 dataRef : `daf.persistence.butlerSubset.ButlerDataRef`
1037 Butler reference of the detector data to be processed
1038 rawExposure : `afw.image.Exposure`
1039 The raw exposure that will later be corrected with the
1040 retrieved calibration data; should not be modified in this
1041 method.
1043 Returns
1044 -------
1045 result : `lsst.pipe.base.Struct`
1046 Result struct with components (which may be `None`):
1047 - ``bias``: bias calibration frame (`afw.image.Exposure`)
1048 - ``linearizer``: functor for linearization (`ip.isr.linearize.LinearizeBase`)
1049 - ``crosstalkSources``: list of possible crosstalk sources (`list`)
1050 - ``dark``: dark calibration frame (`afw.image.Exposure`)
1051 - ``flat``: flat calibration frame (`afw.image.Exposure`)
1052 - ``bfKernel``: Brighter-Fatter kernel (`numpy.ndarray`)
1053 - ``defects``: list of defects (`lsst.ip.isr.Defects`)
1054 - ``fringes``: `lsst.pipe.base.Struct` with components:
1055 - ``fringes``: fringe calibration frame (`afw.image.Exposure`)
1056 - ``seed``: random seed derived from the ccdExposureId for random
1057 number generator (`uint32`).
1058 - ``opticsTransmission``: `lsst.afw.image.TransmissionCurve`
1059 A ``TransmissionCurve`` that represents the throughput of the optics,
1060 to be evaluated in focal-plane coordinates.
1061 - ``filterTransmission`` : `lsst.afw.image.TransmissionCurve`
1062 A ``TransmissionCurve`` that represents the throughput of the filter
1063 itself, to be evaluated in focal-plane coordinates.
1064 - ``sensorTransmission`` : `lsst.afw.image.TransmissionCurve`
1065 A ``TransmissionCurve`` that represents the throughput of the sensor
1066 itself, to be evaluated in post-assembly trimmed detector coordinates.
1067 - ``atmosphereTransmission`` : `lsst.afw.image.TransmissionCurve`
1068 A ``TransmissionCurve`` that represents the throughput of the
1069 atmosphere, assumed to be spatially constant.
1070 - ``strayLightData`` : `object`
1071 An opaque object containing calibration information for
1072 stray-light correction. If `None`, no correction will be
1073 performed.
1074 - ``illumMaskedImage`` : illumination correction image (`lsst.afw.image.MaskedImage`)
1076 Raises
1077 ------
1078 NotImplementedError :
1079 Raised if a per-amplifier brighter-fatter kernel is requested by the configuration.
1080 """
1081 try:
1082 dateObs = rawExposure.getInfo().getVisitInfo().getDate()
1083 dateObs = dateObs.toPython().isoformat()
1084 except RuntimeError:
1085 self.log.warn("Unable to identify dateObs for rawExposure.")
1086 dateObs = None
1088 ccd = rawExposure.getDetector()
1089 filterLabel = rawExposure.getFilterLabel()
1090 rawExposure.mask.addMaskPlane("UNMASKEDNAN") # needed to match pre DM-15862 processing.
1091 biasExposure = (self.getIsrExposure(dataRef, self.config.biasDataProductName)
1092 if self.config.doBias else None)
1093 # immediate=True required for functors and linearizers are functors; see ticket DM-6515
1094 linearizer = (dataRef.get("linearizer", immediate=True)
1095 if self.doLinearize(ccd) else None)
1096 if linearizer is not None and not isinstance(linearizer, numpy.ndarray):
1097 linearizer.log = self.log
1098 if isinstance(linearizer, numpy.ndarray):
1099 linearizer = linearize.Linearizer(table=linearizer, detector=ccd)
1101 crosstalkCalib = None
1102 if self.config.doCrosstalk:
1103 try:
1104 crosstalkCalib = dataRef.get("crosstalk", immediate=True)
1105 except NoResults:
1106 coeffVector = (self.config.crosstalk.crosstalkValues
1107 if self.config.crosstalk.useConfigCoefficients else None)
1108 crosstalkCalib = CrosstalkCalib().fromDetector(ccd, coeffVector=coeffVector)
1109 crosstalkSources = (self.crosstalk.prepCrosstalk(dataRef, crosstalkCalib)
1110 if self.config.doCrosstalk else None)
1112 darkExposure = (self.getIsrExposure(dataRef, self.config.darkDataProductName)
1113 if self.config.doDark else None)
1114 flatExposure = (self.getIsrExposure(dataRef, self.config.flatDataProductName,
1115 dateObs=dateObs)
1116 if self.config.doFlat else None)
1118 brighterFatterKernel = None
1119 brighterFatterGains = None
1120 if self.config.doBrighterFatter is True:
1121 try:
1122 # Use the new-style cp_pipe version of the kernel if it exists
1123 # If using a new-style kernel, always use the self-consistent
1124 # gains, i.e. the ones inside the kernel object itself
1125 brighterFatterKernel = dataRef.get("brighterFatterKernel")
1126 brighterFatterGains = brighterFatterKernel.gain
1127 self.log.info("New style brighter-fatter kernel (brighterFatterKernel) loaded")
1128 except NoResults:
1129 try: # Fall back to the old-style numpy-ndarray style kernel if necessary.
1130 brighterFatterKernel = dataRef.get("bfKernel")
1131 self.log.info("Old style brighter-fatter kernel (np.array) loaded")
1132 except NoResults:
1133 brighterFatterKernel = None
1134 if brighterFatterKernel is not None and not isinstance(brighterFatterKernel, numpy.ndarray):
1135 # If the kernel is not an ndarray, it's the cp_pipe version
1136 # so extract the kernel for this detector, or raise an error
1137 if self.config.brighterFatterLevel == 'DETECTOR':
1138 if brighterFatterKernel.detectorKernel:
1139 brighterFatterKernel = brighterFatterKernel.detectorKernel[ccd.getId()]
1140 else:
1141 raise RuntimeError("Failed to extract kernel from new-style BF kernel.")
1142 else:
1143 # TODO DM-15631 for implementing this
1144 raise NotImplementedError("Per-amplifier brighter-fatter correction not implemented")
1146 defectList = (dataRef.get("defects")
1147 if self.config.doDefect else None)
1148 fringeStruct = (self.fringe.readFringes(dataRef, assembler=self.assembleCcd
1149 if self.config.doAssembleIsrExposures else None)
1150 if self.config.doFringe and self.fringe.checkFilter(rawExposure)
1151 else pipeBase.Struct(fringes=None))
1153 if self.config.doAttachTransmissionCurve:
1154 opticsTransmission = (dataRef.get("transmission_optics")
1155 if self.config.doUseOpticsTransmission else None)
1156 filterTransmission = (dataRef.get("transmission_filter")
1157 if self.config.doUseFilterTransmission else None)
1158 sensorTransmission = (dataRef.get("transmission_sensor")
1159 if self.config.doUseSensorTransmission else None)
1160 atmosphereTransmission = (dataRef.get("transmission_atmosphere")
1161 if self.config.doUseAtmosphereTransmission else None)
1162 else:
1163 opticsTransmission = None
1164 filterTransmission = None
1165 sensorTransmission = None
1166 atmosphereTransmission = None
1168 if self.config.doStrayLight:
1169 strayLightData = self.strayLight.readIsrData(dataRef, rawExposure)
1170 else:
1171 strayLightData = None
1173 illumMaskedImage = (self.getIsrExposure(dataRef,
1174 self.config.illuminationCorrectionDataProductName).getMaskedImage()
1175 if (self.config.doIlluminationCorrection
1176 and filterLabel in self.config.illumFilters)
1177 else None)
1179 # Struct should include only kwargs to run()
1180 return pipeBase.Struct(bias=biasExposure,
1181 linearizer=linearizer,
1182 crosstalk=crosstalkCalib,
1183 crosstalkSources=crosstalkSources,
1184 dark=darkExposure,
1185 flat=flatExposure,
1186 bfKernel=brighterFatterKernel,
1187 bfGains=brighterFatterGains,
1188 defects=defectList,
1189 fringes=fringeStruct,
1190 opticsTransmission=opticsTransmission,
1191 filterTransmission=filterTransmission,
1192 sensorTransmission=sensorTransmission,
1193 atmosphereTransmission=atmosphereTransmission,
1194 strayLightData=strayLightData,
1195 illumMaskedImage=illumMaskedImage
1196 )
1198 @pipeBase.timeMethod
1199 def run(self, ccdExposure, camera=None, bias=None, linearizer=None,
1200 crosstalk=None, crosstalkSources=None,
1201 dark=None, flat=None, bfKernel=None, bfGains=None, defects=None,
1202 fringes=pipeBase.Struct(fringes=None), opticsTransmission=None, filterTransmission=None,
1203 sensorTransmission=None, atmosphereTransmission=None,
1204 detectorNum=None, strayLightData=None, illumMaskedImage=None,
1205 isGen3=False,
1206 ):
1207 """Perform instrument signature removal on an exposure.
1209 Steps included in the ISR processing, in order performed, are:
1210 - saturation and suspect pixel masking
1211 - overscan subtraction
1212 - CCD assembly of individual amplifiers
1213 - bias subtraction
1214 - variance image construction
1215 - linearization of non-linear response
1216 - crosstalk masking
1217 - brighter-fatter correction
1218 - dark subtraction
1219 - fringe correction
1220 - stray light subtraction
1221 - flat correction
1222 - masking of known defects and camera specific features
1223 - vignette calculation
1224 - appending transmission curve and distortion model
1226 Parameters
1227 ----------
1228 ccdExposure : `lsst.afw.image.Exposure`
1229 The raw exposure that is to be run through ISR. The
1230 exposure is modified by this method.
1231 camera : `lsst.afw.cameraGeom.Camera`, optional
1232 The camera geometry for this exposure. Required if ``isGen3`` is
1233 `True` and one or more of ``ccdExposure``, ``bias``, ``dark``, or
1234 ``flat`` does not have an associated detector.
1235 bias : `lsst.afw.image.Exposure`, optional
1236 Bias calibration frame.
1237 linearizer : `lsst.ip.isr.linearize.LinearizeBase`, optional
1238 Functor for linearization.
1239 crosstalk : `lsst.ip.isr.crosstalk.CrosstalkCalib`, optional
1240 Calibration for crosstalk.
1241 crosstalkSources : `list`, optional
1242 List of possible crosstalk sources.
1243 dark : `lsst.afw.image.Exposure`, optional
1244 Dark calibration frame.
1245 flat : `lsst.afw.image.Exposure`, optional
1246 Flat calibration frame.
1247 bfKernel : `numpy.ndarray`, optional
1248 Brighter-fatter kernel.
1249 bfGains : `dict` of `float`, optional
1250 Gains used to override the detector's nominal gains for the
1251 brighter-fatter correction. A dict keyed by amplifier name for
1252 the detector in question.
1253 defects : `lsst.ip.isr.Defects`, optional
1254 List of defects.
1255 fringes : `lsst.pipe.base.Struct`, optional
1256 Struct containing the fringe correction data, with
1257 elements:
1258 - ``fringes``: fringe calibration frame (`afw.image.Exposure`)
1259 - ``seed``: random seed derived from the ccdExposureId for random
1260 number generator (`uint32`)
1261 opticsTransmission: `lsst.afw.image.TransmissionCurve`, optional
1262 A ``TransmissionCurve`` that represents the throughput of the optics,
1263 to be evaluated in focal-plane coordinates.
1264 filterTransmission : `lsst.afw.image.TransmissionCurve`
1265 A ``TransmissionCurve`` that represents the throughput of the filter
1266 itself, to be evaluated in focal-plane coordinates.
1267 sensorTransmission : `lsst.afw.image.TransmissionCurve`
1268 A ``TransmissionCurve`` that represents the throughput of the sensor
1269 itself, to be evaluated in post-assembly trimmed detector coordinates.
1270 atmosphereTransmission : `lsst.afw.image.TransmissionCurve`
1271 A ``TransmissionCurve`` that represents the throughput of the
1272 atmosphere, assumed to be spatially constant.
1273 detectorNum : `int`, optional
1274 The integer number for the detector to process.
1275 isGen3 : bool, optional
1276 Flag this call to run() as using the Gen3 butler environment.
1277 strayLightData : `object`, optional
1278 Opaque object containing calibration information for stray-light
1279 correction. If `None`, no correction will be performed.
1280 illumMaskedImage : `lsst.afw.image.MaskedImage`, optional
1281 Illumination correction image.
1283 Returns
1284 -------
1285 result : `lsst.pipe.base.Struct`
1286 Result struct with component:
1287 - ``exposure`` : `afw.image.Exposure`
1288 The fully ISR corrected exposure.
1289 - ``outputExposure`` : `afw.image.Exposure`
1290 An alias for `exposure`
1291 - ``ossThumb`` : `numpy.ndarray`
1292 Thumbnail image of the exposure after overscan subtraction.
1293 - ``flattenedThumb`` : `numpy.ndarray`
1294 Thumbnail image of the exposure after flat-field correction.
1296 Raises
1297 ------
1298 RuntimeError
1299 Raised if a configuration option is set to True, but the
1300 required calibration data has not been specified.
1302 Notes
1303 -----
1304 The current processed exposure can be viewed by setting the
1305 appropriate lsstDebug entries in the `debug.display`
1306 dictionary. The names of these entries correspond to some of
1307 the IsrTaskConfig Boolean options, with the value denoting the
1308 frame to use. The exposure is shown inside the matching
1309 option check and after the processing of that step has
1310 finished. The steps with debug points are:
1312 doAssembleCcd
1313 doBias
1314 doCrosstalk
1315 doBrighterFatter
1316 doDark
1317 doFringe
1318 doStrayLight
1319 doFlat
1321 In addition, setting the "postISRCCD" entry displays the
1322 exposure after all ISR processing has finished.
1324 """
1326 if isGen3 is True:
1327 # Gen3 currently cannot automatically do configuration overrides.
1328 # DM-15257 looks to discuss this issue.
1329 # Configure input exposures;
1330 if detectorNum is None:
1331 raise RuntimeError("Must supply the detectorNum if running as Gen3.")
1333 ccdExposure = self.ensureExposure(ccdExposure, camera, detectorNum)
1334 bias = self.ensureExposure(bias, camera, detectorNum)
1335 dark = self.ensureExposure(dark, camera, detectorNum)
1336 flat = self.ensureExposure(flat, camera, detectorNum)
1337 else:
1338 if isinstance(ccdExposure, ButlerDataRef):
1339 return self.runDataRef(ccdExposure)
1341 ccd = ccdExposure.getDetector()
1342 filterLabel = ccdExposure.getFilterLabel()
1344 if not ccd:
1345 assert not self.config.doAssembleCcd, "You need a Detector to run assembleCcd."
1346 ccd = [FakeAmp(ccdExposure, self.config)]
1348 # Validate Input
1349 if self.config.doBias and bias is None:
1350 raise RuntimeError("Must supply a bias exposure if config.doBias=True.")
1351 if self.doLinearize(ccd) and linearizer is None:
1352 raise RuntimeError("Must supply a linearizer if config.doLinearize=True for this detector.")
1353 if self.config.doBrighterFatter and bfKernel is None:
1354 raise RuntimeError("Must supply a kernel if config.doBrighterFatter=True.")
1355 if self.config.doDark and dark is None:
1356 raise RuntimeError("Must supply a dark exposure if config.doDark=True.")
1357 if self.config.doFlat and flat is None:
1358 raise RuntimeError("Must supply a flat exposure if config.doFlat=True.")
1359 if self.config.doDefect and defects is None:
1360 raise RuntimeError("Must supply defects if config.doDefect=True.")
1361 if (self.config.doFringe and filterLabel in self.fringe.config.filters
1362 and fringes.fringes is None):
1363 # The `fringes` object needs to be a pipeBase.Struct, as
1364 # we use it as a `dict` for the parameters of
1365 # `FringeTask.run()`. The `fringes.fringes` `list` may
1366 # not be `None` if `doFringe=True`. Otherwise, raise.
1367 raise RuntimeError("Must supply fringe exposure as a pipeBase.Struct.")
1368 if (self.config.doIlluminationCorrection and filterLabel in self.config.illumFilters
1369 and illumMaskedImage is None):
1370 raise RuntimeError("Must supply an illumcor if config.doIlluminationCorrection=True.")
1372 # Begin ISR processing.
1373 if self.config.doConvertIntToFloat:
1374 self.log.info("Converting exposure to floating point values.")
1375 ccdExposure = self.convertIntToFloat(ccdExposure)
1377 if self.config.doBias and self.config.doBiasBeforeOverscan:
1378 self.log.info("Applying bias correction.")
1379 isrFunctions.biasCorrection(ccdExposure.getMaskedImage(), bias.getMaskedImage(),
1380 trimToFit=self.config.doTrimToMatchCalib)
1381 self.debugView(ccdExposure, "doBias")
1383 # Amplifier level processing.
1384 overscans = []
1385 for amp in ccd:
1386 # if ccdExposure is one amp, check for coverage to prevent performing ops multiple times
1387 if ccdExposure.getBBox().contains(amp.getBBox()):
1388 # Check for fully masked bad amplifiers, and generate masks for SUSPECT and SATURATED values.
1389 badAmp = self.maskAmplifier(ccdExposure, amp, defects)
1391 if self.config.doOverscan and not badAmp:
1392 # Overscan correction on amp-by-amp basis.
1393 overscanResults = self.overscanCorrection(ccdExposure, amp)
1394 self.log.debug("Corrected overscan for amplifier %s.", amp.getName())
1395 if overscanResults is not None and \
1396 self.config.qa is not None and self.config.qa.saveStats is True:
1397 if isinstance(overscanResults.overscanFit, float):
1398 qaMedian = overscanResults.overscanFit
1399 qaStdev = float("NaN")
1400 else:
1401 qaStats = afwMath.makeStatistics(overscanResults.overscanFit,
1402 afwMath.MEDIAN | afwMath.STDEVCLIP)
1403 qaMedian = qaStats.getValue(afwMath.MEDIAN)
1404 qaStdev = qaStats.getValue(afwMath.STDEVCLIP)
1406 self.metadata.set(f"FIT MEDIAN {amp.getName()}", qaMedian)
1407 self.metadata.set(f"FIT STDEV {amp.getName()}", qaStdev)
1408 self.log.debug(" Overscan stats for amplifer %s: %f +/- %f",
1409 amp.getName(), qaMedian, qaStdev)
1411 # Residuals after overscan correction
1412 qaStatsAfter = afwMath.makeStatistics(overscanResults.overscanImage,
1413 afwMath.MEDIAN | afwMath.STDEVCLIP)
1414 qaMedianAfter = qaStatsAfter.getValue(afwMath.MEDIAN)
1415 qaStdevAfter = qaStatsAfter.getValue(afwMath.STDEVCLIP)
1417 self.metadata.set(f"RESIDUAL MEDIAN {amp.getName()}", qaMedianAfter)
1418 self.metadata.set(f"RESIDUAL STDEV {amp.getName()}", qaStdevAfter)
1419 self.log.debug(" Overscan stats for amplifer %s after correction: %f +/- %f",
1420 amp.getName(), qaMedianAfter, qaStdevAfter)
1422 ccdExposure.getMetadata().set('OVERSCAN', "Overscan corrected")
1423 else:
1424 if badAmp:
1425 self.log.warn("Amplifier %s is bad.", amp.getName())
1426 overscanResults = None
1428 overscans.append(overscanResults if overscanResults is not None else None)
1429 else:
1430 self.log.info("Skipped OSCAN for %s.", amp.getName())
1432 if self.config.doCrosstalk and self.config.doCrosstalkBeforeAssemble:
1433 self.log.info("Applying crosstalk correction.")
1434 self.crosstalk.run(ccdExposure, crosstalk=crosstalk,
1435 crosstalkSources=crosstalkSources, camera=camera)
1436 self.debugView(ccdExposure, "doCrosstalk")
1438 if self.config.doAssembleCcd:
1439 self.log.info("Assembling CCD from amplifiers.")
1440 ccdExposure = self.assembleCcd.assembleCcd(ccdExposure)
1442 if self.config.expectWcs and not ccdExposure.getWcs():
1443 self.log.warn("No WCS found in input exposure.")
1444 self.debugView(ccdExposure, "doAssembleCcd")
1446 ossThumb = None
1447 if self.config.qa.doThumbnailOss:
1448 ossThumb = isrQa.makeThumbnail(ccdExposure, isrQaConfig=self.config.qa)
1450 if self.config.doBias and not self.config.doBiasBeforeOverscan:
1451 self.log.info("Applying bias correction.")
1452 isrFunctions.biasCorrection(ccdExposure.getMaskedImage(), bias.getMaskedImage(),
1453 trimToFit=self.config.doTrimToMatchCalib)
1454 self.debugView(ccdExposure, "doBias")
1456 if self.config.doVariance:
1457 for amp, overscanResults in zip(ccd, overscans):
1458 if ccdExposure.getBBox().contains(amp.getBBox()):
1459 self.log.debug("Constructing variance map for amplifer %s.", amp.getName())
1460 ampExposure = ccdExposure.Factory(ccdExposure, amp.getBBox())
1461 if overscanResults is not None:
1462 self.updateVariance(ampExposure, amp,
1463 overscanImage=overscanResults.overscanImage)
1464 else:
1465 self.updateVariance(ampExposure, amp,
1466 overscanImage=None)
1467 if self.config.qa is not None and self.config.qa.saveStats is True:
1468 qaStats = afwMath.makeStatistics(ampExposure.getVariance(),
1469 afwMath.MEDIAN | afwMath.STDEVCLIP)
1470 self.metadata.set(f"ISR VARIANCE {amp.getName()} MEDIAN",
1471 qaStats.getValue(afwMath.MEDIAN))
1472 self.metadata.set(f"ISR VARIANCE {amp.getName()} STDEV",
1473 qaStats.getValue(afwMath.STDEVCLIP))
1474 self.log.debug(" Variance stats for amplifer %s: %f +/- %f.",
1475 amp.getName(), qaStats.getValue(afwMath.MEDIAN),
1476 qaStats.getValue(afwMath.STDEVCLIP))
1478 if self.doLinearize(ccd):
1479 self.log.info("Applying linearizer.")
1480 linearizer.applyLinearity(image=ccdExposure.getMaskedImage().getImage(),
1481 detector=ccd, log=self.log)
1483 if self.config.doCrosstalk and not self.config.doCrosstalkBeforeAssemble:
1484 self.log.info("Applying crosstalk correction.")
1485 self.crosstalk.run(ccdExposure, crosstalk=crosstalk,
1486 crosstalkSources=crosstalkSources, isTrimmed=True)
1487 self.debugView(ccdExposure, "doCrosstalk")
1489 # Masking block. Optionally mask known defects, NAN/inf pixels, widen trails, and do
1490 # anything else the camera needs. Saturated and suspect pixels have already been masked.
1491 if self.config.doDefect:
1492 self.log.info("Masking defects.")
1493 self.maskDefect(ccdExposure, defects)
1495 if self.config.numEdgeSuspect > 0:
1496 self.log.info("Masking edges as SUSPECT.")
1497 self.maskEdges(ccdExposure, numEdgePixels=self.config.numEdgeSuspect,
1498 maskPlane="SUSPECT", level=self.config.edgeMaskLevel)
1500 if self.config.doNanMasking:
1501 self.log.info("Masking non-finite (NAN, inf) value pixels.")
1502 self.maskNan(ccdExposure)
1504 if self.config.doWidenSaturationTrails:
1505 self.log.info("Widening saturation trails.")
1506 isrFunctions.widenSaturationTrails(ccdExposure.getMaskedImage().getMask())
1508 if self.config.doCameraSpecificMasking:
1509 self.log.info("Masking regions for camera specific reasons.")
1510 self.masking.run(ccdExposure)
1512 if self.config.doBrighterFatter:
1513 # We need to apply flats and darks before we can interpolate, and we
1514 # need to interpolate before we do B-F, but we do B-F without the
1515 # flats and darks applied so we can work in units of electrons or holes.
1516 # This context manager applies and then removes the darks and flats.
1517 #
1518 # We also do not want to interpolate values here, so operate on temporary
1519 # images so we can apply only the BF-correction and roll back the
1520 # interpolation.
1521 interpExp = ccdExposure.clone()
1522 with self.flatContext(interpExp, flat, dark):
1523 isrFunctions.interpolateFromMask(
1524 maskedImage=interpExp.getMaskedImage(),
1525 fwhm=self.config.fwhm,
1526 growSaturatedFootprints=self.config.growSaturationFootprintSize,
1527 maskNameList=list(self.config.brighterFatterMaskListToInterpolate)
1528 )
1529 bfExp = interpExp.clone()
1531 self.log.info("Applying brighter-fatter correction using kernel type %s / gains %s.",
1532 type(bfKernel), type(bfGains))
1533 bfResults = isrFunctions.brighterFatterCorrection(bfExp, bfKernel,
1534 self.config.brighterFatterMaxIter,
1535 self.config.brighterFatterThreshold,
1536 self.config.brighterFatterApplyGain,
1537 bfGains)
1538 if bfResults[1] == self.config.brighterFatterMaxIter:
1539 self.log.warn("Brighter-fatter correction did not converge, final difference %f.",
1540 bfResults[0])
1541 else:
1542 self.log.info("Finished brighter-fatter correction in %d iterations.",
1543 bfResults[1])
1544 image = ccdExposure.getMaskedImage().getImage()
1545 bfCorr = bfExp.getMaskedImage().getImage()
1546 bfCorr -= interpExp.getMaskedImage().getImage()
1547 image += bfCorr
1549 # Applying the brighter-fatter correction applies a
1550 # convolution to the science image. At the edges this
1551 # convolution may not have sufficient valid pixels to
1552 # produce a valid correction. Mark pixels within the size
1553 # of the brighter-fatter kernel as EDGE to warn of this
1554 # fact.
1555 self.log.info("Ensuring image edges are masked as EDGE to the brighter-fatter kernel size.")
1556 self.maskEdges(ccdExposure, numEdgePixels=numpy.max(bfKernel.shape) // 2,
1557 maskPlane="EDGE")
1559 if self.config.brighterFatterMaskGrowSize > 0:
1560 self.log.info("Growing masks to account for brighter-fatter kernel convolution.")
1561 for maskPlane in self.config.brighterFatterMaskListToInterpolate:
1562 isrFunctions.growMasks(ccdExposure.getMask(),
1563 radius=self.config.brighterFatterMaskGrowSize,
1564 maskNameList=maskPlane,
1565 maskValue=maskPlane)
1567 self.debugView(ccdExposure, "doBrighterFatter")
1569 if self.config.doDark:
1570 self.log.info("Applying dark correction.")
1571 self.darkCorrection(ccdExposure, dark)
1572 self.debugView(ccdExposure, "doDark")
1574 if self.config.doFringe and not self.config.fringeAfterFlat:
1575 self.log.info("Applying fringe correction before flat.")
1576 self.fringe.run(ccdExposure, **fringes.getDict())
1577 self.debugView(ccdExposure, "doFringe")
1579 if self.config.doStrayLight and self.strayLight.check(ccdExposure):
1580 self.log.info("Checking strayLight correction.")
1581 self.strayLight.run(ccdExposure, strayLightData)
1582 self.debugView(ccdExposure, "doStrayLight")
1584 if self.config.doFlat:
1585 self.log.info("Applying flat correction.")
1586 self.flatCorrection(ccdExposure, flat)
1587 self.debugView(ccdExposure, "doFlat")
1589 if self.config.doApplyGains:
1590 self.log.info("Applying gain correction instead of flat.")
1591 isrFunctions.applyGains(ccdExposure, self.config.normalizeGains)
1593 if self.config.doFringe and self.config.fringeAfterFlat:
1594 self.log.info("Applying fringe correction after flat.")
1595 self.fringe.run(ccdExposure, **fringes.getDict())
1597 if self.config.doVignette:
1598 self.log.info("Constructing Vignette polygon.")
1599 self.vignettePolygon = self.vignette.run(ccdExposure)
1601 if self.config.vignette.doWriteVignettePolygon:
1602 self.setValidPolygonIntersect(ccdExposure, self.vignettePolygon)
1604 if self.config.doAttachTransmissionCurve:
1605 self.log.info("Adding transmission curves.")
1606 isrFunctions.attachTransmissionCurve(ccdExposure, opticsTransmission=opticsTransmission,
1607 filterTransmission=filterTransmission,
1608 sensorTransmission=sensorTransmission,
1609 atmosphereTransmission=atmosphereTransmission)
1611 flattenedThumb = None
1612 if self.config.qa.doThumbnailFlattened:
1613 flattenedThumb = isrQa.makeThumbnail(ccdExposure, isrQaConfig=self.config.qa)
1615 if self.config.doIlluminationCorrection and filterLabel in self.config.illumFilters:
1616 self.log.info("Performing illumination correction.")
1617 isrFunctions.illuminationCorrection(ccdExposure.getMaskedImage(),
1618 illumMaskedImage, illumScale=self.config.illumScale,
1619 trimToFit=self.config.doTrimToMatchCalib)
1621 preInterpExp = None
1622 if self.config.doSaveInterpPixels:
1623 preInterpExp = ccdExposure.clone()
1625 # Reset and interpolate bad pixels.
1626 #
1627 # Large contiguous bad regions (which should have the BAD mask
1628 # bit set) should have their values set to the image median.
1629 # This group should include defects and bad amplifiers. As the
1630 # area covered by these defects are large, there's little
1631 # reason to expect that interpolation would provide a more
1632 # useful value.
1633 #
1634 # Smaller defects can be safely interpolated after the larger
1635 # regions have had their pixel values reset. This ensures
1636 # that the remaining defects adjacent to bad amplifiers (as an
1637 # example) do not attempt to interpolate extreme values.
1638 if self.config.doSetBadRegions:
1639 badPixelCount, badPixelValue = isrFunctions.setBadRegions(ccdExposure)
1640 if badPixelCount > 0:
1641 self.log.info("Set %d BAD pixels to %f.", badPixelCount, badPixelValue)
1643 if self.config.doInterpolate:
1644 self.log.info("Interpolating masked pixels.")
1645 isrFunctions.interpolateFromMask(
1646 maskedImage=ccdExposure.getMaskedImage(),
1647 fwhm=self.config.fwhm,
1648 growSaturatedFootprints=self.config.growSaturationFootprintSize,
1649 maskNameList=list(self.config.maskListToInterpolate)
1650 )
1652 self.roughZeroPoint(ccdExposure)
1654 if self.config.doMeasureBackground:
1655 self.log.info("Measuring background level.")
1656 self.measureBackground(ccdExposure, self.config.qa)
1658 if self.config.qa is not None and self.config.qa.saveStats is True:
1659 for amp in ccd:
1660 ampExposure = ccdExposure.Factory(ccdExposure, amp.getBBox())
1661 qaStats = afwMath.makeStatistics(ampExposure.getImage(),
1662 afwMath.MEDIAN | afwMath.STDEVCLIP)
1663 self.metadata.set("ISR BACKGROUND {} MEDIAN".format(amp.getName()),
1664 qaStats.getValue(afwMath.MEDIAN))
1665 self.metadata.set("ISR BACKGROUND {} STDEV".format(amp.getName()),
1666 qaStats.getValue(afwMath.STDEVCLIP))
1667 self.log.debug(" Background stats for amplifer %s: %f +/- %f",
1668 amp.getName(), qaStats.getValue(afwMath.MEDIAN),
1669 qaStats.getValue(afwMath.STDEVCLIP))
1671 self.debugView(ccdExposure, "postISRCCD")
1673 return pipeBase.Struct(
1674 exposure=ccdExposure,
1675 ossThumb=ossThumb,
1676 flattenedThumb=flattenedThumb,
1678 preInterpolatedExposure=preInterpExp,
1679 outputExposure=ccdExposure,
1680 outputOssThumbnail=ossThumb,
1681 outputFlattenedThumbnail=flattenedThumb,
1682 )
1684 @pipeBase.timeMethod
1685 def runDataRef(self, sensorRef):
1686 """Perform instrument signature removal on a ButlerDataRef of a Sensor.
1688 This method contains the `CmdLineTask` interface to the ISR
1689 processing. All IO is handled here, freeing the `run()` method
1690 to manage only pixel-level calculations. The steps performed
1691 are:
1692 - Read in necessary detrending/isr/calibration data.
1693 - Process raw exposure in `run()`.
1694 - Persist the ISR-corrected exposure as "postISRCCD" if
1695 config.doWrite=True.
1697 Parameters
1698 ----------
1699 sensorRef : `daf.persistence.butlerSubset.ButlerDataRef`
1700 DataRef of the detector data to be processed
1702 Returns
1703 -------
1704 result : `lsst.pipe.base.Struct`
1705 Result struct with component:
1706 - ``exposure`` : `afw.image.Exposure`
1707 The fully ISR corrected exposure.
1709 Raises
1710 ------
1711 RuntimeError
1712 Raised if a configuration option is set to True, but the
1713 required calibration data does not exist.
1715 """
1716 self.log.info("Performing ISR on sensor %s.", sensorRef.dataId)
1718 ccdExposure = sensorRef.get(self.config.datasetType)
1720 camera = sensorRef.get("camera")
1721 isrData = self.readIsrData(sensorRef, ccdExposure)
1723 result = self.run(ccdExposure, camera=camera, **isrData.getDict())
1725 if self.config.doWrite:
1726 sensorRef.put(result.exposure, "postISRCCD")
1727 if result.preInterpolatedExposure is not None:
1728 sensorRef.put(result.preInterpolatedExposure, "postISRCCD_uninterpolated")
1729 if result.ossThumb is not None:
1730 isrQa.writeThumbnail(sensorRef, result.ossThumb, "ossThumb")
1731 if result.flattenedThumb is not None:
1732 isrQa.writeThumbnail(sensorRef, result.flattenedThumb, "flattenedThumb")
1734 return result
1736 def getIsrExposure(self, dataRef, datasetType, dateObs=None, immediate=True):
1737 """Retrieve a calibration dataset for removing instrument signature.
1739 Parameters
1740 ----------
1742 dataRef : `daf.persistence.butlerSubset.ButlerDataRef`
1743 DataRef of the detector data to find calibration datasets
1744 for.
1745 datasetType : `str`
1746 Type of dataset to retrieve (e.g. 'bias', 'flat', etc).
1747 dateObs : `str`, optional
1748 Date of the observation. Used to correct butler failures
1749 when using fallback filters.
1750 immediate : `Bool`
1751 If True, disable butler proxies to enable error handling
1752 within this routine.
1754 Returns
1755 -------
1756 exposure : `lsst.afw.image.Exposure`
1757 Requested calibration frame.
1759 Raises
1760 ------
1761 RuntimeError
1762 Raised if no matching calibration frame can be found.
1763 """
1764 try:
1765 exp = dataRef.get(datasetType, immediate=immediate)
1766 except Exception as exc1:
1767 if not self.config.fallbackFilterName:
1768 raise RuntimeError("Unable to retrieve %s for %s: %s." % (datasetType, dataRef.dataId, exc1))
1769 try:
1770 if self.config.useFallbackDate and dateObs:
1771 exp = dataRef.get(datasetType, filter=self.config.fallbackFilterName,
1772 dateObs=dateObs, immediate=immediate)
1773 else:
1774 exp = dataRef.get(datasetType, filter=self.config.fallbackFilterName, immediate=immediate)
1775 except Exception as exc2:
1776 raise RuntimeError("Unable to retrieve %s for %s, even with fallback filter %s: %s AND %s." %
1777 (datasetType, dataRef.dataId, self.config.fallbackFilterName, exc1, exc2))
1778 self.log.warn("Using fallback calibration from filter %s.", self.config.fallbackFilterName)
1780 if self.config.doAssembleIsrExposures:
1781 exp = self.assembleCcd.assembleCcd(exp)
1782 return exp
1784 def ensureExposure(self, inputExp, camera, detectorNum):
1785 """Ensure that the data returned by Butler is a fully constructed exposure.
1787 ISR requires exposure-level image data for historical reasons, so if we did
1788 not recieve that from Butler, construct it from what we have, modifying the
1789 input in place.
1791 Parameters
1792 ----------
1793 inputExp : `lsst.afw.image.Exposure`, `lsst.afw.image.DecoratedImageU`, or
1794 `lsst.afw.image.ImageF`
1795 The input data structure obtained from Butler.
1796 camera : `lsst.afw.cameraGeom.camera`
1797 The camera associated with the image. Used to find the appropriate
1798 detector.
1799 detectorNum : `int`
1800 The detector this exposure should match.
1802 Returns
1803 -------
1804 inputExp : `lsst.afw.image.Exposure`
1805 The re-constructed exposure, with appropriate detector parameters.
1807 Raises
1808 ------
1809 TypeError
1810 Raised if the input data cannot be used to construct an exposure.
1811 """
1812 if isinstance(inputExp, afwImage.DecoratedImageU):
1813 inputExp = afwImage.makeExposure(afwImage.makeMaskedImage(inputExp))
1814 elif isinstance(inputExp, afwImage.ImageF):
1815 inputExp = afwImage.makeExposure(afwImage.makeMaskedImage(inputExp))
1816 elif isinstance(inputExp, afwImage.MaskedImageF):
1817 inputExp = afwImage.makeExposure(inputExp)
1818 elif isinstance(inputExp, afwImage.Exposure):
1819 pass
1820 elif inputExp is None:
1821 # Assume this will be caught by the setup if it is a problem.
1822 return inputExp
1823 else:
1824 raise TypeError("Input Exposure is not known type in isrTask.ensureExposure: %s." %
1825 (type(inputExp), ))
1827 if inputExp.getDetector() is None:
1828 inputExp.setDetector(camera[detectorNum])
1830 return inputExp
1832 def convertIntToFloat(self, exposure):
1833 """Convert exposure image from uint16 to float.
1835 If the exposure does not need to be converted, the input is
1836 immediately returned. For exposures that are converted to use
1837 floating point pixels, the variance is set to unity and the
1838 mask to zero.
1840 Parameters
1841 ----------
1842 exposure : `lsst.afw.image.Exposure`
1843 The raw exposure to be converted.
1845 Returns
1846 -------
1847 newexposure : `lsst.afw.image.Exposure`
1848 The input ``exposure``, converted to floating point pixels.
1850 Raises
1851 ------
1852 RuntimeError
1853 Raised if the exposure type cannot be converted to float.
1855 """
1856 if isinstance(exposure, afwImage.ExposureF):
1857 # Nothing to be done
1858 self.log.debug("Exposure already of type float.")
1859 return exposure
1860 if not hasattr(exposure, "convertF"):
1861 raise RuntimeError("Unable to convert exposure (%s) to float." % type(exposure))
1863 newexposure = exposure.convertF()
1864 newexposure.variance[:] = 1
1865 newexposure.mask[:] = 0x0
1867 return newexposure
1869 def maskAmplifier(self, ccdExposure, amp, defects):
1870 """Identify bad amplifiers, saturated and suspect pixels.
1872 Parameters
1873 ----------
1874 ccdExposure : `lsst.afw.image.Exposure`
1875 Input exposure to be masked.
1876 amp : `lsst.afw.table.AmpInfoCatalog`
1877 Catalog of parameters defining the amplifier on this
1878 exposure to mask.
1879 defects : `lsst.ip.isr.Defects`
1880 List of defects. Used to determine if the entire
1881 amplifier is bad.
1883 Returns
1884 -------
1885 badAmp : `Bool`
1886 If this is true, the entire amplifier area is covered by
1887 defects and unusable.
1889 """
1890 maskedImage = ccdExposure.getMaskedImage()
1892 badAmp = False
1894 # Check if entire amp region is defined as a defect (need to use amp.getBBox() for correct
1895 # comparison with current defects definition.
1896 if defects is not None:
1897 badAmp = bool(sum([v.getBBox().contains(amp.getBBox()) for v in defects]))
1899 # In the case of a bad amp, we will set mask to "BAD" (here use amp.getRawBBox() for correct
1900 # association with pixels in current ccdExposure).
1901 if badAmp:
1902 dataView = afwImage.MaskedImageF(maskedImage, amp.getRawBBox(),
1903 afwImage.PARENT)
1904 maskView = dataView.getMask()
1905 maskView |= maskView.getPlaneBitMask("BAD")
1906 del maskView
1907 return badAmp
1909 # Mask remaining defects after assembleCcd() to allow for defects that cross amplifier boundaries.
1910 # Saturation and suspect pixels can be masked now, though.
1911 limits = dict()
1912 if self.config.doSaturation and not badAmp:
1913 limits.update({self.config.saturatedMaskName: amp.getSaturation()})
1914 if self.config.doSuspect and not badAmp:
1915 limits.update({self.config.suspectMaskName: amp.getSuspectLevel()})
1916 if math.isfinite(self.config.saturation):
1917 limits.update({self.config.saturatedMaskName: self.config.saturation})
1919 for maskName, maskThreshold in limits.items():
1920 if not math.isnan(maskThreshold):
1921 dataView = maskedImage.Factory(maskedImage, amp.getRawBBox())
1922 isrFunctions.makeThresholdMask(
1923 maskedImage=dataView,
1924 threshold=maskThreshold,
1925 growFootprints=0,
1926 maskName=maskName
1927 )
1929 # Determine if we've fully masked this amplifier with SUSPECT and SAT pixels.
1930 maskView = afwImage.Mask(maskedImage.getMask(), amp.getRawDataBBox(),
1931 afwImage.PARENT)
1932 maskVal = maskView.getPlaneBitMask([self.config.saturatedMaskName,
1933 self.config.suspectMaskName])
1934 if numpy.all(maskView.getArray() & maskVal > 0):
1935 badAmp = True
1936 maskView |= maskView.getPlaneBitMask("BAD")
1938 return badAmp
1940 def overscanCorrection(self, ccdExposure, amp):
1941 """Apply overscan correction in place.
1943 This method does initial pixel rejection of the overscan
1944 region. The overscan can also be optionally segmented to
1945 allow for discontinuous overscan responses to be fit
1946 separately. The actual overscan subtraction is performed by
1947 the `lsst.ip.isr.isrFunctions.overscanCorrection` function,
1948 which is called here after the amplifier is preprocessed.
1950 Parameters
1951 ----------
1952 ccdExposure : `lsst.afw.image.Exposure`
1953 Exposure to have overscan correction performed.
1954 amp : `lsst.afw.cameraGeom.Amplifer`
1955 The amplifier to consider while correcting the overscan.
1957 Returns
1958 -------
1959 overscanResults : `lsst.pipe.base.Struct`
1960 Result struct with components:
1961 - ``imageFit`` : scalar or `lsst.afw.image.Image`
1962 Value or fit subtracted from the amplifier image data.
1963 - ``overscanFit`` : scalar or `lsst.afw.image.Image`
1964 Value or fit subtracted from the overscan image data.
1965 - ``overscanImage`` : `lsst.afw.image.Image`
1966 Image of the overscan region with the overscan
1967 correction applied. This quantity is used to estimate
1968 the amplifier read noise empirically.
1970 Raises
1971 ------
1972 RuntimeError
1973 Raised if the ``amp`` does not contain raw pixel information.
1975 See Also
1976 --------
1977 lsst.ip.isr.isrFunctions.overscanCorrection
1978 """
1979 if amp.getRawHorizontalOverscanBBox().isEmpty():
1980 self.log.info("ISR_OSCAN: No overscan region. Not performing overscan correction.")
1981 return None
1983 statControl = afwMath.StatisticsControl()
1984 statControl.setAndMask(ccdExposure.mask.getPlaneBitMask("SAT"))
1986 # Determine the bounding boxes
1987 dataBBox = amp.getRawDataBBox()
1988 oscanBBox = amp.getRawHorizontalOverscanBBox()
1989 dx0 = 0
1990 dx1 = 0
1992 prescanBBox = amp.getRawPrescanBBox()
1993 if (oscanBBox.getBeginX() > prescanBBox.getBeginX()): # amp is at the right
1994 dx0 += self.config.overscanNumLeadingColumnsToSkip
1995 dx1 -= self.config.overscanNumTrailingColumnsToSkip
1996 else:
1997 dx0 += self.config.overscanNumTrailingColumnsToSkip
1998 dx1 -= self.config.overscanNumLeadingColumnsToSkip
2000 # Determine if we need to work on subregions of the amplifier and overscan.
2001 imageBBoxes = []
2002 overscanBBoxes = []
2004 if ((self.config.overscanBiasJump
2005 and self.config.overscanBiasJumpLocation)
2006 and (ccdExposure.getMetadata().exists(self.config.overscanBiasJumpKeyword)
2007 and ccdExposure.getMetadata().getScalar(self.config.overscanBiasJumpKeyword) in
2008 self.config.overscanBiasJumpDevices)):
2009 if amp.getReadoutCorner() in (ReadoutCorner.LL, ReadoutCorner.LR):
2010 yLower = self.config.overscanBiasJumpLocation
2011 yUpper = dataBBox.getHeight() - yLower
2012 else:
2013 yUpper = self.config.overscanBiasJumpLocation
2014 yLower = dataBBox.getHeight() - yUpper
2016 imageBBoxes.append(lsst.geom.Box2I(dataBBox.getBegin(),
2017 lsst.geom.Extent2I(dataBBox.getWidth(), yLower)))
2018 overscanBBoxes.append(lsst.geom.Box2I(oscanBBox.getBegin() + lsst.geom.Extent2I(dx0, 0),
2019 lsst.geom.Extent2I(oscanBBox.getWidth() - dx0 + dx1,
2020 yLower)))
2022 imageBBoxes.append(lsst.geom.Box2I(dataBBox.getBegin() + lsst.geom.Extent2I(0, yLower),
2023 lsst.geom.Extent2I(dataBBox.getWidth(), yUpper)))
2024 overscanBBoxes.append(lsst.geom.Box2I(oscanBBox.getBegin() + lsst.geom.Extent2I(dx0, yLower),
2025 lsst.geom.Extent2I(oscanBBox.getWidth() - dx0 + dx1,
2026 yUpper)))
2027 else:
2028 imageBBoxes.append(lsst.geom.Box2I(dataBBox.getBegin(),
2029 lsst.geom.Extent2I(dataBBox.getWidth(), dataBBox.getHeight())))
2030 overscanBBoxes.append(lsst.geom.Box2I(oscanBBox.getBegin() + lsst.geom.Extent2I(dx0, 0),
2031 lsst.geom.Extent2I(oscanBBox.getWidth() - dx0 + dx1,
2032 oscanBBox.getHeight())))
2034 # Perform overscan correction on subregions, ensuring saturated pixels are masked.
2035 for imageBBox, overscanBBox in zip(imageBBoxes, overscanBBoxes):
2036 ampImage = ccdExposure.maskedImage[imageBBox]
2037 overscanImage = ccdExposure.maskedImage[overscanBBox]
2039 overscanArray = overscanImage.image.array
2040 median = numpy.ma.median(numpy.ma.masked_where(overscanImage.mask.array, overscanArray))
2041 bad = numpy.where(numpy.abs(overscanArray - median) > self.config.overscanMaxDev)
2042 overscanImage.mask.array[bad] = overscanImage.mask.getPlaneBitMask("SAT")
2044 statControl = afwMath.StatisticsControl()
2045 statControl.setAndMask(ccdExposure.mask.getPlaneBitMask("SAT"))
2047 overscanResults = self.overscan.run(ampImage.getImage(), overscanImage, amp)
2049 # Measure average overscan levels and record them in the metadata.
2050 levelStat = afwMath.MEDIAN
2051 sigmaStat = afwMath.STDEVCLIP
2053 sctrl = afwMath.StatisticsControl(self.config.qa.flatness.clipSigma,
2054 self.config.qa.flatness.nIter)
2055 metadata = ccdExposure.getMetadata()
2056 ampNum = amp.getName()
2057 # if self.config.overscanFitType in ("MEDIAN", "MEAN", "MEANCLIP"):
2058 if isinstance(overscanResults.overscanFit, float):
2059 metadata.set("ISR_OSCAN_LEVEL%s" % ampNum, overscanResults.overscanFit)
2060 metadata.set("ISR_OSCAN_SIGMA%s" % ampNum, 0.0)
2061 else:
2062 stats = afwMath.makeStatistics(overscanResults.overscanFit, levelStat | sigmaStat, sctrl)
2063 metadata.set("ISR_OSCAN_LEVEL%s" % ampNum, stats.getValue(levelStat))
2064 metadata.set("ISR_OSCAN_SIGMA%s" % ampNum, stats.getValue(sigmaStat))
2066 return overscanResults
2068 def updateVariance(self, ampExposure, amp, overscanImage=None):
2069 """Set the variance plane using the amplifier gain and read noise
2071 The read noise is calculated from the ``overscanImage`` if the
2072 ``doEmpiricalReadNoise`` option is set in the configuration; otherwise
2073 the value from the amplifier data is used.
2075 Parameters
2076 ----------
2077 ampExposure : `lsst.afw.image.Exposure`
2078 Exposure to process.
2079 amp : `lsst.afw.table.AmpInfoRecord` or `FakeAmp`
2080 Amplifier detector data.
2081 overscanImage : `lsst.afw.image.MaskedImage`, optional.
2082 Image of overscan, required only for empirical read noise.
2084 See also
2085 --------
2086 lsst.ip.isr.isrFunctions.updateVariance
2087 """
2088 maskPlanes = [self.config.saturatedMaskName, self.config.suspectMaskName]
2089 gain = amp.getGain()
2091 if math.isnan(gain):
2092 gain = 1.0
2093 self.log.warn("Gain set to NAN! Updating to 1.0 to generate Poisson variance.")
2094 elif gain <= 0:
2095 patchedGain = 1.0
2096 self.log.warn("Gain for amp %s == %g <= 0; setting to %f.",
2097 amp.getName(), gain, patchedGain)
2098 gain = patchedGain
2100 if self.config.doEmpiricalReadNoise and overscanImage is None:
2101 self.log.info("Overscan is none for EmpiricalReadNoise.")
2103 if self.config.doEmpiricalReadNoise and overscanImage is not None:
2104 stats = afwMath.StatisticsControl()
2105 stats.setAndMask(overscanImage.mask.getPlaneBitMask(maskPlanes))
2106 readNoise = afwMath.makeStatistics(overscanImage, afwMath.STDEVCLIP, stats).getValue()
2107 self.log.info("Calculated empirical read noise for amp %s: %f.",
2108 amp.getName(), readNoise)
2109 else:
2110 readNoise = amp.getReadNoise()
2112 isrFunctions.updateVariance(
2113 maskedImage=ampExposure.getMaskedImage(),
2114 gain=gain,
2115 readNoise=readNoise,
2116 )
2118 def darkCorrection(self, exposure, darkExposure, invert=False):
2119 """Apply dark correction in place.
2121 Parameters
2122 ----------
2123 exposure : `lsst.afw.image.Exposure`
2124 Exposure to process.
2125 darkExposure : `lsst.afw.image.Exposure`
2126 Dark exposure of the same size as ``exposure``.
2127 invert : `Bool`, optional
2128 If True, re-add the dark to an already corrected image.
2130 Raises
2131 ------
2132 RuntimeError
2133 Raised if either ``exposure`` or ``darkExposure`` do not
2134 have their dark time defined.
2136 See Also
2137 --------
2138 lsst.ip.isr.isrFunctions.darkCorrection
2139 """
2140 expScale = exposure.getInfo().getVisitInfo().getDarkTime()
2141 if math.isnan(expScale):
2142 raise RuntimeError("Exposure darktime is NAN.")
2143 if darkExposure.getInfo().getVisitInfo() is not None \
2144 and not math.isnan(darkExposure.getInfo().getVisitInfo().getDarkTime()):
2145 darkScale = darkExposure.getInfo().getVisitInfo().getDarkTime()
2146 else:
2147 # DM-17444: darkExposure.getInfo.getVisitInfo() is None
2148 # so getDarkTime() does not exist.
2149 self.log.warn("darkExposure.getInfo().getVisitInfo() does not exist. Using darkScale = 1.0.")
2150 darkScale = 1.0
2152 isrFunctions.darkCorrection(
2153 maskedImage=exposure.getMaskedImage(),
2154 darkMaskedImage=darkExposure.getMaskedImage(),
2155 expScale=expScale,
2156 darkScale=darkScale,
2157 invert=invert,
2158 trimToFit=self.config.doTrimToMatchCalib
2159 )
2161 def doLinearize(self, detector):
2162 """Check if linearization is needed for the detector cameraGeom.
2164 Checks config.doLinearize and the linearity type of the first
2165 amplifier.
2167 Parameters
2168 ----------
2169 detector : `lsst.afw.cameraGeom.Detector`
2170 Detector to get linearity type from.
2172 Returns
2173 -------
2174 doLinearize : `Bool`
2175 If True, linearization should be performed.
2176 """
2177 return self.config.doLinearize and \
2178 detector.getAmplifiers()[0].getLinearityType() != NullLinearityType
2180 def flatCorrection(self, exposure, flatExposure, invert=False):
2181 """Apply flat correction in place.
2183 Parameters
2184 ----------
2185 exposure : `lsst.afw.image.Exposure`
2186 Exposure to process.
2187 flatExposure : `lsst.afw.image.Exposure`
2188 Flat exposure of the same size as ``exposure``.
2189 invert : `Bool`, optional
2190 If True, unflatten an already flattened image.
2192 See Also
2193 --------
2194 lsst.ip.isr.isrFunctions.flatCorrection
2195 """
2196 isrFunctions.flatCorrection(
2197 maskedImage=exposure.getMaskedImage(),
2198 flatMaskedImage=flatExposure.getMaskedImage(),
2199 scalingType=self.config.flatScalingType,
2200 userScale=self.config.flatUserScale,
2201 invert=invert,
2202 trimToFit=self.config.doTrimToMatchCalib
2203 )
2205 def saturationDetection(self, exposure, amp):
2206 """Detect saturated pixels and mask them using mask plane config.saturatedMaskName, in place.
2208 Parameters
2209 ----------
2210 exposure : `lsst.afw.image.Exposure`
2211 Exposure to process. Only the amplifier DataSec is processed.
2212 amp : `lsst.afw.table.AmpInfoCatalog`
2213 Amplifier detector data.
2215 See Also
2216 --------
2217 lsst.ip.isr.isrFunctions.makeThresholdMask
2218 """
2219 if not math.isnan(amp.getSaturation()):
2220 maskedImage = exposure.getMaskedImage()
2221 dataView = maskedImage.Factory(maskedImage, amp.getRawBBox())
2222 isrFunctions.makeThresholdMask(
2223 maskedImage=dataView,
2224 threshold=amp.getSaturation(),
2225 growFootprints=0,
2226 maskName=self.config.saturatedMaskName,
2227 )
2229 def saturationInterpolation(self, exposure):
2230 """Interpolate over saturated pixels, in place.
2232 This method should be called after `saturationDetection`, to
2233 ensure that the saturated pixels have been identified in the
2234 SAT mask. It should also be called after `assembleCcd`, since
2235 saturated regions may cross amplifier boundaries.
2237 Parameters
2238 ----------
2239 exposure : `lsst.afw.image.Exposure`
2240 Exposure to process.
2242 See Also
2243 --------
2244 lsst.ip.isr.isrTask.saturationDetection
2245 lsst.ip.isr.isrFunctions.interpolateFromMask
2246 """
2247 isrFunctions.interpolateFromMask(
2248 maskedImage=exposure.getMaskedImage(),
2249 fwhm=self.config.fwhm,
2250 growSaturatedFootprints=self.config.growSaturationFootprintSize,
2251 maskNameList=list(self.config.saturatedMaskName),
2252 )
2254 def suspectDetection(self, exposure, amp):
2255 """Detect suspect pixels and mask them using mask plane config.suspectMaskName, in place.
2257 Parameters
2258 ----------
2259 exposure : `lsst.afw.image.Exposure`
2260 Exposure to process. Only the amplifier DataSec is processed.
2261 amp : `lsst.afw.table.AmpInfoCatalog`
2262 Amplifier detector data.
2264 See Also
2265 --------
2266 lsst.ip.isr.isrFunctions.makeThresholdMask
2268 Notes
2269 -----
2270 Suspect pixels are pixels whose value is greater than amp.getSuspectLevel().
2271 This is intended to indicate pixels that may be affected by unknown systematics;
2272 for example if non-linearity corrections above a certain level are unstable
2273 then that would be a useful value for suspectLevel. A value of `nan` indicates
2274 that no such level exists and no pixels are to be masked as suspicious.
2275 """
2276 suspectLevel = amp.getSuspectLevel()
2277 if math.isnan(suspectLevel):
2278 return
2280 maskedImage = exposure.getMaskedImage()
2281 dataView = maskedImage.Factory(maskedImage, amp.getRawBBox())
2282 isrFunctions.makeThresholdMask(
2283 maskedImage=dataView,
2284 threshold=suspectLevel,
2285 growFootprints=0,
2286 maskName=self.config.suspectMaskName,
2287 )
2289 def maskDefect(self, exposure, defectBaseList):
2290 """Mask defects using mask plane "BAD", in place.
2292 Parameters
2293 ----------
2294 exposure : `lsst.afw.image.Exposure`
2295 Exposure to process.
2296 defectBaseList : `lsst.ip.isr.Defects` or `list` of
2297 `lsst.afw.image.DefectBase`.
2298 List of defects to mask.
2300 Notes
2301 -----
2302 Call this after CCD assembly, since defects may cross amplifier boundaries.
2303 """
2304 maskedImage = exposure.getMaskedImage()
2305 if not isinstance(defectBaseList, Defects):
2306 # Promotes DefectBase to Defect
2307 defectList = Defects(defectBaseList)
2308 else:
2309 defectList = defectBaseList
2310 defectList.maskPixels(maskedImage, maskName="BAD")
2312 def maskEdges(self, exposure, numEdgePixels=0, maskPlane="SUSPECT", level='DETECTOR'):
2313 """Mask edge pixels with applicable mask plane.
2315 Parameters
2316 ----------
2317 exposure : `lsst.afw.image.Exposure`
2318 Exposure to process.
2319 numEdgePixels : `int`, optional
2320 Number of edge pixels to mask.
2321 maskPlane : `str`, optional
2322 Mask plane name to use.
2323 level : `str`, optional
2324 Level at which to mask edges.
2325 """
2326 maskedImage = exposure.getMaskedImage()
2327 maskBitMask = maskedImage.getMask().getPlaneBitMask(maskPlane)
2329 if numEdgePixels > 0:
2330 if level == 'DETECTOR':
2331 boxes = [maskedImage.getBBox()]
2332 elif level == 'AMP':
2333 boxes = [amp.getBBox() for amp in exposure.getDetector()]
2335 for box in boxes:
2336 # This makes a bbox numEdgeSuspect pixels smaller than the image on each side
2337 subImage = maskedImage[box]
2338 box.grow(-numEdgePixels)
2339 # Mask pixels outside box
2340 SourceDetectionTask.setEdgeBits(
2341 subImage,
2342 box,
2343 maskBitMask)
2345 def maskAndInterpolateDefects(self, exposure, defectBaseList):
2346 """Mask and interpolate defects using mask plane "BAD", in place.
2348 Parameters
2349 ----------
2350 exposure : `lsst.afw.image.Exposure`
2351 Exposure to process.
2352 defectBaseList : `lsst.ip.isr.Defects` or `list` of
2353 `lsst.afw.image.DefectBase`.
2354 List of defects to mask and interpolate.
2356 See Also
2357 --------
2358 lsst.ip.isr.isrTask.maskDefect
2359 """
2360 self.maskDefect(exposure, defectBaseList)
2361 self.maskEdges(exposure, numEdgePixels=self.config.numEdgeSuspect,
2362 maskPlane="SUSPECT", level=self.config.edgeMaskLevel)
2363 isrFunctions.interpolateFromMask(
2364 maskedImage=exposure.getMaskedImage(),
2365 fwhm=self.config.fwhm,
2366 growSaturatedFootprints=0,
2367 maskNameList=["BAD"],
2368 )
2370 def maskNan(self, exposure):
2371 """Mask NaNs using mask plane "UNMASKEDNAN", in place.
2373 Parameters
2374 ----------
2375 exposure : `lsst.afw.image.Exposure`
2376 Exposure to process.
2378 Notes
2379 -----
2380 We mask over all non-finite values (NaN, inf), including those
2381 that are masked with other bits (because those may or may not be
2382 interpolated over later, and we want to remove all NaN/infs).
2383 Despite this behaviour, the "UNMASKEDNAN" mask plane is used to
2384 preserve the historical name.
2385 """
2386 maskedImage = exposure.getMaskedImage()
2388 # Find and mask NaNs
2389 maskedImage.getMask().addMaskPlane("UNMASKEDNAN")
2390 maskVal = maskedImage.getMask().getPlaneBitMask("UNMASKEDNAN")
2391 numNans = maskNans(maskedImage, maskVal)
2392 self.metadata.set("NUMNANS", numNans)
2393 if numNans > 0:
2394 self.log.warn("There were %d unmasked NaNs.", numNans)
2396 def maskAndInterpolateNan(self, exposure):
2397 """"Mask and interpolate NaN/infs using mask plane "UNMASKEDNAN",
2398 in place.
2400 Parameters
2401 ----------
2402 exposure : `lsst.afw.image.Exposure`
2403 Exposure to process.
2405 See Also
2406 --------
2407 lsst.ip.isr.isrTask.maskNan
2408 """
2409 self.maskNan(exposure)
2410 isrFunctions.interpolateFromMask(
2411 maskedImage=exposure.getMaskedImage(),
2412 fwhm=self.config.fwhm,
2413 growSaturatedFootprints=0,
2414 maskNameList=["UNMASKEDNAN"],
2415 )
2417 def measureBackground(self, exposure, IsrQaConfig=None):
2418 """Measure the image background in subgrids, for quality control purposes.
2420 Parameters
2421 ----------
2422 exposure : `lsst.afw.image.Exposure`
2423 Exposure to process.
2424 IsrQaConfig : `lsst.ip.isr.isrQa.IsrQaConfig`
2425 Configuration object containing parameters on which background
2426 statistics and subgrids to use.
2427 """
2428 if IsrQaConfig is not None:
2429 statsControl = afwMath.StatisticsControl(IsrQaConfig.flatness.clipSigma,
2430 IsrQaConfig.flatness.nIter)
2431 maskVal = exposure.getMaskedImage().getMask().getPlaneBitMask(["BAD", "SAT", "DETECTED"])
2432 statsControl.setAndMask(maskVal)
2433 maskedImage = exposure.getMaskedImage()
2434 stats = afwMath.makeStatistics(maskedImage, afwMath.MEDIAN | afwMath.STDEVCLIP, statsControl)
2435 skyLevel = stats.getValue(afwMath.MEDIAN)
2436 skySigma = stats.getValue(afwMath.STDEVCLIP)
2437 self.log.info("Flattened sky level: %f +/- %f.", skyLevel, skySigma)
2438 metadata = exposure.getMetadata()
2439 metadata.set('SKYLEVEL', skyLevel)
2440 metadata.set('SKYSIGMA', skySigma)
2442 # calcluating flatlevel over the subgrids
2443 stat = afwMath.MEANCLIP if IsrQaConfig.flatness.doClip else afwMath.MEAN
2444 meshXHalf = int(IsrQaConfig.flatness.meshX/2.)
2445 meshYHalf = int(IsrQaConfig.flatness.meshY/2.)
2446 nX = int((exposure.getWidth() + meshXHalf) / IsrQaConfig.flatness.meshX)
2447 nY = int((exposure.getHeight() + meshYHalf) / IsrQaConfig.flatness.meshY)
2448 skyLevels = numpy.zeros((nX, nY))
2450 for j in range(nY):
2451 yc = meshYHalf + j * IsrQaConfig.flatness.meshY
2452 for i in range(nX):
2453 xc = meshXHalf + i * IsrQaConfig.flatness.meshX
2455 xLLC = xc - meshXHalf
2456 yLLC = yc - meshYHalf
2457 xURC = xc + meshXHalf - 1
2458 yURC = yc + meshYHalf - 1
2460 bbox = lsst.geom.Box2I(lsst.geom.Point2I(xLLC, yLLC), lsst.geom.Point2I(xURC, yURC))
2461 miMesh = maskedImage.Factory(exposure.getMaskedImage(), bbox, afwImage.LOCAL)
2463 skyLevels[i, j] = afwMath.makeStatistics(miMesh, stat, statsControl).getValue()
2465 good = numpy.where(numpy.isfinite(skyLevels))
2466 skyMedian = numpy.median(skyLevels[good])
2467 flatness = (skyLevels[good] - skyMedian) / skyMedian
2468 flatness_rms = numpy.std(flatness)
2469 flatness_pp = flatness.max() - flatness.min() if len(flatness) > 0 else numpy.nan
2471 self.log.info("Measuring sky levels in %dx%d grids: %f.", nX, nY, skyMedian)
2472 self.log.info("Sky flatness in %dx%d grids - pp: %f rms: %f.",
2473 nX, nY, flatness_pp, flatness_rms)
2475 metadata.set('FLATNESS_PP', float(flatness_pp))
2476 metadata.set('FLATNESS_RMS', float(flatness_rms))
2477 metadata.set('FLATNESS_NGRIDS', '%dx%d' % (nX, nY))
2478 metadata.set('FLATNESS_MESHX', IsrQaConfig.flatness.meshX)
2479 metadata.set('FLATNESS_MESHY', IsrQaConfig.flatness.meshY)
2481 def roughZeroPoint(self, exposure):
2482 """Set an approximate magnitude zero point for the exposure.
2484 Parameters
2485 ----------
2486 exposure : `lsst.afw.image.Exposure`
2487 Exposure to process.
2488 """
2489 filterLabel = exposure.getFilterLabel()
2490 if filterLabel in self.config.fluxMag0T1:
2491 fluxMag0 = self.config.fluxMag0T1[filterLabel]
2492 else:
2493 self.log.warn("No rough magnitude zero point set for filter %s.", filterLabel)
2494 fluxMag0 = self.config.defaultFluxMag0T1
2496 expTime = exposure.getInfo().getVisitInfo().getExposureTime()
2497 if not expTime > 0: # handle NaN as well as <= 0
2498 self.log.warn("Non-positive exposure time; skipping rough zero point.")
2499 return
2501 self.log.info("Setting rough magnitude zero point: %f", 2.5*math.log10(fluxMag0*expTime))
2502 exposure.setPhotoCalib(afwImage.makePhotoCalibFromCalibZeroPoint(fluxMag0*expTime, 0.0))
2504 def setValidPolygonIntersect(self, ccdExposure, fpPolygon):
2505 """Set the valid polygon as the intersection of fpPolygon and the ccd corners.
2507 Parameters
2508 ----------
2509 ccdExposure : `lsst.afw.image.Exposure`
2510 Exposure to process.
2511 fpPolygon : `lsst.afw.geom.Polygon`
2512 Polygon in focal plane coordinates.
2513 """
2514 # Get ccd corners in focal plane coordinates
2515 ccd = ccdExposure.getDetector()
2516 fpCorners = ccd.getCorners(FOCAL_PLANE)
2517 ccdPolygon = Polygon(fpCorners)
2519 # Get intersection of ccd corners with fpPolygon
2520 intersect = ccdPolygon.intersectionSingle(fpPolygon)
2522 # Transform back to pixel positions and build new polygon
2523 ccdPoints = ccd.transform(intersect, FOCAL_PLANE, PIXELS)
2524 validPolygon = Polygon(ccdPoints)
2525 ccdExposure.getInfo().setValidPolygon(validPolygon)
2527 @contextmanager
2528 def flatContext(self, exp, flat, dark=None):
2529 """Context manager that applies and removes flats and darks,
2530 if the task is configured to apply them.
2532 Parameters
2533 ----------
2534 exp : `lsst.afw.image.Exposure`
2535 Exposure to process.
2536 flat : `lsst.afw.image.Exposure`
2537 Flat exposure the same size as ``exp``.
2538 dark : `lsst.afw.image.Exposure`, optional
2539 Dark exposure the same size as ``exp``.
2541 Yields
2542 ------
2543 exp : `lsst.afw.image.Exposure`
2544 The flat and dark corrected exposure.
2545 """
2546 if self.config.doDark and dark is not None:
2547 self.darkCorrection(exp, dark)
2548 if self.config.doFlat:
2549 self.flatCorrection(exp, flat)
2550 try:
2551 yield exp
2552 finally:
2553 if self.config.doFlat:
2554 self.flatCorrection(exp, flat, invert=True)
2555 if self.config.doDark and dark is not None:
2556 self.darkCorrection(exp, dark, invert=True)
2558 def debugView(self, exposure, stepname):
2559 """Utility function to examine ISR exposure at different stages.
2561 Parameters
2562 ----------
2563 exposure : `lsst.afw.image.Exposure`
2564 Exposure to view.
2565 stepname : `str`
2566 State of processing to view.
2567 """
2568 frame = getDebugFrame(self._display, stepname)
2569 if frame:
2570 display = getDisplay(frame)
2571 display.scale('asinh', 'zscale')
2572 display.mtv(exposure)
2573 prompt = "Press Enter to continue [c]... "
2574 while True:
2575 ans = input(prompt).lower()
2576 if ans in ("", "c",):
2577 break
2580class FakeAmp(object):
2581 """A Detector-like object that supports returning gain and saturation level
2583 This is used when the input exposure does not have a detector.
2585 Parameters
2586 ----------
2587 exposure : `lsst.afw.image.Exposure`
2588 Exposure to generate a fake amplifier for.
2589 config : `lsst.ip.isr.isrTaskConfig`
2590 Configuration to apply to the fake amplifier.
2591 """
2593 def __init__(self, exposure, config):
2594 self._bbox = exposure.getBBox(afwImage.LOCAL)
2595 self._RawHorizontalOverscanBBox = lsst.geom.Box2I()
2596 self._gain = config.gain
2597 self._readNoise = config.readNoise
2598 self._saturation = config.saturation
2600 def getBBox(self):
2601 return self._bbox
2603 def getRawBBox(self):
2604 return self._bbox
2606 def getRawHorizontalOverscanBBox(self):
2607 return self._RawHorizontalOverscanBBox
2609 def getGain(self):
2610 return self._gain
2612 def getReadNoise(self):
2613 return self._readNoise
2615 def getSaturation(self):
2616 return self._saturation
2618 def getSuspectLevel(self):
2619 return float("NaN")
2622class RunIsrConfig(pexConfig.Config):
2623 isr = pexConfig.ConfigurableField(target=IsrTask, doc="Instrument signature removal")
2626class RunIsrTask(pipeBase.CmdLineTask):
2627 """Task to wrap the default IsrTask to allow it to be retargeted.
2629 The standard IsrTask can be called directly from a command line
2630 program, but doing so removes the ability of the task to be
2631 retargeted. As most cameras override some set of the IsrTask
2632 methods, this would remove those data-specific methods in the
2633 output post-ISR images. This wrapping class fixes the issue,
2634 allowing identical post-ISR images to be generated by both the
2635 processCcd and isrTask code.
2636 """
2637 ConfigClass = RunIsrConfig
2638 _DefaultName = "runIsr"
2640 def __init__(self, *args, **kwargs):
2641 super().__init__(*args, **kwargs)
2642 self.makeSubtask("isr")
2644 def runDataRef(self, dataRef):
2645 """
2646 Parameters
2647 ----------
2648 dataRef : `lsst.daf.persistence.ButlerDataRef`
2649 data reference of the detector data to be processed
2651 Returns
2652 -------
2653 result : `pipeBase.Struct`
2654 Result struct with component:
2656 - exposure : `lsst.afw.image.Exposure`
2657 Post-ISR processed exposure.
2658 """
2659 return self.isr.runDataRef(dataRef)