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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
25import lsst.geom
26import lsst.afw.image as afwImage
27import lsst.afw.detection as afwDetection
28import lsst.afw.math as afwMath
29import lsst.meas.algorithms as measAlg
30import lsst.afw.cameraGeom as camGeom
32from lsst.meas.algorithms.detection import SourceDetectionTask
34from contextlib import contextmanager
36from .overscan import OverscanCorrectionTask, OverscanCorrectionTaskConfig
37from .defects import Defects
40def createPsf(fwhm):
41 """Make a double Gaussian PSF.
43 Parameters
44 ----------
45 fwhm : scalar
46 FWHM of double Gaussian smoothing kernel.
48 Returns
49 -------
50 psf : `lsst.meas.algorithms.DoubleGaussianPsf`
51 The created smoothing kernel.
52 """
53 ksize = 4*int(fwhm) + 1
54 return measAlg.DoubleGaussianPsf(ksize, ksize, fwhm/(2*math.sqrt(2*math.log(2))))
57def transposeMaskedImage(maskedImage):
58 """Make a transposed copy of a masked image.
60 Parameters
61 ----------
62 maskedImage : `lsst.afw.image.MaskedImage`
63 Image to process.
65 Returns
66 -------
67 transposed : `lsst.afw.image.MaskedImage`
68 The transposed copy of the input image.
69 """
70 transposed = maskedImage.Factory(lsst.geom.Extent2I(maskedImage.getHeight(), maskedImage.getWidth()))
71 transposed.getImage().getArray()[:] = maskedImage.getImage().getArray().T
72 transposed.getMask().getArray()[:] = maskedImage.getMask().getArray().T
73 transposed.getVariance().getArray()[:] = maskedImage.getVariance().getArray().T
74 return transposed
77def interpolateDefectList(maskedImage, defectList, fwhm, fallbackValue=None):
78 """Interpolate over defects specified in a defect list.
80 Parameters
81 ----------
82 maskedImage : `lsst.afw.image.MaskedImage`
83 Image to process.
84 defectList : `lsst.meas.algorithms.Defects`
85 List of defects to interpolate over.
86 fwhm : scalar
87 FWHM of double Gaussian smoothing kernel.
88 fallbackValue : scalar, optional
89 Fallback value if an interpolated value cannot be determined.
90 If None, then the clipped mean of the image is used.
91 """
92 psf = createPsf(fwhm)
93 if fallbackValue is None:
94 fallbackValue = afwMath.makeStatistics(maskedImage.getImage(), afwMath.MEANCLIP).getValue()
95 if 'INTRP' not in maskedImage.getMask().getMaskPlaneDict():
96 maskedImage.getMask().addMaskPlane('INTRP')
97 measAlg.interpolateOverDefects(maskedImage, psf, defectList, fallbackValue, True)
98 return maskedImage
101def makeThresholdMask(maskedImage, threshold, growFootprints=1, maskName='SAT'):
102 """Mask pixels based on threshold detection.
104 Parameters
105 ----------
106 maskedImage : `lsst.afw.image.MaskedImage`
107 Image to process. Only the mask plane is updated.
108 threshold : scalar
109 Detection threshold.
110 growFootprints : scalar, optional
111 Number of pixels to grow footprints of detected regions.
112 maskName : str, optional
113 Mask plane name, or list of names to convert
115 Returns
116 -------
117 defectList : `lsst.meas.algorithms.Defects`
118 Defect list constructed from pixels above the threshold.
119 """
120 # find saturated regions
121 thresh = afwDetection.Threshold(threshold)
122 fs = afwDetection.FootprintSet(maskedImage, thresh)
124 if growFootprints > 0:
125 fs = afwDetection.FootprintSet(fs, rGrow=growFootprints, isotropic=False)
126 fpList = fs.getFootprints()
128 # set mask
129 mask = maskedImage.getMask()
130 bitmask = mask.getPlaneBitMask(maskName)
131 afwDetection.setMaskFromFootprintList(mask, fpList, bitmask)
133 return Defects.fromFootprintList(fpList)
136def growMasks(mask, radius=0, maskNameList=['BAD'], maskValue="BAD"):
137 """Grow a mask by an amount and add to the requested plane.
139 Parameters
140 ----------
141 mask : `lsst.afw.image.Mask`
142 Mask image to process.
143 radius : scalar
144 Amount to grow the mask.
145 maskNameList : `str` or `list` [`str`]
146 Mask names that should be grown.
147 maskValue : `str`
148 Mask plane to assign the newly masked pixels to.
149 """
150 if radius > 0:
151 thresh = afwDetection.Threshold(mask.getPlaneBitMask(maskNameList), afwDetection.Threshold.BITMASK)
152 fpSet = afwDetection.FootprintSet(mask, thresh)
153 fpSet = afwDetection.FootprintSet(fpSet, rGrow=radius, isotropic=False)
154 fpSet.setMask(mask, maskValue)
157def interpolateFromMask(maskedImage, fwhm, growSaturatedFootprints=1,
158 maskNameList=['SAT'], fallbackValue=None):
159 """Interpolate over defects identified by a particular set of mask planes.
161 Parameters
162 ----------
163 maskedImage : `lsst.afw.image.MaskedImage`
164 Image to process.
165 fwhm : scalar
166 FWHM of double Gaussian smoothing kernel.
167 growSaturatedFootprints : scalar, optional
168 Number of pixels to grow footprints for saturated pixels.
169 maskNameList : `List` of `str`, optional
170 Mask plane name.
171 fallbackValue : scalar, optional
172 Value of last resort for interpolation.
173 """
174 mask = maskedImage.getMask()
176 if growSaturatedFootprints > 0 and "SAT" in maskNameList:
177 # If we are interpolating over an area larger than the original masked region, we need
178 # to expand the original mask bit to the full area to explain why we interpolated there.
179 growMasks(mask, radius=growSaturatedFootprints, maskNameList=['SAT'], maskValue="SAT")
181 thresh = afwDetection.Threshold(mask.getPlaneBitMask(maskNameList), afwDetection.Threshold.BITMASK)
182 fpSet = afwDetection.FootprintSet(mask, thresh)
183 defectList = Defects.fromFootprintList(fpSet.getFootprints())
185 interpolateDefectList(maskedImage, defectList, fwhm, fallbackValue=fallbackValue)
187 return maskedImage
190def saturationCorrection(maskedImage, saturation, fwhm, growFootprints=1, interpolate=True, maskName='SAT',
191 fallbackValue=None):
192 """Mark saturated pixels and optionally interpolate over them
194 Parameters
195 ----------
196 maskedImage : `lsst.afw.image.MaskedImage`
197 Image to process.
198 saturation : scalar
199 Saturation level used as the detection threshold.
200 fwhm : scalar
201 FWHM of double Gaussian smoothing kernel.
202 growFootprints : scalar, optional
203 Number of pixels to grow footprints of detected regions.
204 interpolate : Bool, optional
205 If True, saturated pixels are interpolated over.
206 maskName : str, optional
207 Mask plane name.
208 fallbackValue : scalar, optional
209 Value of last resort for interpolation.
210 """
211 defectList = makeThresholdMask(
212 maskedImage=maskedImage,
213 threshold=saturation,
214 growFootprints=growFootprints,
215 maskName=maskName,
216 )
217 if interpolate:
218 interpolateDefectList(maskedImage, defectList, fwhm, fallbackValue=fallbackValue)
220 return maskedImage
223def trimToMatchCalibBBox(rawMaskedImage, calibMaskedImage):
224 """Compute number of edge trim pixels to match the calibration data.
226 Use the dimension difference between the raw exposure and the
227 calibration exposure to compute the edge trim pixels. This trim
228 is applied symmetrically, with the same number of pixels masked on
229 each side.
231 Parameters
232 ----------
233 rawMaskedImage : `lsst.afw.image.MaskedImage`
234 Image to trim.
235 calibMaskedImage : `lsst.afw.image.MaskedImage`
236 Calibration image to draw new bounding box from.
238 Returns
239 -------
240 replacementMaskedImage : `lsst.afw.image.MaskedImage`
241 ``rawMaskedImage`` trimmed to the appropriate size
242 Raises
243 ------
244 RuntimeError
245 Rasied if ``rawMaskedImage`` cannot be symmetrically trimmed to
246 match ``calibMaskedImage``.
247 """
248 nx, ny = rawMaskedImage.getBBox().getDimensions() - calibMaskedImage.getBBox().getDimensions()
249 if nx != ny:
250 raise RuntimeError("Raw and calib maskedImages are trimmed differently in X and Y.")
251 if nx % 2 != 0:
252 raise RuntimeError("Calibration maskedImage is trimmed unevenly in X.")
253 if nx < 0:
254 raise RuntimeError("Calibration maskedImage is larger than raw data.")
256 nEdge = nx//2
257 if nEdge > 0:
258 replacementMaskedImage = rawMaskedImage[nEdge:-nEdge, nEdge:-nEdge, afwImage.LOCAL]
259 SourceDetectionTask.setEdgeBits(
260 rawMaskedImage,
261 replacementMaskedImage.getBBox(),
262 rawMaskedImage.getMask().getPlaneBitMask("EDGE")
263 )
264 else:
265 replacementMaskedImage = rawMaskedImage
267 return replacementMaskedImage
270def biasCorrection(maskedImage, biasMaskedImage, trimToFit=False):
271 """Apply bias correction in place.
273 Parameters
274 ----------
275 maskedImage : `lsst.afw.image.MaskedImage`
276 Image to process. The image is modified by this method.
277 biasMaskedImage : `lsst.afw.image.MaskedImage`
278 Bias image of the same size as ``maskedImage``
279 trimToFit : `Bool`, optional
280 If True, raw data is symmetrically trimmed to match
281 calibration size.
283 Raises
284 ------
285 RuntimeError
286 Raised if ``maskedImage`` and ``biasMaskedImage`` do not have
287 the same size.
289 """
290 if trimToFit:
291 maskedImage = trimToMatchCalibBBox(maskedImage, biasMaskedImage)
293 if maskedImage.getBBox(afwImage.LOCAL) != biasMaskedImage.getBBox(afwImage.LOCAL):
294 raise RuntimeError("maskedImage bbox %s != biasMaskedImage bbox %s" %
295 (maskedImage.getBBox(afwImage.LOCAL), biasMaskedImage.getBBox(afwImage.LOCAL)))
296 maskedImage -= biasMaskedImage
299def darkCorrection(maskedImage, darkMaskedImage, expScale, darkScale, invert=False, trimToFit=False):
300 """Apply dark correction in place.
302 Parameters
303 ----------
304 maskedImage : `lsst.afw.image.MaskedImage`
305 Image to process. The image is modified by this method.
306 darkMaskedImage : `lsst.afw.image.MaskedImage`
307 Dark image of the same size as ``maskedImage``.
308 expScale : scalar
309 Dark exposure time for ``maskedImage``.
310 darkScale : scalar
311 Dark exposure time for ``darkMaskedImage``.
312 invert : `Bool`, optional
313 If True, re-add the dark to an already corrected image.
314 trimToFit : `Bool`, optional
315 If True, raw data is symmetrically trimmed to match
316 calibration size.
318 Raises
319 ------
320 RuntimeError
321 Raised if ``maskedImage`` and ``darkMaskedImage`` do not have
322 the same size.
324 Notes
325 -----
326 The dark correction is applied by calculating:
327 maskedImage -= dark * expScaling / darkScaling
328 """
329 if trimToFit:
330 maskedImage = trimToMatchCalibBBox(maskedImage, darkMaskedImage)
332 if maskedImage.getBBox(afwImage.LOCAL) != darkMaskedImage.getBBox(afwImage.LOCAL):
333 raise RuntimeError("maskedImage bbox %s != darkMaskedImage bbox %s" %
334 (maskedImage.getBBox(afwImage.LOCAL), darkMaskedImage.getBBox(afwImage.LOCAL)))
336 scale = expScale / darkScale
337 if not invert:
338 maskedImage.scaledMinus(scale, darkMaskedImage)
339 else:
340 maskedImage.scaledPlus(scale, darkMaskedImage)
343def updateVariance(maskedImage, gain, readNoise):
344 """Set the variance plane based on the image plane.
346 Parameters
347 ----------
348 maskedImage : `lsst.afw.image.MaskedImage`
349 Image to process. The variance plane is modified.
350 gain : scalar
351 The amplifier gain in electrons/ADU.
352 readNoise : scalar
353 The amplifier read nmoise in ADU/pixel.
354 """
355 var = maskedImage.getVariance()
356 var[:] = maskedImage.getImage()
357 var /= gain
358 var += readNoise**2
361def flatCorrection(maskedImage, flatMaskedImage, scalingType, userScale=1.0, invert=False, trimToFit=False):
362 """Apply flat correction in place.
364 Parameters
365 ----------
366 maskedImage : `lsst.afw.image.MaskedImage`
367 Image to process. The image is modified.
368 flatMaskedImage : `lsst.afw.image.MaskedImage`
369 Flat image of the same size as ``maskedImage``
370 scalingType : str
371 Flat scale computation method. Allowed values are 'MEAN',
372 'MEDIAN', or 'USER'.
373 userScale : scalar, optional
374 Scale to use if ``scalingType``='USER'.
375 invert : `Bool`, optional
376 If True, unflatten an already flattened image.
377 trimToFit : `Bool`, optional
378 If True, raw data is symmetrically trimmed to match
379 calibration size.
381 Raises
382 ------
383 RuntimeError
384 Raised if ``maskedImage`` and ``flatMaskedImage`` do not have
385 the same size or if ``scalingType`` is not an allowed value.
386 """
387 if trimToFit:
388 maskedImage = trimToMatchCalibBBox(maskedImage, flatMaskedImage)
390 if maskedImage.getBBox(afwImage.LOCAL) != flatMaskedImage.getBBox(afwImage.LOCAL):
391 raise RuntimeError("maskedImage bbox %s != flatMaskedImage bbox %s" %
392 (maskedImage.getBBox(afwImage.LOCAL), flatMaskedImage.getBBox(afwImage.LOCAL)))
394 # Figure out scale from the data
395 # Ideally the flats are normalized by the calibration product pipeline, but this allows some flexibility
396 # in the case that the flat is created by some other mechanism.
397 if scalingType in ('MEAN', 'MEDIAN'):
398 scalingType = afwMath.stringToStatisticsProperty(scalingType)
399 flatScale = afwMath.makeStatistics(flatMaskedImage.image, scalingType).getValue()
400 elif scalingType == 'USER':
401 flatScale = userScale
402 else:
403 raise RuntimeError('%s : %s not implemented' % ("flatCorrection", scalingType))
405 if not invert:
406 maskedImage.scaledDivides(1.0/flatScale, flatMaskedImage)
407 else:
408 maskedImage.scaledMultiplies(1.0/flatScale, flatMaskedImage)
411def illuminationCorrection(maskedImage, illumMaskedImage, illumScale, trimToFit=True):
412 """Apply illumination correction in place.
414 Parameters
415 ----------
416 maskedImage : `lsst.afw.image.MaskedImage`
417 Image to process. The image is modified.
418 illumMaskedImage : `lsst.afw.image.MaskedImage`
419 Illumination correction image of the same size as ``maskedImage``.
420 illumScale : scalar
421 Scale factor for the illumination correction.
422 trimToFit : `Bool`, optional
423 If True, raw data is symmetrically trimmed to match
424 calibration size.
426 Raises
427 ------
428 RuntimeError
429 Raised if ``maskedImage`` and ``illumMaskedImage`` do not have
430 the same size.
431 """
432 if trimToFit:
433 maskedImage = trimToMatchCalibBBox(maskedImage, illumMaskedImage)
435 if maskedImage.getBBox(afwImage.LOCAL) != illumMaskedImage.getBBox(afwImage.LOCAL):
436 raise RuntimeError("maskedImage bbox %s != illumMaskedImage bbox %s" %
437 (maskedImage.getBBox(afwImage.LOCAL), illumMaskedImage.getBBox(afwImage.LOCAL)))
439 maskedImage.scaledDivides(1.0/illumScale, illumMaskedImage)
442def overscanCorrection(ampMaskedImage, overscanImage, fitType='MEDIAN', order=1, collapseRej=3.0,
443 statControl=None, overscanIsInt=True):
444 """Apply overscan correction in place.
446 Parameters
447 ----------
448 ampMaskedImage : `lsst.afw.image.MaskedImage`
449 Image of amplifier to correct; modified.
450 overscanImage : `lsst.afw.image.Image` or `lsst.afw.image.MaskedImage`
451 Image of overscan; modified.
452 fitType : `str`
453 Type of fit for overscan correction. May be one of:
455 - ``MEAN``: use mean of overscan.
456 - ``MEANCLIP``: use clipped mean of overscan.
457 - ``MEDIAN``: use median of overscan.
458 - ``MEDIAN_PER_ROW``: use median per row of overscan.
459 - ``POLY``: fit with ordinary polynomial.
460 - ``CHEB``: fit with Chebyshev polynomial.
461 - ``LEG``: fit with Legendre polynomial.
462 - ``NATURAL_SPLINE``: fit with natural spline.
463 - ``CUBIC_SPLINE``: fit with cubic spline.
464 - ``AKIMA_SPLINE``: fit with Akima spline.
466 order : `int`
467 Polynomial order or number of spline knots; ignored unless
468 ``fitType`` indicates a polynomial or spline.
469 statControl : `lsst.afw.math.StatisticsControl`
470 Statistics control object. In particular, we pay attention to numSigmaClip
471 overscanIsInt : `bool`
472 Treat the overscan region as consisting of integers, even if it's been
473 converted to float. E.g. handle ties properly.
475 Returns
476 -------
477 result : `lsst.pipe.base.Struct`
478 Result struct with components:
480 - ``imageFit``: Value(s) removed from image (scalar or
481 `lsst.afw.image.Image`)
482 - ``overscanFit``: Value(s) removed from overscan (scalar or
483 `lsst.afw.image.Image`)
484 - ``overscanImage``: Overscan corrected overscan region
485 (`lsst.afw.image.Image`)
486 Raises
487 ------
488 RuntimeError
489 Raised if ``fitType`` is not an allowed value.
491 Notes
492 -----
493 The ``ampMaskedImage`` and ``overscanImage`` are modified, with the fit
494 subtracted. Note that the ``overscanImage`` should not be a subimage of
495 the ``ampMaskedImage``, to avoid being subtracted twice.
497 Debug plots are available for the SPLINE fitTypes by setting the
498 `debug.display` for `name` == "lsst.ip.isr.isrFunctions". These
499 plots show the scatter plot of the overscan data (collapsed along
500 the perpendicular dimension) as a function of position on the CCD
501 (normalized between +/-1).
502 """
503 ampImage = ampMaskedImage.getImage()
505 config = OverscanCorrectionTaskConfig()
506 if fitType:
507 config.fitType = fitType
508 if order:
509 config.order = order
510 if collapseRej:
511 config.numSigmaClip = collapseRej
512 if overscanIsInt:
513 config.overscanIsInt = True
515 overscanTask = OverscanCorrectionTask(config=config)
516 return overscanTask.run(ampImage, overscanImage)
519def brighterFatterCorrection(exposure, kernel, maxIter, threshold, applyGain, gains=None):
520 """Apply brighter fatter correction in place for the image.
522 Parameters
523 ----------
524 exposure : `lsst.afw.image.Exposure`
525 Exposure to have brighter-fatter correction applied. Modified
526 by this method.
527 kernel : `numpy.ndarray`
528 Brighter-fatter kernel to apply.
529 maxIter : scalar
530 Number of correction iterations to run.
531 threshold : scalar
532 Convergence threshold in terms of the sum of absolute
533 deviations between an iteration and the previous one.
534 applyGain : `Bool`
535 If True, then the exposure values are scaled by the gain prior
536 to correction.
537 gains : `dict` [`str`, `float`]
538 A dictionary, keyed by amplifier name, of the gains to use.
539 If gains is None, the nominal gains in the amplifier object are used.
541 Returns
542 -------
543 diff : `float`
544 Final difference between iterations achieved in correction.
545 iteration : `int`
546 Number of iterations used to calculate correction.
548 Notes
549 -----
550 This correction takes a kernel that has been derived from flat
551 field images to redistribute the charge. The gradient of the
552 kernel is the deflection field due to the accumulated charge.
554 Given the original image I(x) and the kernel K(x) we can compute
555 the corrected image Ic(x) using the following equation:
557 Ic(x) = I(x) + 0.5*d/dx(I(x)*d/dx(int( dy*K(x-y)*I(y))))
559 To evaluate the derivative term we expand it as follows:
561 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))) )
563 Because we use the measured counts instead of the incident counts
564 we apply the correction iteratively to reconstruct the original
565 counts and the correction. We stop iterating when the summed
566 difference between the current corrected image and the one from
567 the previous iteration is below the threshold. We do not require
568 convergence because the number of iterations is too large a
569 computational cost. How we define the threshold still needs to be
570 evaluated, the current default was shown to work reasonably well
571 on a small set of images. For more information on the method see
572 DocuShare Document-19407.
574 The edges as defined by the kernel are not corrected because they
575 have spurious values due to the convolution.
576 """
577 image = exposure.getMaskedImage().getImage()
579 # The image needs to be units of electrons/holes
580 with gainContext(exposure, image, applyGain, gains):
582 kLx = numpy.shape(kernel)[0]
583 kLy = numpy.shape(kernel)[1]
584 kernelImage = afwImage.ImageD(kLx, kLy)
585 kernelImage.getArray()[:, :] = kernel
586 tempImage = image.clone()
588 nanIndex = numpy.isnan(tempImage.getArray())
589 tempImage.getArray()[nanIndex] = 0.
591 outImage = afwImage.ImageF(image.getDimensions())
592 corr = numpy.zeros_like(image.getArray())
593 prev_image = numpy.zeros_like(image.getArray())
594 convCntrl = afwMath.ConvolutionControl(False, True, 1)
595 fixedKernel = afwMath.FixedKernel(kernelImage)
597 # Define boundary by convolution region. The region that the correction will be
598 # calculated for is one fewer in each dimension because of the second derivative terms.
599 # NOTE: these need to use integer math, as we're using start:end as numpy index ranges.
600 startX = kLx//2
601 endX = -kLx//2
602 startY = kLy//2
603 endY = -kLy//2
605 for iteration in range(maxIter):
607 afwMath.convolve(outImage, tempImage, fixedKernel, convCntrl)
608 tmpArray = tempImage.getArray()
609 outArray = outImage.getArray()
611 with numpy.errstate(invalid="ignore", over="ignore"):
612 # First derivative term
613 gradTmp = numpy.gradient(tmpArray[startY:endY, startX:endX])
614 gradOut = numpy.gradient(outArray[startY:endY, startX:endX])
615 first = (gradTmp[0]*gradOut[0] + gradTmp[1]*gradOut[1])[1:-1, 1:-1]
617 # Second derivative term
618 diffOut20 = numpy.diff(outArray, 2, 0)[startY:endY, startX + 1:endX - 1]
619 diffOut21 = numpy.diff(outArray, 2, 1)[startY + 1:endY - 1, startX:endX]
620 second = tmpArray[startY + 1:endY - 1, startX + 1:endX - 1]*(diffOut20 + diffOut21)
622 corr[startY + 1:endY - 1, startX + 1:endX - 1] = 0.5*(first + second)
624 tmpArray[:, :] = image.getArray()[:, :]
625 tmpArray[nanIndex] = 0.
626 tmpArray[startY:endY, startX:endX] += corr[startY:endY, startX:endX]
628 if iteration > 0:
629 diff = numpy.sum(numpy.abs(prev_image - tmpArray))
631 if diff < threshold:
632 break
633 prev_image[:, :] = tmpArray[:, :]
635 image.getArray()[startY + 1:endY - 1, startX + 1:endX - 1] += \
636 corr[startY + 1:endY - 1, startX + 1:endX - 1]
638 return diff, iteration
641@contextmanager
642def gainContext(exp, image, apply, gains=None):
643 """Context manager that applies and removes gain.
645 Parameters
646 ----------
647 exp : `lsst.afw.image.Exposure`
648 Exposure to apply/remove gain.
649 image : `lsst.afw.image.Image`
650 Image to apply/remove gain.
651 apply : `Bool`
652 If True, apply and remove the amplifier gain.
653 gains : `dict` [`str`, `float`]
654 A dictionary, keyed by amplifier name, of the gains to use.
655 If gains is None, the nominal gains in the amplifier object are used.
657 Yields
658 ------
659 exp : `lsst.afw.image.Exposure`
660 Exposure with the gain applied.
661 """
662 # check we have all of them if provided because mixing and matching would
663 # be a real mess
664 if gains and apply is True:
665 ampNames = [amp.getName() for amp in exp.getDetector()]
666 for ampName in ampNames:
667 if ampName not in gains.keys():
668 raise RuntimeError(f"Gains provided to gain context, but no entry found for amp {ampName}")
670 if apply:
671 ccd = exp.getDetector()
672 for amp in ccd:
673 sim = image.Factory(image, amp.getBBox())
674 if gains:
675 gain = gains[amp.getName()]
676 else:
677 gain = amp.getGain()
678 sim *= gain
680 try:
681 yield exp
682 finally:
683 if apply:
684 ccd = exp.getDetector()
685 for amp in ccd:
686 sim = image.Factory(image, amp.getBBox())
687 if gains:
688 gain = gains[amp.getName()]
689 else:
690 gain = amp.getGain()
691 sim /= gain
694def attachTransmissionCurve(exposure, opticsTransmission=None, filterTransmission=None,
695 sensorTransmission=None, atmosphereTransmission=None):
696 """Attach a TransmissionCurve to an Exposure, given separate curves for
697 different components.
699 Parameters
700 ----------
701 exposure : `lsst.afw.image.Exposure`
702 Exposure object to modify by attaching the product of all given
703 ``TransmissionCurves`` in post-assembly trimmed detector coordinates.
704 Must have a valid ``Detector`` attached that matches the detector
705 associated with sensorTransmission.
706 opticsTransmission : `lsst.afw.image.TransmissionCurve`
707 A ``TransmissionCurve`` that represents the throughput of the optics,
708 to be evaluated in focal-plane coordinates.
709 filterTransmission : `lsst.afw.image.TransmissionCurve`
710 A ``TransmissionCurve`` that represents the throughput of the filter
711 itself, to be evaluated in focal-plane coordinates.
712 sensorTransmission : `lsst.afw.image.TransmissionCurve`
713 A ``TransmissionCurve`` that represents the throughput of the sensor
714 itself, to be evaluated in post-assembly trimmed detector coordinates.
715 atmosphereTransmission : `lsst.afw.image.TransmissionCurve`
716 A ``TransmissionCurve`` that represents the throughput of the
717 atmosphere, assumed to be spatially constant.
719 Returns
720 -------
721 combined : `lsst.afw.image.TransmissionCurve`
722 The TransmissionCurve attached to the exposure.
724 Notes
725 -----
726 All ``TransmissionCurve`` arguments are optional; if none are provided, the
727 attached ``TransmissionCurve`` will have unit transmission everywhere.
728 """
729 combined = afwImage.TransmissionCurve.makeIdentity()
730 if atmosphereTransmission is not None:
731 combined *= atmosphereTransmission
732 if opticsTransmission is not None:
733 combined *= opticsTransmission
734 if filterTransmission is not None:
735 combined *= filterTransmission
736 detector = exposure.getDetector()
737 fpToPix = detector.getTransform(fromSys=camGeom.FOCAL_PLANE,
738 toSys=camGeom.PIXELS)
739 combined = combined.transformedBy(fpToPix)
740 if sensorTransmission is not None:
741 combined *= sensorTransmission
742 exposure.getInfo().setTransmissionCurve(combined)
743 return combined
746def applyGains(exposure, normalizeGains=False, ptcGains=None):
747 """Scale an exposure by the amplifier gains.
749 Parameters
750 ----------
751 exposure : `lsst.afw.image.Exposure`
752 Exposure to process. The image is modified.
753 normalizeGains : `Bool`, optional
754 If True, then amplifiers are scaled to force the median of
755 each amplifier to equal the median of those medians.
756 ptcGains : `dict`[`str`], optional
757 Dictionary keyed by amp name containing the PTC gains.
758 """
759 ccd = exposure.getDetector()
760 ccdImage = exposure.getMaskedImage()
762 medians = []
763 for amp in ccd:
764 sim = ccdImage.Factory(ccdImage, amp.getBBox())
765 if ptcGains:
766 sim *= ptcGains[amp.getName()]
767 else:
768 sim *= amp.getGain()
770 if normalizeGains:
771 medians.append(numpy.median(sim.getImage().getArray()))
773 if normalizeGains:
774 median = numpy.median(numpy.array(medians))
775 for index, amp in enumerate(ccd):
776 sim = ccdImage.Factory(ccdImage, amp.getBBox())
777 if medians[index] != 0.0:
778 sim *= median/medians[index]
781def widenSaturationTrails(mask):
782 """Grow the saturation trails by an amount dependent on the width of the trail.
784 Parameters
785 ----------
786 mask : `lsst.afw.image.Mask`
787 Mask which will have the saturated areas grown.
788 """
790 extraGrowDict = {}
791 for i in range(1, 6):
792 extraGrowDict[i] = 0
793 for i in range(6, 8):
794 extraGrowDict[i] = 1
795 for i in range(8, 10):
796 extraGrowDict[i] = 3
797 extraGrowMax = 4
799 if extraGrowMax <= 0:
800 return
802 saturatedBit = mask.getPlaneBitMask("SAT")
804 xmin, ymin = mask.getBBox().getMin()
805 width = mask.getWidth()
807 thresh = afwDetection.Threshold(saturatedBit, afwDetection.Threshold.BITMASK)
808 fpList = afwDetection.FootprintSet(mask, thresh).getFootprints()
810 for fp in fpList:
811 for s in fp.getSpans():
812 x0, x1 = s.getX0(), s.getX1()
814 extraGrow = extraGrowDict.get(x1 - x0 + 1, extraGrowMax)
815 if extraGrow > 0:
816 y = s.getY() - ymin
817 x0 -= xmin + extraGrow
818 x1 -= xmin - extraGrow
820 if x0 < 0:
821 x0 = 0
822 if x1 >= width - 1:
823 x1 = width - 1
825 mask.array[y, x0:x1+1] |= saturatedBit
828def setBadRegions(exposure, badStatistic="MEDIAN"):
829 """Set all BAD areas of the chip to the average of the rest of the exposure
831 Parameters
832 ----------
833 exposure : `lsst.afw.image.Exposure`
834 Exposure to mask. The exposure mask is modified.
835 badStatistic : `str`, optional
836 Statistic to use to generate the replacement value from the
837 image data. Allowed values are 'MEDIAN' or 'MEANCLIP'.
839 Returns
840 -------
841 badPixelCount : scalar
842 Number of bad pixels masked.
843 badPixelValue : scalar
844 Value substituted for bad pixels.
846 Raises
847 ------
848 RuntimeError
849 Raised if `badStatistic` is not an allowed value.
850 """
851 if badStatistic == "MEDIAN":
852 statistic = afwMath.MEDIAN
853 elif badStatistic == "MEANCLIP":
854 statistic = afwMath.MEANCLIP
855 else:
856 raise RuntimeError("Impossible method %s of bad region correction" % badStatistic)
858 mi = exposure.getMaskedImage()
859 mask = mi.getMask()
860 BAD = mask.getPlaneBitMask("BAD")
861 INTRP = mask.getPlaneBitMask("INTRP")
863 sctrl = afwMath.StatisticsControl()
864 sctrl.setAndMask(BAD)
865 value = afwMath.makeStatistics(mi, statistic, sctrl).getValue()
867 maskArray = mask.getArray()
868 imageArray = mi.getImage().getArray()
869 badPixels = numpy.logical_and((maskArray & BAD) > 0, (maskArray & INTRP) == 0)
870 imageArray[:] = numpy.where(badPixels, value, imageArray)
872 return badPixels.sum(), value
875def checkFilter(exposure, filterList, log):
876 """Check to see if an exposure is in a filter specified by a list.
878 The goal of this is to provide a unified filter checking interface
879 for all filter dependent stages.
881 Parameters
882 ----------
883 exposure : `lsst.afw.image.Exposure`
884 Exposure to examine.
885 filterList : `list` [`str`]
886 List of physical_filter names to check.
887 log : `lsst.log.Log`
888 Logger to handle messages.
890 Returns
891 -------
892 result : `bool`
893 True if the exposure's filter is contained in the list.
894 """
895 thisFilter = exposure.getFilterLabel()
896 if thisFilter is None:
897 log.warning("No FilterLabel attached to this exposure!")
898 return False
900 thisPhysicalFilter = getPhysicalFilter(thisFilter, log)
901 if thisPhysicalFilter in filterList:
902 return True
903 elif thisFilter.bandLabel in filterList:
904 if log:
905 log.warning("Physical filter (%s) should be used instead of band %s for filter configurations"
906 " (%s)", thisPhysicalFilter, thisFilter.bandLabel, filterList)
907 return True
908 else:
909 return False
912def getPhysicalFilter(filterLabel, log):
913 """Get the physical filter label associated with the given filterLabel.
915 If ``filterLabel`` is `None` or there is no physicalLabel attribute
916 associated with the given ``filterLabel``, the returned label will be
917 "Unknown".
919 Parameters
920 ----------
921 filterLabel : `lsst.afw.image.FilterLabel`
922 The `lsst.afw.image.FilterLabel` object from which to derive the
923 physical filter label.
924 log : `lsst.log.Log`
925 Logger to handle messages.
927 Returns
928 -------
929 physicalFilter : `str`
930 The value returned by the physicalLabel attribute of ``filterLabel`` if
931 it exists, otherwise set to \"Unknown\".
932 """
933 if filterLabel is None:
934 physicalFilter = "Unknown"
935 log.warning("filterLabel is None. Setting physicalFilter to \"Unknown\".")
936 else:
937 try:
938 physicalFilter = filterLabel.physicalLabel
939 except RuntimeError:
940 log.warning("filterLabel has no physicalLabel attribute. Setting physicalFilter to \"Unknown\".")
941 physicalFilter = "Unknown"
942 return physicalFilter