Coverage for python/lsst/cp/pipe/defects.py: 15%
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« prev ^ index » next coverage.py v6.5.0, created at 2023-03-11 11:19 +0000
1# This file is part of cp_pipe.
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/>.
21#
22import numpy as np
24import lsst.pipe.base as pipeBase
25import lsst.pipe.base.connectionTypes as cT
27from lsstDebug import getDebugFrame
28import lsst.pex.config as pexConfig
30import lsst.afw.image as afwImage
31import lsst.afw.math as afwMath
32import lsst.afw.detection as afwDetection
33import lsst.afw.display as afwDisplay
34from lsst.afw import cameraGeom
35from lsst.geom import Box2I, Point2I
36from lsst.meas.algorithms import SourceDetectionTask
37from lsst.ip.isr import Defects, countMaskedPixels
38from lsst.pex.exceptions import InvalidParameterError
40from ._lookupStaticCalibration import lookupStaticCalibration
42__all__ = ['MeasureDefectsTaskConfig', 'MeasureDefectsTask',
43 'MergeDefectsTaskConfig', 'MergeDefectsTask', ]
46class MeasureDefectsConnections(pipeBase.PipelineTaskConnections,
47 dimensions=("instrument", "exposure", "detector")):
48 inputExp = cT.Input(
49 name="defectExps",
50 doc="Input ISR-processed exposures to measure.",
51 storageClass="Exposure",
52 dimensions=("instrument", "detector", "exposure"),
53 multiple=False
54 )
55 camera = cT.PrerequisiteInput(
56 name='camera',
57 doc="Camera associated with this exposure.",
58 storageClass="Camera",
59 dimensions=("instrument", ),
60 isCalibration=True,
61 lookupFunction=lookupStaticCalibration,
62 )
64 outputDefects = cT.Output(
65 name="singleExpDefects",
66 doc="Output measured defects.",
67 storageClass="Defects",
68 dimensions=("instrument", "detector", "exposure"),
69 )
72class MeasureDefectsTaskConfig(pipeBase.PipelineTaskConfig,
73 pipelineConnections=MeasureDefectsConnections):
74 """Configuration for measuring defects from a list of exposures
75 """
77 nSigmaBright = pexConfig.Field(
78 dtype=float,
79 doc=("Number of sigma above mean for bright pixel detection. The default value was found to be"
80 " appropriate for some LSST sensors in DM-17490."),
81 default=4.8,
82 )
83 nSigmaDark = pexConfig.Field(
84 dtype=float,
85 doc=("Number of sigma below mean for dark pixel detection. The default value was found to be"
86 " appropriate for some LSST sensors in DM-17490."),
87 default=-5.0,
88 )
89 nPixBorderUpDown = pexConfig.Field(
90 dtype=int,
91 doc="Number of pixels to exclude from top & bottom of image when looking for defects.",
92 default=7,
93 )
94 nPixBorderLeftRight = pexConfig.Field(
95 dtype=int,
96 doc="Number of pixels to exclude from left & right of image when looking for defects.",
97 default=7,
98 )
99 badOnAndOffPixelColumnThreshold = pexConfig.Field(
100 dtype=int,
101 doc=("If BPC is the set of all the bad pixels in a given column (not necessarily consecutive) "
102 "and the size of BPC is at least 'badOnAndOffPixelColumnThreshold', all the pixels between the "
103 "pixels that satisfy minY (BPC) and maxY (BPC) will be marked as bad, with 'Y' being the long "
104 "axis of the amplifier (and 'X' the other axis, which for a column is a constant for all "
105 "pixels in the set BPC). If there are more than 'goodPixelColumnGapThreshold' consecutive "
106 "non-bad pixels in BPC, an exception to the above is made and those consecutive "
107 "'goodPixelColumnGapThreshold' are not marked as bad."),
108 default=50,
109 )
110 goodPixelColumnGapThreshold = pexConfig.Field(
111 dtype=int,
112 doc=("Size, in pixels, of usable consecutive pixels in a column with on and off bad pixels (see "
113 "'badOnAndOffPixelColumnThreshold')."),
114 default=30,
115 )
117 def validate(self):
118 super().validate()
119 if self.nSigmaBright < 0.0:
120 raise ValueError("nSigmaBright must be above 0.0.")
121 if self.nSigmaDark > 0.0:
122 raise ValueError("nSigmaDark must be below 0.0.")
125class MeasureDefectsTask(pipeBase.PipelineTask):
126 """Measure the defects from one exposure.
127 """
129 ConfigClass = MeasureDefectsTaskConfig
130 _DefaultName = 'cpDefectMeasure'
132 def run(self, inputExp, camera):
133 """Measure one exposure for defects.
135 Parameters
136 ----------
137 inputExp : `lsst.afw.image.Exposure`
138 Exposure to examine.
139 camera : `lsst.afw.cameraGeom.Camera`
140 Camera to use for metadata.
142 Returns
143 -------
144 results : `lsst.pipe.base.Struct`
145 Results struct containing:
147 ``outputDefects``
148 The defects measured from this exposure
149 (`lsst.ip.isr.Defects`).
150 """
151 detector = inputExp.getDetector()
153 filterName = inputExp.getFilter().physicalLabel
154 datasetType = inputExp.getMetadata().get('IMGTYPE', 'UNKNOWN')
156 if datasetType.lower() == 'dark':
157 nSigmaList = [self.config.nSigmaBright]
158 else:
159 nSigmaList = [self.config.nSigmaBright, self.config.nSigmaDark]
160 defects = self.findHotAndColdPixels(inputExp, nSigmaList)
162 msg = "Found %s defects containing %s pixels in %s"
163 self.log.info(msg, len(defects), self._nPixFromDefects(defects), datasetType)
165 defects.updateMetadataFromExposures([inputExp])
166 defects.updateMetadata(camera=camera, detector=detector, filterName=filterName,
167 setCalibId=True, setDate=True,
168 cpDefectGenImageType=datasetType)
170 return pipeBase.Struct(
171 outputDefects=defects,
172 )
174 @staticmethod
175 def _nPixFromDefects(defects):
176 """Count pixels in a defect.
178 Parameters
179 ----------
180 defects : `lsst.ip.isr.Defects`
181 Defects to measure.
183 Returns
184 -------
185 nPix : `int`
186 Number of defect pixels.
187 """
188 nPix = 0
189 for defect in defects:
190 nPix += defect.getBBox().getArea()
191 return nPix
193 def findHotAndColdPixels(self, exp, nSigma):
194 """Find hot and cold pixels in an image.
196 Using config-defined thresholds on a per-amp basis, mask
197 pixels that are nSigma above threshold in dark frames (hot
198 pixels), or nSigma away from the clipped mean in flats (hot &
199 cold pixels).
201 Parameters
202 ----------
203 exp : `lsst.afw.image.exposure.Exposure`
204 The exposure in which to find defects.
205 nSigma : `list` [`float`]
206 Detection threshold to use. Positive for DETECTED pixels,
207 negative for DETECTED_NEGATIVE pixels.
209 Returns
210 -------
211 defects : `lsst.ip.isr.Defects`
212 The defects found in the image.
213 """
214 self._setEdgeBits(exp)
215 maskedIm = exp.maskedImage
217 # the detection polarity for afwDetection, True for positive,
218 # False for negative, and therefore True for darks as they only have
219 # bright pixels, and both for flats, as they have bright and dark pix
220 footprintList = []
222 for amp in exp.getDetector():
223 ampImg = maskedIm[amp.getBBox()].clone()
225 # crop ampImage depending on where the amp lies in the image
226 if self.config.nPixBorderLeftRight:
227 if ampImg.getX0() == 0:
228 ampImg = ampImg[self.config.nPixBorderLeftRight:, :, afwImage.LOCAL]
229 else:
230 ampImg = ampImg[:-self.config.nPixBorderLeftRight, :, afwImage.LOCAL]
231 if self.config.nPixBorderUpDown:
232 if ampImg.getY0() == 0:
233 ampImg = ampImg[:, self.config.nPixBorderUpDown:, afwImage.LOCAL]
234 else:
235 ampImg = ampImg[:, :-self.config.nPixBorderUpDown, afwImage.LOCAL]
237 if self._getNumGoodPixels(ampImg) == 0: # amp contains no usable pixels
238 continue
240 # Remove a background estimate
241 ampImg -= afwMath.makeStatistics(ampImg, afwMath.MEANCLIP, ).getValue()
243 mergedSet = None
244 for sigma in nSigma:
245 nSig = np.abs(sigma)
246 self.debugHistogram('ampFlux', ampImg, nSig, exp)
247 polarity = {-1: False, 1: True}[np.sign(sigma)]
249 threshold = afwDetection.createThreshold(nSig, 'stdev', polarity=polarity)
251 try:
252 footprintSet = afwDetection.FootprintSet(ampImg, threshold)
253 except InvalidParameterError:
254 # This occurs if the image sigma value is 0.0.
255 # Let's mask the whole area.
256 minValue = np.nanmin(ampImg.image.array) - 1.0
257 threshold = afwDetection.createThreshold(minValue, 'value', polarity=True)
258 footprintSet = afwDetection.FootprintSet(ampImg, threshold)
260 footprintSet.setMask(maskedIm.mask, ("DETECTED" if polarity else "DETECTED_NEGATIVE"))
262 if mergedSet is None:
263 mergedSet = footprintSet
264 else:
265 mergedSet.merge(footprintSet)
267 footprintList += mergedSet.getFootprints()
269 self.debugView('defectMap', ampImg,
270 Defects.fromFootprintList(mergedSet.getFootprints()), exp.getDetector())
272 defects = Defects.fromFootprintList(footprintList)
273 defects = self.maskBlocksIfIntermitentBadPixelsInColumn(defects)
275 return defects
277 @staticmethod
278 def _getNumGoodPixels(maskedIm, badMaskString="NO_DATA"):
279 """Return the number of non-bad pixels in the image."""
280 nPixels = maskedIm.mask.array.size
281 nBad = countMaskedPixels(maskedIm, badMaskString)
282 return nPixels - nBad
284 def _setEdgeBits(self, exposureOrMaskedImage, maskplaneToSet='EDGE'):
285 """Set edge bits on an exposure or maskedImage.
287 Raises
288 ------
289 TypeError
290 Raised if parameter ``exposureOrMaskedImage`` is an invalid type.
291 """
292 if isinstance(exposureOrMaskedImage, afwImage.Exposure):
293 mi = exposureOrMaskedImage.maskedImage
294 elif isinstance(exposureOrMaskedImage, afwImage.MaskedImage):
295 mi = exposureOrMaskedImage
296 else:
297 t = type(exposureOrMaskedImage)
298 raise TypeError(f"Function supports exposure or maskedImage but not {t}")
300 MASKBIT = mi.mask.getPlaneBitMask(maskplaneToSet)
301 if self.config.nPixBorderLeftRight:
302 mi.mask[: self.config.nPixBorderLeftRight, :, afwImage.LOCAL] |= MASKBIT
303 mi.mask[-self.config.nPixBorderLeftRight:, :, afwImage.LOCAL] |= MASKBIT
304 if self.config.nPixBorderUpDown:
305 mi.mask[:, : self.config.nPixBorderUpDown, afwImage.LOCAL] |= MASKBIT
306 mi.mask[:, -self.config.nPixBorderUpDown:, afwImage.LOCAL] |= MASKBIT
308 def maskBlocksIfIntermitentBadPixelsInColumn(self, defects):
309 """Mask blocks in a column if there are on-and-off bad pixels
311 If there's a column with on and off bad pixels, mask all the
312 pixels in between, except if there is a large enough gap of
313 consecutive good pixels between two bad pixels in the column.
315 Parameters
316 ----------
317 defects : `lsst.ip.isr.Defects`
318 The defects found in the image so far
320 Returns
321 -------
322 defects : `lsst.ip.isr.Defects`
323 If the number of bad pixels in a column is not larger or
324 equal than self.config.badPixelColumnThreshold, the input
325 list is returned. Otherwise, the defects list returned
326 will include boxes that mask blocks of on-and-of pixels.
327 """
328 # Get the (x, y) values of each bad pixel in amp.
329 coordinates = []
330 for defect in defects:
331 bbox = defect.getBBox()
332 x0, y0 = bbox.getMinX(), bbox.getMinY()
333 deltaX0, deltaY0 = bbox.getDimensions()
334 for j in np.arange(y0, y0+deltaY0):
335 for i in np.arange(x0, x0 + deltaX0):
336 coordinates.append((i, j))
338 x, y = [], []
339 for coordinatePair in coordinates:
340 x.append(coordinatePair[0])
341 y.append(coordinatePair[1])
343 x = np.array(x)
344 y = np.array(y)
345 # Find the defects with same "x" (vertical) coordinate (column).
346 unique, counts = np.unique(x, return_counts=True)
347 multipleX = []
348 for (a, b) in zip(unique, counts):
349 if b >= self.config.badOnAndOffPixelColumnThreshold:
350 multipleX.append(a)
351 if len(multipleX) != 0:
352 defects = self._markBlocksInBadColumn(x, y, multipleX, defects)
354 return defects
356 def _markBlocksInBadColumn(self, x, y, multipleX, defects):
357 """Mask blocks in a column if number of on-and-off bad pixels is above
358 threshold.
360 This function is called if the number of on-and-off bad pixels
361 in a column is larger or equal than
362 self.config.badOnAndOffPixelColumnThreshold.
364 Parameters
365 ---------
366 x : `list`
367 Lower left x coordinate of defect box. x coordinate is
368 along the short axis if amp.
369 y : `list`
370 Lower left y coordinate of defect box. x coordinate is
371 along the long axis if amp.
372 multipleX : list
373 List of x coordinates in amp. with multiple bad pixels
374 (i.e., columns with defects).
375 defects : `lsst.ip.isr.Defects`
376 The defcts found in the image so far
378 Returns
379 -------
380 defects : `lsst.ip.isr.Defects`
381 The defects list returned that will include boxes that
382 mask blocks of on-and-of pixels.
383 """
384 with defects.bulk_update():
385 goodPixelColumnGapThreshold = self.config.goodPixelColumnGapThreshold
386 for x0 in multipleX:
387 index = np.where(x == x0)
388 multipleY = y[index] # multipleY and multipleX are in 1-1 correspondence.
389 multipleY.sort() # Ensure that the y values are sorted to look for gaps.
390 minY, maxY = np.min(multipleY), np.max(multipleY)
391 # Next few lines: don't mask pixels in column if gap
392 # of good pixels between two consecutive bad pixels is
393 # larger or equal than 'goodPixelColumnGapThreshold'.
394 diffIndex = np.where(np.diff(multipleY) >= goodPixelColumnGapThreshold)[0]
395 if len(diffIndex) != 0:
396 limits = [minY] # put the minimum first
397 for gapIndex in diffIndex:
398 limits.append(multipleY[gapIndex])
399 limits.append(multipleY[gapIndex+1])
400 limits.append(maxY) # maximum last
401 for i in np.arange(0, len(limits)-1, 2):
402 s = Box2I(minimum=Point2I(x0, limits[i]), maximum=Point2I(x0, limits[i+1]))
403 defects.append(s)
404 else: # No gap is large enough
405 s = Box2I(minimum=Point2I(x0, minY), maximum=Point2I(x0, maxY))
406 defects.append(s)
407 return defects
409 def debugView(self, stepname, ampImage, defects, detector): # pragma: no cover
410 """Plot the defects found by the task.
412 Parameters
413 ----------
414 stepname : `str`
415 Debug frame to request.
416 ampImage : `lsst.afw.image.MaskedImage`
417 Amplifier image to display.
418 defects : `lsst.ip.isr.Defects`
419 The defects to plot.
420 detector : `lsst.afw.cameraGeom.Detector`
421 Detector holding camera geometry.
422 """
423 frame = getDebugFrame(self._display, stepname)
424 if frame:
425 disp = afwDisplay.Display(frame=frame)
426 disp.scale('asinh', 'zscale')
427 disp.setMaskTransparency(80)
428 disp.setMaskPlaneColor("BAD", afwDisplay.RED)
430 maskedIm = ampImage.clone()
431 defects.maskPixels(maskedIm, "BAD")
433 mpDict = maskedIm.mask.getMaskPlaneDict()
434 for plane in mpDict.keys():
435 if plane in ['BAD']:
436 continue
437 disp.setMaskPlaneColor(plane, afwDisplay.IGNORE)
439 disp.setImageColormap('gray')
440 disp.mtv(maskedIm)
441 cameraGeom.utils.overlayCcdBoxes(detector, isTrimmed=True, display=disp)
442 prompt = "Press Enter to continue [c]... "
443 while True:
444 ans = input(prompt).lower()
445 if ans in ('', 'c', ):
446 break
448 def debugHistogram(self, stepname, ampImage, nSigmaUsed, exp):
449 """Make a histogram of the distribution of pixel values for
450 each amp.
452 The main image data histogram is plotted in blue. Edge
453 pixels, if masked, are in red. Note that masked edge pixels
454 do not contribute to the underflow and overflow numbers.
456 Note that this currently only supports the 16-amp LSST
457 detectors.
459 Parameters
460 ----------
461 stepname : `str`
462 Debug frame to request.
463 ampImage : `lsst.afw.image.MaskedImage`
464 Amplifier image to display.
465 nSigmaUsed : `float`
466 The number of sigma used for detection
467 exp : `lsst.afw.image.exposure.Exposure`
468 The exposure in which the defects were found.
469 """
470 frame = getDebugFrame(self._display, stepname)
471 if frame:
472 import matplotlib.pyplot as plt
474 detector = exp.getDetector()
475 nX = np.floor(np.sqrt(len(detector)))
476 nY = len(detector) // nX
477 fig, ax = plt.subplots(nrows=int(nY), ncols=int(nX), sharex='col', sharey='row', figsize=(13, 10))
479 expTime = exp.getInfo().getVisitInfo().getExposureTime()
481 for (amp, a) in zip(reversed(detector), ax.flatten()):
482 mi = exp.maskedImage[amp.getBBox()]
484 # normalize by expTime as we plot in ADU/s and don't
485 # always work with master calibs
486 mi.image.array /= expTime
487 stats = afwMath.makeStatistics(mi, afwMath.MEANCLIP | afwMath.STDEVCLIP)
488 mean, sigma = stats.getValue(afwMath.MEANCLIP), stats.getValue(afwMath.STDEVCLIP)
489 # Get array of pixels
490 EDGEBIT = exp.maskedImage.mask.getPlaneBitMask("EDGE")
491 imgData = mi.image.array[(mi.mask.array & EDGEBIT) == 0].flatten()
492 edgeData = mi.image.array[(mi.mask.array & EDGEBIT) != 0].flatten()
494 thrUpper = mean + nSigmaUsed*sigma
495 thrLower = mean - nSigmaUsed*sigma
497 nRight = len(imgData[imgData > thrUpper])
498 nLeft = len(imgData[imgData < thrLower])
500 nsig = nSigmaUsed + 1.2 # add something small so the edge of the plot is out from level used
501 leftEdge = mean - nsig * nSigmaUsed*sigma
502 rightEdge = mean + nsig * nSigmaUsed*sigma
503 nbins = np.linspace(leftEdge, rightEdge, 1000)
504 ey, bin_borders, patches = a.hist(edgeData, histtype='step', bins=nbins,
505 lw=1, edgecolor='red')
506 y, bin_borders, patches = a.hist(imgData, histtype='step', bins=nbins,
507 lw=3, edgecolor='blue')
509 # Report number of entries in over- and under-flow
510 # bins, i.e. off the edges of the histogram
511 nOverflow = len(imgData[imgData > rightEdge])
512 nUnderflow = len(imgData[imgData < leftEdge])
514 # Put v-lines and textboxes in
515 a.axvline(thrUpper, c='k')
516 a.axvline(thrLower, c='k')
517 msg = f"{amp.getName()}\nmean:{mean: .2f}\n$\\sigma$:{sigma: .2f}"
518 a.text(0.65, 0.6, msg, transform=a.transAxes, fontsize=11)
519 msg = f"nLeft:{nLeft}\nnRight:{nRight}\nnOverflow:{nOverflow}\nnUnderflow:{nUnderflow}"
520 a.text(0.03, 0.6, msg, transform=a.transAxes, fontsize=11.5)
522 # set axis limits and scales
523 a.set_ylim([1., 1.7*np.max(y)])
524 lPlot, rPlot = a.get_xlim()
525 a.set_xlim(np.array([lPlot, rPlot]))
526 a.set_yscale('log')
527 a.set_xlabel("ADU/s")
528 fig.show()
529 prompt = "Press Enter or c to continue [chp]..."
530 while True:
531 ans = input(prompt).lower()
532 if ans in ("", " ", "c",):
533 break
534 elif ans in ("p", ):
535 import pdb
536 pdb.set_trace()
537 elif ans in ("h", ):
538 print("[h]elp [c]ontinue [p]db")
539 plt.close()
542class MergeDefectsConnections(pipeBase.PipelineTaskConnections,
543 dimensions=("instrument", "detector")):
544 inputDefects = cT.Input(
545 name="singleExpDefects",
546 doc="Measured defect lists.",
547 storageClass="Defects",
548 dimensions=("instrument", "detector", "exposure"),
549 multiple=True,
550 )
551 camera = cT.PrerequisiteInput(
552 name='camera',
553 doc="Camera associated with these defects.",
554 storageClass="Camera",
555 dimensions=("instrument", ),
556 isCalibration=True,
557 lookupFunction=lookupStaticCalibration,
558 )
560 mergedDefects = cT.Output(
561 name="defects",
562 doc="Final merged defects.",
563 storageClass="Defects",
564 dimensions=("instrument", "detector"),
565 multiple=False,
566 isCalibration=True,
567 )
570class MergeDefectsTaskConfig(pipeBase.PipelineTaskConfig,
571 pipelineConnections=MergeDefectsConnections):
572 """Configuration for merging single exposure defects.
573 """
575 assertSameRun = pexConfig.Field(
576 dtype=bool,
577 doc=("Ensure that all visits are from the same run? Raises if this is not the case, or "
578 "if the run key isn't found."),
579 default=False, # false because most obs_packages don't have runs. obs_lsst/ts8 overrides this.
580 )
581 ignoreFilters = pexConfig.Field(
582 dtype=bool,
583 doc=("Set the filters used in the CALIB_ID to NONE regardless of the filters on the input"
584 " images. Allows mixing of filters in the input flats. Set to False if you think"
585 " your defects might be chromatic and want to have registry support for varying"
586 " defects with respect to filter."),
587 default=True,
588 )
589 nullFilterName = pexConfig.Field(
590 dtype=str,
591 doc=("The name of the null filter if ignoreFilters is True. Usually something like NONE or EMPTY"),
592 default="NONE",
593 )
594 combinationMode = pexConfig.ChoiceField(
595 doc="Which types of defects to identify",
596 dtype=str,
597 default="FRACTION",
598 allowed={
599 "AND": "Logical AND the pixels found in each visit to form set ",
600 "OR": "Logical OR the pixels found in each visit to form set ",
601 "FRACTION": "Use pixels found in more than config.combinationFraction of visits ",
602 }
603 )
604 combinationFraction = pexConfig.RangeField(
605 dtype=float,
606 doc=("The fraction (0..1) of visits in which a pixel was found to be defective across"
607 " the visit list in order to be marked as a defect. Note, upper bound is exclusive, so use"
608 " mode AND to require pixel to appear in all images."),
609 default=0.7,
610 min=0,
611 max=1,
612 )
613 edgesAsDefects = pexConfig.Field(
614 dtype=bool,
615 doc=("Mark all edge pixels, as defined by nPixBorder[UpDown, LeftRight], as defects."
616 " Normal treatment is to simply exclude this region from the defect finding, such that no"
617 " defect will be located there."),
618 default=False,
619 )
622class MergeDefectsTask(pipeBase.PipelineTask):
623 """Merge the defects from multiple exposures.
624 """
626 ConfigClass = MergeDefectsTaskConfig
627 _DefaultName = 'cpDefectMerge'
629 def run(self, inputDefects, camera):
630 """Merge a list of single defects to find the common defect regions.
632 Parameters
633 ----------
634 inputDefects : `list` [`lsst.ip.isr.Defects`]
635 Partial defects from a single exposure.
636 camera : `lsst.afw.cameraGeom.Camera`
637 Camera to use for metadata.
639 Returns
640 -------
641 results : `lsst.pipe.base.Struct`
642 Results struct containing:
644 ``mergedDefects``
645 The defects merged from the input lists
646 (`lsst.ip.isr.Defects`).
647 """
648 detectorId = inputDefects[0].getMetadata().get('DETECTOR', None)
649 if detectorId is None:
650 raise RuntimeError("Cannot identify detector id.")
651 detector = camera[detectorId]
653 imageTypes = set()
654 for inDefect in inputDefects:
655 imageType = inDefect.getMetadata().get('cpDefectGenImageType', 'UNKNOWN')
656 imageTypes.add(imageType)
658 # Determine common defect pixels separately for each input image type.
659 splitDefects = list()
660 for imageType in imageTypes:
661 sumImage = afwImage.MaskedImageF(detector.getBBox())
662 count = 0
663 for inDefect in inputDefects:
664 if imageType == inDefect.getMetadata().get('cpDefectGenImageType', 'UNKNOWN'):
665 count += 1
666 for defect in inDefect:
667 sumImage.image[defect.getBBox()] += 1.0
668 sumImage /= count
669 nDetected = len(np.where(sumImage.getImage().getArray() > 0)[0])
670 self.log.info("Pre-merge %s pixels with non-zero detections for %s" % (nDetected, imageType))
672 if self.config.combinationMode == 'AND':
673 threshold = 1.0
674 elif self.config.combinationMode == 'OR':
675 threshold = 0.0
676 elif self.config.combinationMode == 'FRACTION':
677 threshold = self.config.combinationFraction
678 else:
679 raise RuntimeError(f"Got unsupported combinationMode {self.config.combinationMode}")
680 indices = np.where(sumImage.getImage().getArray() > threshold)
681 BADBIT = sumImage.getMask().getPlaneBitMask('BAD')
682 sumImage.getMask().getArray()[indices] |= BADBIT
683 self.log.info("Post-merge %s pixels marked as defects for %s" % (len(indices[0]), imageType))
684 partialDefect = Defects.fromMask(sumImage, 'BAD')
685 splitDefects.append(partialDefect)
687 # Do final combination of separate image types
688 finalImage = afwImage.MaskedImageF(detector.getBBox())
689 for inDefect in splitDefects:
690 for defect in inDefect:
691 finalImage.image[defect.getBBox()] += 1
692 finalImage /= len(splitDefects)
693 nDetected = len(np.where(finalImage.getImage().getArray() > 0)[0])
694 self.log.info("Pre-final merge %s pixels with non-zero detections" % (nDetected, ))
696 # This combination is the OR of all image types
697 threshold = 0.0
698 indices = np.where(finalImage.getImage().getArray() > threshold)
699 BADBIT = finalImage.getMask().getPlaneBitMask('BAD')
700 finalImage.getMask().getArray()[indices] |= BADBIT
701 self.log.info("Post-final merge %s pixels marked as defects" % (len(indices[0]), ))
703 if self.config.edgesAsDefects:
704 self.log.info("Masking edge pixels as defects.")
705 # Do the same as IsrTask.maskEdges()
706 box = detector.getBBox()
707 subImage = finalImage[box]
708 box.grow(-self.nPixBorder)
709 SourceDetectionTask.setEdgeBits(subImage, box, BADBIT)
711 merged = Defects.fromMask(finalImage, 'BAD')
712 merged.updateMetadataFromExposures(inputDefects)
713 merged.updateMetadata(camera=camera, detector=detector, filterName=None,
714 setCalibId=True, setDate=True)
716 return pipeBase.Struct(
717 mergedDefects=merged,
718 )