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1#
2# LSST Data Management System
3# Copyright 2008, 2009, 2010 LSST Corporation.
4#
5# This product includes software developed by the
6# LSST Project (http://www.lsst.org/).
7#
8# This program is free software: you can redistribute it and/or modify
9# it under the terms of the GNU General Public License as published by
10# the Free Software Foundation, either version 3 of the License, or
11# (at your option) any later version.
12#
13# This program is distributed in the hope that it will be useful,
14# but WITHOUT ANY WARRANTY; without even the implied warranty of
15# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
16# GNU General Public License for more details.
17#
18# You should have received a copy of the LSST License Statement and
19# the GNU General Public License along with this program. If not,
20# see <http://www.lsstcorp.org/LegalNotices/>.
21#
22import math
23import numpy
24from deprecated.sphinx import deprecated
26import lsst.geom
27import lsst.afw.image as afwImage
28import lsst.afw.detection as afwDetection
29import lsst.afw.math as afwMath
30import lsst.meas.algorithms as measAlg
31import lsst.pex.exceptions as pexExcept
32import lsst.afw.cameraGeom as camGeom
34from lsst.afw.geom.wcsUtils import makeDistortedTanWcs
35from lsst.meas.algorithms.detection import SourceDetectionTask
36from lsst.pipe.base import Struct
38from contextlib import contextmanager
41def createPsf(fwhm):
42 """Make a double Gaussian PSF.
44 Parameters
45 ----------
46 fwhm : scalar
47 FWHM of double Gaussian smoothing kernel.
49 Returns
50 -------
51 psf : `lsst.meas.algorithms.DoubleGaussianPsf`
52 The created smoothing kernel.
53 """
54 ksize = 4*int(fwhm) + 1
55 return measAlg.DoubleGaussianPsf(ksize, ksize, fwhm/(2*math.sqrt(2*math.log(2))))
58def transposeMaskedImage(maskedImage):
59 """Make a transposed copy of a masked image.
61 Parameters
62 ----------
63 maskedImage : `lsst.afw.image.MaskedImage`
64 Image to process.
66 Returns
67 -------
68 transposed : `lsst.afw.image.MaskedImage`
69 The transposed copy of the input image.
70 """
71 transposed = maskedImage.Factory(lsst.geom.Extent2I(maskedImage.getHeight(), maskedImage.getWidth()))
72 transposed.getImage().getArray()[:] = maskedImage.getImage().getArray().T
73 transposed.getMask().getArray()[:] = maskedImage.getMask().getArray().T
74 transposed.getVariance().getArray()[:] = maskedImage.getVariance().getArray().T
75 return transposed
78def interpolateDefectList(maskedImage, defectList, fwhm, fallbackValue=None):
79 """Interpolate over defects specified in a defect list.
81 Parameters
82 ----------
83 maskedImage : `lsst.afw.image.MaskedImage`
84 Image to process.
85 defectList : `lsst.meas.algorithms.Defects`
86 List of defects to interpolate over.
87 fwhm : scalar
88 FWHM of double Gaussian smoothing kernel.
89 fallbackValue : scalar, optional
90 Fallback value if an interpolated value cannot be determined.
91 If None, then the clipped mean of the image is used.
92 """
93 psf = createPsf(fwhm)
94 if fallbackValue is None:
95 fallbackValue = afwMath.makeStatistics(maskedImage.getImage(), afwMath.MEANCLIP).getValue()
96 if 'INTRP' not in maskedImage.getMask().getMaskPlaneDict():
97 maskedImage.getMask().addMaskPlane('INTRP')
98 measAlg.interpolateOverDefects(maskedImage, psf, defectList, fallbackValue, True)
99 return maskedImage
102def makeThresholdMask(maskedImage, threshold, growFootprints=1, maskName='SAT'):
103 """Mask pixels based on threshold detection.
105 Parameters
106 ----------
107 maskedImage : `lsst.afw.image.MaskedImage`
108 Image to process. Only the mask plane is updated.
109 threshold : scalar
110 Detection threshold.
111 growFootprints : scalar, optional
112 Number of pixels to grow footprints of detected regions.
113 maskName : str, optional
114 Mask plane name, or list of names to convert
116 Returns
117 -------
118 defectList : `lsst.meas.algorithms.Defects`
119 Defect list constructed from pixels above the threshold.
120 """
121 # find saturated regions
122 thresh = afwDetection.Threshold(threshold)
123 fs = afwDetection.FootprintSet(maskedImage, thresh)
125 if growFootprints > 0:
126 fs = afwDetection.FootprintSet(fs, rGrow=growFootprints, isotropic=False)
127 fpList = fs.getFootprints()
129 # set mask
130 mask = maskedImage.getMask()
131 bitmask = mask.getPlaneBitMask(maskName)
132 afwDetection.setMaskFromFootprintList(mask, fpList, bitmask)
134 return measAlg.Defects.fromFootprintList(fpList)
137def growMasks(mask, radius=0, maskNameList=['BAD'], maskValue="BAD"):
138 """Grow a mask by an amount and add to the requested plane.
140 Parameters
141 ----------
142 mask : `lsst.afw.image.Mask`
143 Mask image to process.
144 radius : scalar
145 Amount to grow the mask.
146 maskNameList : `str` or `list` [`str`]
147 Mask names that should be grown.
148 maskValue : `str`
149 Mask plane to assign the newly masked pixels to.
150 """
151 if radius > 0:
152 thresh = afwDetection.Threshold(mask.getPlaneBitMask(maskNameList), afwDetection.Threshold.BITMASK)
153 fpSet = afwDetection.FootprintSet(mask, thresh)
154 fpSet = afwDetection.FootprintSet(fpSet, rGrow=radius, isotropic=False)
155 fpSet.setMask(mask, maskValue)
158def interpolateFromMask(maskedImage, fwhm, growSaturatedFootprints=1,
159 maskNameList=['SAT'], fallbackValue=None):
160 """Interpolate over defects identified by a particular set of mask planes.
162 Parameters
163 ----------
164 maskedImage : `lsst.afw.image.MaskedImage`
165 Image to process.
166 fwhm : scalar
167 FWHM of double Gaussian smoothing kernel.
168 growSaturatedFootprints : scalar, optional
169 Number of pixels to grow footprints for saturated pixels.
170 maskNameList : `List` of `str`, optional
171 Mask plane name.
172 fallbackValue : scalar, optional
173 Value of last resort for interpolation.
174 """
175 mask = maskedImage.getMask()
177 if growSaturatedFootprints > 0 and "SAT" in maskNameList:
178 # If we are interpolating over an area larger than the original masked region, we need
179 # to expand the original mask bit to the full area to explain why we interpolated there.
180 growMasks(mask, radius=growSaturatedFootprints, maskNameList=['SAT'], maskValue="SAT")
182 thresh = afwDetection.Threshold(mask.getPlaneBitMask(maskNameList), afwDetection.Threshold.BITMASK)
183 fpSet = afwDetection.FootprintSet(mask, thresh)
184 defectList = measAlg.Defects.fromFootprintList(fpSet.getFootprints())
186 interpolateDefectList(maskedImage, defectList, fwhm, fallbackValue=fallbackValue)
188 return maskedImage
191def saturationCorrection(maskedImage, saturation, fwhm, growFootprints=1, interpolate=True, maskName='SAT',
192 fallbackValue=None):
193 """Mark saturated pixels and optionally interpolate over them
195 Parameters
196 ----------
197 maskedImage : `lsst.afw.image.MaskedImage`
198 Image to process.
199 saturation : scalar
200 Saturation level used as the detection threshold.
201 fwhm : scalar
202 FWHM of double Gaussian smoothing kernel.
203 growFootprints : scalar, optional
204 Number of pixels to grow footprints of detected regions.
205 interpolate : Bool, optional
206 If True, saturated pixels are interpolated over.
207 maskName : str, optional
208 Mask plane name.
209 fallbackValue : scalar, optional
210 Value of last resort for interpolation.
211 """
212 defectList = makeThresholdMask(
213 maskedImage=maskedImage,
214 threshold=saturation,
215 growFootprints=growFootprints,
216 maskName=maskName,
217 )
218 if interpolate:
219 interpolateDefectList(maskedImage, defectList, fwhm, fallbackValue=fallbackValue)
221 return maskedImage
224def trimToMatchCalibBBox(rawMaskedImage, calibMaskedImage):
225 """Compute number of edge trim pixels to match the calibration data.
227 Use the dimension difference between the raw exposure and the
228 calibration exposure to compute the edge trim pixels. This trim
229 is applied symmetrically, with the same number of pixels masked on
230 each side.
232 Parameters
233 ----------
234 rawMaskedImage : `lsst.afw.image.MaskedImage`
235 Image to trim.
236 calibMaskedImage : `lsst.afw.image.MaskedImage`
237 Calibration image to draw new bounding box from.
239 Returns
240 -------
241 replacementMaskedImage : `lsst.afw.image.MaskedImage`
242 ``rawMaskedImage`` trimmed to the appropriate size
243 Raises
244 ------
245 RuntimeError
246 Rasied if ``rawMaskedImage`` cannot be symmetrically trimmed to
247 match ``calibMaskedImage``.
248 """
249 nx, ny = rawMaskedImage.getBBox().getDimensions() - calibMaskedImage.getBBox().getDimensions()
250 if nx != ny:
251 raise RuntimeError("Raw and calib maskedImages are trimmed differently in X and Y.")
252 if nx % 2 != 0:
253 raise RuntimeError("Calibration maskedImage is trimmed unevenly in X.")
254 if nx < 0:
255 raise RuntimeError("Calibration maskedImage is larger than raw data.")
257 nEdge = nx//2
258 if nEdge > 0:
259 replacementMaskedImage = rawMaskedImage[nEdge:-nEdge, nEdge:-nEdge, afwImage.LOCAL]
260 SourceDetectionTask.setEdgeBits(
261 rawMaskedImage,
262 replacementMaskedImage.getBBox(),
263 rawMaskedImage.getMask().getPlaneBitMask("EDGE")
264 )
265 else:
266 replacementMaskedImage = rawMaskedImage
268 return replacementMaskedImage
271def biasCorrection(maskedImage, biasMaskedImage, trimToFit=False):
272 """Apply bias correction in place.
274 Parameters
275 ----------
276 maskedImage : `lsst.afw.image.MaskedImage`
277 Image to process. The image is modified by this method.
278 biasMaskedImage : `lsst.afw.image.MaskedImage`
279 Bias image of the same size as ``maskedImage``
280 trimToFit : `Bool`, optional
281 If True, raw data is symmetrically trimmed to match
282 calibration size.
284 Raises
285 ------
286 RuntimeError
287 Raised if ``maskedImage`` and ``biasMaskedImage`` do not have
288 the same size.
290 """
291 if trimToFit:
292 maskedImage = trimToMatchCalibBBox(maskedImage, biasMaskedImage)
294 if maskedImage.getBBox(afwImage.LOCAL) != biasMaskedImage.getBBox(afwImage.LOCAL):
295 raise RuntimeError("maskedImage bbox %s != biasMaskedImage bbox %s" %
296 (maskedImage.getBBox(afwImage.LOCAL), biasMaskedImage.getBBox(afwImage.LOCAL)))
297 maskedImage -= biasMaskedImage
300def darkCorrection(maskedImage, darkMaskedImage, expScale, darkScale, invert=False, trimToFit=False):
301 """Apply dark correction in place.
303 Parameters
304 ----------
305 maskedImage : `lsst.afw.image.MaskedImage`
306 Image to process. The image is modified by this method.
307 darkMaskedImage : `lsst.afw.image.MaskedImage`
308 Dark image of the same size as ``maskedImage``.
309 expScale : scalar
310 Dark exposure time for ``maskedImage``.
311 darkScale : scalar
312 Dark exposure time for ``darkMaskedImage``.
313 invert : `Bool`, optional
314 If True, re-add the dark to an already corrected image.
315 trimToFit : `Bool`, optional
316 If True, raw data is symmetrically trimmed to match
317 calibration size.
319 Raises
320 ------
321 RuntimeError
322 Raised if ``maskedImage`` and ``darkMaskedImage`` do not have
323 the same size.
325 Notes
326 -----
327 The dark correction is applied by calculating:
328 maskedImage -= dark * expScaling / darkScaling
329 """
330 if trimToFit:
331 maskedImage = trimToMatchCalibBBox(maskedImage, darkMaskedImage)
333 if maskedImage.getBBox(afwImage.LOCAL) != darkMaskedImage.getBBox(afwImage.LOCAL):
334 raise RuntimeError("maskedImage bbox %s != darkMaskedImage bbox %s" %
335 (maskedImage.getBBox(afwImage.LOCAL), darkMaskedImage.getBBox(afwImage.LOCAL)))
337 scale = expScale / darkScale
338 if not invert:
339 maskedImage.scaledMinus(scale, darkMaskedImage)
340 else:
341 maskedImage.scaledPlus(scale, darkMaskedImage)
344def updateVariance(maskedImage, gain, readNoise):
345 """Set the variance plane based on the image plane.
347 Parameters
348 ----------
349 maskedImage : `lsst.afw.image.MaskedImage`
350 Image to process. The variance plane is modified.
351 gain : scalar
352 The amplifier gain in electrons/ADU.
353 readNoise : scalar
354 The amplifier read nmoise in ADU/pixel.
355 """
356 var = maskedImage.getVariance()
357 var[:] = maskedImage.getImage()
358 var /= gain
359 var += readNoise**2
362def flatCorrection(maskedImage, flatMaskedImage, scalingType, userScale=1.0, invert=False, trimToFit=False):
363 """Apply flat correction in place.
365 Parameters
366 ----------
367 maskedImage : `lsst.afw.image.MaskedImage`
368 Image to process. The image is modified.
369 flatMaskedImage : `lsst.afw.image.MaskedImage`
370 Flat image of the same size as ``maskedImage``
371 scalingType : str
372 Flat scale computation method. Allowed values are 'MEAN',
373 'MEDIAN', or 'USER'.
374 userScale : scalar, optional
375 Scale to use if ``scalingType``='USER'.
376 invert : `Bool`, optional
377 If True, unflatten an already flattened image.
378 trimToFit : `Bool`, optional
379 If True, raw data is symmetrically trimmed to match
380 calibration size.
382 Raises
383 ------
384 RuntimeError
385 Raised if ``maskedImage`` and ``flatMaskedImage`` do not have
386 the same size or if ``scalingType`` is not an allowed value.
387 """
388 if trimToFit:
389 maskedImage = trimToMatchCalibBBox(maskedImage, flatMaskedImage)
391 if maskedImage.getBBox(afwImage.LOCAL) != flatMaskedImage.getBBox(afwImage.LOCAL):
392 raise RuntimeError("maskedImage bbox %s != flatMaskedImage bbox %s" %
393 (maskedImage.getBBox(afwImage.LOCAL), flatMaskedImage.getBBox(afwImage.LOCAL)))
395 # Figure out scale from the data
396 # Ideally the flats are normalized by the calibration product pipeline, but this allows some flexibility
397 # in the case that the flat is created by some other mechanism.
398 if scalingType in ('MEAN', 'MEDIAN'):
399 scalingType = afwMath.stringToStatisticsProperty(scalingType)
400 flatScale = afwMath.makeStatistics(flatMaskedImage.image, scalingType).getValue()
401 elif scalingType == 'USER':
402 flatScale = userScale
403 else:
404 raise RuntimeError('%s : %s not implemented' % ("flatCorrection", scalingType))
406 if not invert:
407 maskedImage.scaledDivides(1.0/flatScale, flatMaskedImage)
408 else:
409 maskedImage.scaledMultiplies(1.0/flatScale, flatMaskedImage)
412def illuminationCorrection(maskedImage, illumMaskedImage, illumScale, trimToFit=True):
413 """Apply illumination correction in place.
415 Parameters
416 ----------
417 maskedImage : `lsst.afw.image.MaskedImage`
418 Image to process. The image is modified.
419 illumMaskedImage : `lsst.afw.image.MaskedImage`
420 Illumination correction image of the same size as ``maskedImage``.
421 illumScale : scalar
422 Scale factor for the illumination correction.
423 trimToFit : `Bool`, optional
424 If True, raw data is symmetrically trimmed to match
425 calibration size.
427 Raises
428 ------
429 RuntimeError
430 Raised if ``maskedImage`` and ``illumMaskedImage`` do not have
431 the same size.
432 """
433 if trimToFit:
434 maskedImage = trimToMatchCalibBBox(maskedImage, illumMaskedImage)
436 if maskedImage.getBBox(afwImage.LOCAL) != illumMaskedImage.getBBox(afwImage.LOCAL):
437 raise RuntimeError("maskedImage bbox %s != illumMaskedImage bbox %s" %
438 (maskedImage.getBBox(afwImage.LOCAL), illumMaskedImage.getBBox(afwImage.LOCAL)))
440 maskedImage.scaledDivides(1.0/illumScale, illumMaskedImage)
443def overscanCorrection(ampMaskedImage, overscanImage, fitType='MEDIAN', order=1, collapseRej=3.0,
444 statControl=None, overscanIsInt=True):
445 """Apply overscan correction in place.
447 Parameters
448 ----------
449 ampMaskedImage : `lsst.afw.image.MaskedImage`
450 Image of amplifier to correct; modified.
451 overscanImage : `lsst.afw.image.Image` or `lsst.afw.image.MaskedImage`
452 Image of overscan; modified.
453 fitType : `str`
454 Type of fit for overscan correction. May be one of:
456 - ``MEAN``: use mean of overscan.
457 - ``MEANCLIP``: use clipped mean of overscan.
458 - ``MEDIAN``: use median of overscan.
459 - ``MEDIAN_PER_ROW``: use median per row of overscan.
460 - ``POLY``: fit with ordinary polynomial.
461 - ``CHEB``: fit with Chebyshev polynomial.
462 - ``LEG``: fit with Legendre polynomial.
463 - ``NATURAL_SPLINE``: fit with natural spline.
464 - ``CUBIC_SPLINE``: fit with cubic spline.
465 - ``AKIMA_SPLINE``: fit with Akima spline.
467 order : `int`
468 Polynomial order or number of spline knots; ignored unless
469 ``fitType`` indicates a polynomial or spline.
470 statControl : `lsst.afw.math.StatisticsControl`
471 Statistics control object. In particular, we pay attention to numSigmaClip
472 overscanIsInt : `bool`
473 Treat the overscan region as consisting of integers, even if it's been
474 converted to float. E.g. handle ties properly.
476 Returns
477 -------
478 result : `lsst.pipe.base.Struct`
479 Result struct with components:
481 - ``imageFit``: Value(s) removed from image (scalar or
482 `lsst.afw.image.Image`)
483 - ``overscanFit``: Value(s) removed from overscan (scalar or
484 `lsst.afw.image.Image`)
485 - ``overscanImage``: Overscan corrected overscan region
486 (`lsst.afw.image.Image`)
487 Raises
488 ------
489 pexExcept.Exception
490 Raised if ``fitType`` is not an allowed value.
492 Notes
493 -----
494 The ``ampMaskedImage`` and ``overscanImage`` are modified, with the fit
495 subtracted. Note that the ``overscanImage`` should not be a subimage of
496 the ``ampMaskedImage``, to avoid being subtracted twice.
498 Debug plots are available for the SPLINE fitTypes by setting the
499 `debug.display` for `name` == "lsst.ip.isr.isrFunctions". These
500 plots show the scatter plot of the overscan data (collapsed along
501 the perpendicular dimension) as a function of position on the CCD
502 (normalized between +/-1).
503 """
504 ampImage = ampMaskedImage.getImage()
505 if statControl is None:
506 statControl = afwMath.StatisticsControl()
508 numSigmaClip = statControl.getNumSigmaClip()
509 if fitType in ('MEAN', 'MEANCLIP'):
510 fitType = afwMath.stringToStatisticsProperty(fitType)
511 offImage = afwMath.makeStatistics(overscanImage, fitType, statControl).getValue()
512 overscanFit = offImage
513 elif fitType in ('MEDIAN', 'MEDIAN_PER_ROW',):
514 if overscanIsInt:
515 # we need an image with integer pixels to handle ties properly
516 if hasattr(overscanImage, "image"):
517 imageI = overscanImage.image.convertI()
518 overscanImageI = afwImage.MaskedImageI(imageI, overscanImage.mask, overscanImage.variance)
519 else:
520 overscanImageI = overscanImage.convertI()
521 else:
522 overscanImageI = overscanImage
523 if fitType in ('MEDIAN',):
524 fitTypeStats = afwMath.stringToStatisticsProperty(fitType)
525 offImage = afwMath.makeStatistics(overscanImageI, fitTypeStats, statControl).getValue()
526 overscanFit = offImage
527 elif fitType in ('MEDIAN_PER_ROW',):
528 if hasattr(overscanImageI, "getImage"):
529 biasArray = overscanImageI.getImage().getArray()
530 else:
531 biasArray = overscanImageI.getArray()
532 shortInd = numpy.argmin(biasArray.shape)
533 if shortInd == 0:
534 # Convert to some 'standard' representation to make things easier
535 biasArray = numpy.transpose(biasArray)
537 fitTypeStats = afwMath.stringToStatisticsProperty('MEDIAN')
538 collapsed = []
539 for row in biasArray:
540 rowMedian = afwMath.makeStatistics(row, fitTypeStats, statControl).getValue()
541 collapsed.append(rowMedian)
542 collapsed = numpy.array(collapsed)
543 offImage = ampImage.Factory(ampImage.getDimensions())
544 offArray = offImage.getArray()
545 overscanFit = afwImage.ImageF(overscanImage.getDimensions())
546 overscanArray = overscanFit.getArray()
548 if shortInd == 1:
549 offArray[:, :] = collapsed[:, numpy.newaxis]
550 overscanArray[:, :] = collapsed[:, numpy.newaxis]
551 else:
552 offArray[:, :] = collapsed[numpy.newaxis, :]
553 overscanArray[:, :] = collapsed[numpy.newaxis, :]
555 del collapsed, biasArray
557 if overscanIsInt:
558 del overscanImageI
559 elif fitType in ('POLY', 'CHEB', 'LEG', 'NATURAL_SPLINE', 'CUBIC_SPLINE', 'AKIMA_SPLINE'):
560 if hasattr(overscanImage, "getImage"):
561 biasArray = overscanImage.getImage().getArray()
562 biasArray = numpy.ma.masked_where(overscanImage.getMask().getArray() & statControl.getAndMask(),
563 biasArray)
564 else:
565 biasArray = overscanImage.getArray()
566 # Fit along the long axis, so collapse along each short row and fit the resulting array
567 shortInd = numpy.argmin(biasArray.shape)
568 if shortInd == 0:
569 # Convert to some 'standard' representation to make things easier
570 biasArray = numpy.transpose(biasArray)
572 # Do a single round of clipping to weed out CR hits and signal leaking into the overscan
573 percentiles = numpy.percentile(biasArray, [25.0, 50.0, 75.0], axis=1)
574 medianBiasArr = percentiles[1]
575 stdevBiasArr = 0.74*(percentiles[2] - percentiles[0]) # robust stdev
576 diff = numpy.abs(biasArray - medianBiasArr[:, numpy.newaxis])
577 biasMaskedArr = numpy.ma.masked_where(diff > numSigmaClip*stdevBiasArr[:, numpy.newaxis], biasArray)
578 collapsed = numpy.mean(biasMaskedArr, axis=1)
579 if collapsed.mask.sum() > 0:
580 collapsed.data[collapsed.mask] = numpy.mean(biasArray.data[collapsed.mask], axis=1)
582 del biasArray, percentiles, stdevBiasArr, diff, biasMaskedArr
584 if shortInd == 0:
585 collapsed = numpy.transpose(collapsed)
587 num = len(collapsed)
588 indices = 2.0*numpy.arange(num)/float(num) - 1.0
590 if fitType in ('POLY', 'CHEB', 'LEG'):
591 # A numpy polynomial
592 poly = numpy.polynomial
593 fitter, evaler = {"POLY": (poly.polynomial.polyfit, poly.polynomial.polyval),
594 "CHEB": (poly.chebyshev.chebfit, poly.chebyshev.chebval),
595 "LEG": (poly.legendre.legfit, poly.legendre.legval),
596 }[fitType]
598 coeffs = fitter(indices, collapsed, order)
599 fitBiasArr = evaler(indices, coeffs)
600 elif 'SPLINE' in fitType:
601 # An afw interpolation
602 numBins = order
603 #
604 # numpy.histogram needs a real array for the mask, but numpy.ma "optimises" the case
605 # no-values-are-masked by replacing the mask array by a scalar, numpy.ma.nomask
606 #
607 # Issue DM-415
608 #
609 collapsedMask = collapsed.mask
610 try:
611 if collapsedMask == numpy.ma.nomask:
612 collapsedMask = numpy.array(len(collapsed)*[numpy.ma.nomask])
613 except ValueError: # If collapsedMask is an array the test fails [needs .all()]
614 pass
616 numPerBin, binEdges = numpy.histogram(indices, bins=numBins,
617 weights=1-collapsedMask.astype(int))
618 # Binning is just a histogram, with weights equal to the values.
619 # Use a similar trick to get the bin centers (this deals with different numbers per bin).
620 with numpy.errstate(invalid="ignore"): # suppress NAN warnings
621 values = numpy.histogram(indices, bins=numBins,
622 weights=collapsed.data*~collapsedMask)[0]/numPerBin
623 binCenters = numpy.histogram(indices, bins=numBins,
624 weights=indices*~collapsedMask)[0]/numPerBin
625 interp = afwMath.makeInterpolate(binCenters.astype(float)[numPerBin > 0],
626 values.astype(float)[numPerBin > 0],
627 afwMath.stringToInterpStyle(fitType))
628 fitBiasArr = numpy.array([interp.interpolate(i) for i in indices])
630 import lsstDebug
631 if lsstDebug.Info(__name__).display:
632 import matplotlib.pyplot as plot
633 figure = plot.figure(1)
634 figure.clear()
635 axes = figure.add_axes((0.1, 0.1, 0.8, 0.8))
636 axes.plot(indices[~collapsedMask], collapsed[~collapsedMask], 'k+')
637 if collapsedMask.sum() > 0:
638 axes.plot(indices[collapsedMask], collapsed.data[collapsedMask], 'b+')
639 axes.plot(indices, fitBiasArr, 'r-')
640 plot.xlabel("centered/scaled position along overscan region")
641 plot.ylabel("pixel value/fit value")
642 figure.show()
643 prompt = "Press Enter or c to continue [chp]... "
644 while True:
645 ans = input(prompt).lower()
646 if ans in ("", "c",):
647 break
648 if ans in ("p",):
649 import pdb
650 pdb.set_trace()
651 elif ans in ("h", ):
652 print("h[elp] c[ontinue] p[db]")
653 plot.close()
655 offImage = ampImage.Factory(ampImage.getDimensions())
656 offArray = offImage.getArray()
657 overscanFit = afwImage.ImageF(overscanImage.getDimensions())
658 overscanArray = overscanFit.getArray()
659 if shortInd == 1:
660 offArray[:, :] = fitBiasArr[:, numpy.newaxis]
661 overscanArray[:, :] = fitBiasArr[:, numpy.newaxis]
662 else:
663 offArray[:, :] = fitBiasArr[numpy.newaxis, :]
664 overscanArray[:, :] = fitBiasArr[numpy.newaxis, :]
666 # We don't trust any extrapolation: mask those pixels as SUSPECT
667 # This will occur when the top and or bottom edges of the overscan
668 # contain saturated values. The values will be extrapolated from
669 # the surrounding pixels, but we cannot entirely trust the value of
670 # the extrapolation, and will mark the image mask plane to flag the
671 # image as such.
672 mask = ampMaskedImage.getMask()
673 maskArray = mask.getArray() if shortInd == 1 else mask.getArray().transpose()
674 suspect = mask.getPlaneBitMask("SUSPECT")
675 try:
676 if collapsed.mask == numpy.ma.nomask:
677 # There is no mask, so the whole array is fine
678 pass
679 except ValueError: # If collapsed.mask is an array the test fails [needs .all()]
680 for low in range(num):
681 if not collapsed.mask[low]:
682 break
683 if low > 0:
684 maskArray[:low, :] |= suspect
685 for high in range(1, num):
686 if not collapsed.mask[-high]:
687 break
688 if high > 1:
689 maskArray[-high:, :] |= suspect
691 else:
692 raise pexExcept.Exception('%s : %s an invalid overscan type' % ("overscanCorrection", fitType))
693 ampImage -= offImage
694 overscanImage -= overscanFit
695 return Struct(imageFit=offImage, overscanFit=overscanFit, overscanImage=overscanImage)
698def brighterFatterCorrection(exposure, kernel, maxIter, threshold, applyGain, gains=None):
699 """Apply brighter fatter correction in place for the image.
701 Parameters
702 ----------
703 exposure : `lsst.afw.image.Exposure`
704 Exposure to have brighter-fatter correction applied. Modified
705 by this method.
706 kernel : `numpy.ndarray`
707 Brighter-fatter kernel to apply.
708 maxIter : scalar
709 Number of correction iterations to run.
710 threshold : scalar
711 Convergence threshold in terms of the sum of absolute
712 deviations between an iteration and the previous one.
713 applyGain : `Bool`
714 If True, then the exposure values are scaled by the gain prior
715 to correction.
716 gains : `dict` [`str`, `float`]
717 A dictionary, keyed by amplifier name, of the gains to use.
718 If gains is None, the nominal gains in the amplifier object are used.
720 Returns
721 -------
722 diff : `float`
723 Final difference between iterations achieved in correction.
724 iteration : `int`
725 Number of iterations used to calculate correction.
727 Notes
728 -----
729 This correction takes a kernel that has been derived from flat
730 field images to redistribute the charge. The gradient of the
731 kernel is the deflection field due to the accumulated charge.
733 Given the original image I(x) and the kernel K(x) we can compute
734 the corrected image Ic(x) using the following equation:
736 Ic(x) = I(x) + 0.5*d/dx(I(x)*d/dx(int( dy*K(x-y)*I(y))))
738 To evaluate the derivative term we expand it as follows:
740 0.5 * ( d/dx(I(x))*d/dx(int(dy*K(x-y)*I(y))) + I(x)*d^2/dx^2(int(dy* K(x-y)*I(y))) )
742 Because we use the measured counts instead of the incident counts
743 we apply the correction iteratively to reconstruct the original
744 counts and the correction. We stop iterating when the summed
745 difference between the current corrected image and the one from
746 the previous iteration is below the threshold. We do not require
747 convergence because the number of iterations is too large a
748 computational cost. How we define the threshold still needs to be
749 evaluated, the current default was shown to work reasonably well
750 on a small set of images. For more information on the method see
751 DocuShare Document-19407.
753 The edges as defined by the kernel are not corrected because they
754 have spurious values due to the convolution.
755 """
756 image = exposure.getMaskedImage().getImage()
758 # The image needs to be units of electrons/holes
759 with gainContext(exposure, image, applyGain, gains):
761 kLx = numpy.shape(kernel)[0]
762 kLy = numpy.shape(kernel)[1]
763 kernelImage = afwImage.ImageD(kLx, kLy)
764 kernelImage.getArray()[:, :] = kernel
765 tempImage = image.clone()
767 nanIndex = numpy.isnan(tempImage.getArray())
768 tempImage.getArray()[nanIndex] = 0.
770 outImage = afwImage.ImageF(image.getDimensions())
771 corr = numpy.zeros_like(image.getArray())
772 prev_image = numpy.zeros_like(image.getArray())
773 convCntrl = afwMath.ConvolutionControl(False, True, 1)
774 fixedKernel = afwMath.FixedKernel(kernelImage)
776 # Define boundary by convolution region. The region that the correction will be
777 # calculated for is one fewer in each dimension because of the second derivative terms.
778 # NOTE: these need to use integer math, as we're using start:end as numpy index ranges.
779 startX = kLx//2
780 endX = -kLx//2
781 startY = kLy//2
782 endY = -kLy//2
784 for iteration in range(maxIter):
786 afwMath.convolve(outImage, tempImage, fixedKernel, convCntrl)
787 tmpArray = tempImage.getArray()
788 outArray = outImage.getArray()
790 with numpy.errstate(invalid="ignore", over="ignore"):
791 # First derivative term
792 gradTmp = numpy.gradient(tmpArray[startY:endY, startX:endX])
793 gradOut = numpy.gradient(outArray[startY:endY, startX:endX])
794 first = (gradTmp[0]*gradOut[0] + gradTmp[1]*gradOut[1])[1:-1, 1:-1]
796 # Second derivative term
797 diffOut20 = numpy.diff(outArray, 2, 0)[startY:endY, startX + 1:endX - 1]
798 diffOut21 = numpy.diff(outArray, 2, 1)[startY + 1:endY - 1, startX:endX]
799 second = tmpArray[startY + 1:endY - 1, startX + 1:endX - 1]*(diffOut20 + diffOut21)
801 corr[startY + 1:endY - 1, startX + 1:endX - 1] = 0.5*(first + second)
803 tmpArray[:, :] = image.getArray()[:, :]
804 tmpArray[nanIndex] = 0.
805 tmpArray[startY:endY, startX:endX] += corr[startY:endY, startX:endX]
807 if iteration > 0:
808 diff = numpy.sum(numpy.abs(prev_image - tmpArray))
810 if diff < threshold:
811 break
812 prev_image[:, :] = tmpArray[:, :]
814 image.getArray()[startY + 1:endY - 1, startX + 1:endX - 1] += \
815 corr[startY + 1:endY - 1, startX + 1:endX - 1]
817 return diff, iteration
820@contextmanager
821def gainContext(exp, image, apply, gains=None):
822 """Context manager that applies and removes gain.
824 Parameters
825 ----------
826 exp : `lsst.afw.image.Exposure`
827 Exposure to apply/remove gain.
828 image : `lsst.afw.image.Image`
829 Image to apply/remove gain.
830 apply : `Bool`
831 If True, apply and remove the amplifier gain.
832 gains : `dict` [`str`, `float`]
833 A dictionary, keyed by amplifier name, of the gains to use.
834 If gains is None, the nominal gains in the amplifier object are used.
836 Yields
837 ------
838 exp : `lsst.afw.image.Exposure`
839 Exposure with the gain applied.
840 """
841 # check we have all of them if provided because mixing and matching would
842 # be a real mess
843 if gains and apply is True:
844 ampNames = [amp.getName() for amp in exp.getDetector()]
845 for ampName in ampNames:
846 if ampName not in gains.keys():
847 raise RuntimeError(f"Gains provided to gain context, but no entry found for amp {ampName}")
849 if apply:
850 ccd = exp.getDetector()
851 for amp in ccd:
852 sim = image.Factory(image, amp.getBBox())
853 if gains:
854 gain = gains[amp.getName()]
855 else:
856 gain = amp.getGain()
857 sim *= gain
859 try:
860 yield exp
861 finally:
862 if apply:
863 ccd = exp.getDetector()
864 for amp in ccd:
865 sim = image.Factory(image, amp.getBBox())
866 if gains:
867 gain = gains[amp.getName()]
868 else:
869 gain = amp.getGain()
870 sim /= gain
873def attachTransmissionCurve(exposure, opticsTransmission=None, filterTransmission=None,
874 sensorTransmission=None, atmosphereTransmission=None):
875 """Attach a TransmissionCurve to an Exposure, given separate curves for
876 different components.
878 Parameters
879 ----------
880 exposure : `lsst.afw.image.Exposure`
881 Exposure object to modify by attaching the product of all given
882 ``TransmissionCurves`` in post-assembly trimmed detector coordinates.
883 Must have a valid ``Detector`` attached that matches the detector
884 associated with sensorTransmission.
885 opticsTransmission : `lsst.afw.image.TransmissionCurve`
886 A ``TransmissionCurve`` that represents the throughput of the optics,
887 to be evaluated in focal-plane coordinates.
888 filterTransmission : `lsst.afw.image.TransmissionCurve`
889 A ``TransmissionCurve`` that represents the throughput of the filter
890 itself, to be evaluated in focal-plane coordinates.
891 sensorTransmission : `lsst.afw.image.TransmissionCurve`
892 A ``TransmissionCurve`` that represents the throughput of the sensor
893 itself, to be evaluated in post-assembly trimmed detector coordinates.
894 atmosphereTransmission : `lsst.afw.image.TransmissionCurve`
895 A ``TransmissionCurve`` that represents the throughput of the
896 atmosphere, assumed to be spatially constant.
898 Returns
899 -------
900 combined : `lsst.afw.image.TransmissionCurve`
901 The TransmissionCurve attached to the exposure.
903 Notes
904 -----
905 All ``TransmissionCurve`` arguments are optional; if none are provided, the
906 attached ``TransmissionCurve`` will have unit transmission everywhere.
907 """
908 combined = afwImage.TransmissionCurve.makeIdentity()
909 if atmosphereTransmission is not None:
910 combined *= atmosphereTransmission
911 if opticsTransmission is not None:
912 combined *= opticsTransmission
913 if filterTransmission is not None:
914 combined *= filterTransmission
915 detector = exposure.getDetector()
916 fpToPix = detector.getTransform(fromSys=camGeom.FOCAL_PLANE,
917 toSys=camGeom.PIXELS)
918 combined = combined.transformedBy(fpToPix)
919 if sensorTransmission is not None:
920 combined *= sensorTransmission
921 exposure.getInfo().setTransmissionCurve(combined)
922 return combined
925@deprecated(reason="Camera geometry-based SkyWcs are now set when reading raws. To be removed after v19.",
926 category=FutureWarning)
927def addDistortionModel(exposure, camera):
928 """!Update the WCS in exposure with a distortion model based on camera
929 geometry.
931 Parameters
932 ----------
933 exposure : `lsst.afw.image.Exposure`
934 Exposure to process. Must contain a Detector and WCS. The
935 exposure is modified.
936 camera : `lsst.afw.cameraGeom.Camera`
937 Camera geometry.
939 Raises
940 ------
941 RuntimeError
942 Raised if ``exposure`` is lacking a Detector or WCS, or if
943 ``camera`` is None.
944 Notes
945 -----
946 Add a model for optical distortion based on geometry found in ``camera``
947 and the ``exposure``'s detector. The raw input exposure is assumed
948 have a TAN WCS that has no compensation for optical distortion.
949 Two other possibilities are:
950 - The raw input exposure already has a model for optical distortion,
951 as is the case for raw DECam data.
952 In that case you should set config.doAddDistortionModel False.
953 - The raw input exposure has a model for distortion, but it has known
954 deficiencies severe enough to be worth fixing (e.g. because they
955 cause problems for fitting a better WCS). In that case you should
956 override this method with a version suitable for your raw data.
958 """
959 wcs = exposure.getWcs()
960 if wcs is None:
961 raise RuntimeError("exposure has no WCS")
962 if camera is None:
963 raise RuntimeError("camera is None")
964 detector = exposure.getDetector()
965 if detector is None:
966 raise RuntimeError("exposure has no Detector")
967 pixelToFocalPlane = detector.getTransform(camGeom.PIXELS, camGeom.FOCAL_PLANE)
968 focalPlaneToFieldAngle = camera.getTransformMap().getTransform(camGeom.FOCAL_PLANE,
969 camGeom.FIELD_ANGLE)
970 distortedWcs = makeDistortedTanWcs(wcs, pixelToFocalPlane, focalPlaneToFieldAngle)
971 exposure.setWcs(distortedWcs)
974def applyGains(exposure, normalizeGains=False):
975 """Scale an exposure by the amplifier gains.
977 Parameters
978 ----------
979 exposure : `lsst.afw.image.Exposure`
980 Exposure to process. The image is modified.
981 normalizeGains : `Bool`, optional
982 If True, then amplifiers are scaled to force the median of
983 each amplifier to equal the median of those medians.
984 """
985 ccd = exposure.getDetector()
986 ccdImage = exposure.getMaskedImage()
988 medians = []
989 for amp in ccd:
990 sim = ccdImage.Factory(ccdImage, amp.getBBox())
991 sim *= amp.getGain()
993 if normalizeGains:
994 medians.append(numpy.median(sim.getImage().getArray()))
996 if normalizeGains:
997 median = numpy.median(numpy.array(medians))
998 for index, amp in enumerate(ccd):
999 sim = ccdImage.Factory(ccdImage, amp.getBBox())
1000 if medians[index] != 0.0:
1001 sim *= median/medians[index]
1004def widenSaturationTrails(mask):
1005 """Grow the saturation trails by an amount dependent on the width of the trail.
1007 Parameters
1008 ----------
1009 mask : `lsst.afw.image.Mask`
1010 Mask which will have the saturated areas grown.
1011 """
1013 extraGrowDict = {}
1014 for i in range(1, 6):
1015 extraGrowDict[i] = 0
1016 for i in range(6, 8):
1017 extraGrowDict[i] = 1
1018 for i in range(8, 10):
1019 extraGrowDict[i] = 3
1020 extraGrowMax = 4
1022 if extraGrowMax <= 0:
1023 return
1025 saturatedBit = mask.getPlaneBitMask("SAT")
1027 xmin, ymin = mask.getBBox().getMin()
1028 width = mask.getWidth()
1030 thresh = afwDetection.Threshold(saturatedBit, afwDetection.Threshold.BITMASK)
1031 fpList = afwDetection.FootprintSet(mask, thresh).getFootprints()
1033 for fp in fpList:
1034 for s in fp.getSpans():
1035 x0, x1 = s.getX0(), s.getX1()
1037 extraGrow = extraGrowDict.get(x1 - x0 + 1, extraGrowMax)
1038 if extraGrow > 0:
1039 y = s.getY() - ymin
1040 x0 -= xmin + extraGrow
1041 x1 -= xmin - extraGrow
1043 if x0 < 0:
1044 x0 = 0
1045 if x1 >= width - 1:
1046 x1 = width - 1
1048 mask.array[y, x0:x1+1] |= saturatedBit
1051def setBadRegions(exposure, badStatistic="MEDIAN"):
1052 """Set all BAD areas of the chip to the average of the rest of the exposure
1054 Parameters
1055 ----------
1056 exposure : `lsst.afw.image.Exposure`
1057 Exposure to mask. The exposure mask is modified.
1058 badStatistic : `str`, optional
1059 Statistic to use to generate the replacement value from the
1060 image data. Allowed values are 'MEDIAN' or 'MEANCLIP'.
1062 Returns
1063 -------
1064 badPixelCount : scalar
1065 Number of bad pixels masked.
1066 badPixelValue : scalar
1067 Value substituted for bad pixels.
1069 Raises
1070 ------
1071 RuntimeError
1072 Raised if `badStatistic` is not an allowed value.
1073 """
1074 if badStatistic == "MEDIAN":
1075 statistic = afwMath.MEDIAN
1076 elif badStatistic == "MEANCLIP":
1077 statistic = afwMath.MEANCLIP
1078 else:
1079 raise RuntimeError("Impossible method %s of bad region correction" % badStatistic)
1081 mi = exposure.getMaskedImage()
1082 mask = mi.getMask()
1083 BAD = mask.getPlaneBitMask("BAD")
1084 INTRP = mask.getPlaneBitMask("INTRP")
1086 sctrl = afwMath.StatisticsControl()
1087 sctrl.setAndMask(BAD)
1088 value = afwMath.makeStatistics(mi, statistic, sctrl).getValue()
1090 maskArray = mask.getArray()
1091 imageArray = mi.getImage().getArray()
1092 badPixels = numpy.logical_and((maskArray & BAD) > 0, (maskArray & INTRP) == 0)
1093 imageArray[:] = numpy.where(badPixels, value, imageArray)
1095 return badPixels.sum(), value