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isrFunctions.py
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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
24
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
31
32from lsst.meas.algorithms.detection import SourceDetectionTask
33
34from contextlib import contextmanager
35
36from .defects import Defects
37
38
39def createPsf(fwhm):
40 """Make a double Gaussian PSF.
41
42 Parameters
43 ----------
44 fwhm : scalar
45 FWHM of double Gaussian smoothing kernel.
46
47 Returns
48 -------
50 The created smoothing kernel.
51 """
52 ksize = 4*int(fwhm) + 1
53 return measAlg.DoubleGaussianPsf(ksize, ksize, fwhm/(2*math.sqrt(2*math.log(2))))
54
55
56def transposeMaskedImage(maskedImage):
57 """Make a transposed copy of a masked image.
58
59 Parameters
60 ----------
61 maskedImage : `lsst.afw.image.MaskedImage`
62 Image to process.
63
64 Returns
65 -------
66 transposed : `lsst.afw.image.MaskedImage`
67 The transposed copy of the input image.
68 """
69 transposed = maskedImage.Factory(lsst.geom.Extent2I(maskedImage.getHeight(), maskedImage.getWidth()))
70 transposed.getImage().getArray()[:] = maskedImage.getImage().getArray().T
71 transposed.getMask().getArray()[:] = maskedImage.getMask().getArray().T
72 transposed.getVariance().getArray()[:] = maskedImage.getVariance().getArray().T
73 return transposed
74
75
76def interpolateDefectList(maskedImage, defectList, fwhm, fallbackValue=None):
77 """Interpolate over defects specified in a defect list.
78
79 Parameters
80 ----------
81 maskedImage : `lsst.afw.image.MaskedImage`
82 Image to process.
83 defectList : `lsst.meas.algorithms.Defects`
84 List of defects to interpolate over.
85 fwhm : scalar
86 FWHM of double Gaussian smoothing kernel.
87 fallbackValue : scalar, optional
88 Fallback value if an interpolated value cannot be determined.
89 If None, then the clipped mean of the image is used.
90 """
91 psf = createPsf(fwhm)
92 if fallbackValue is None:
93 fallbackValue = afwMath.makeStatistics(maskedImage.getImage(), afwMath.MEANCLIP).getValue()
94 if 'INTRP' not in maskedImage.getMask().getMaskPlaneDict():
95 maskedImage.getMask().addMaskPlane('INTRP')
96 measAlg.interpolateOverDefects(maskedImage, psf, defectList, fallbackValue, True)
97 return maskedImage
98
99
100def makeThresholdMask(maskedImage, threshold, growFootprints=1, maskName='SAT'):
101 """Mask pixels based on threshold detection.
102
103 Parameters
104 ----------
105 maskedImage : `lsst.afw.image.MaskedImage`
106 Image to process. Only the mask plane is updated.
107 threshold : scalar
108 Detection threshold.
109 growFootprints : scalar, optional
110 Number of pixels to grow footprints of detected regions.
111 maskName : str, optional
112 Mask plane name, or list of names to convert
113
114 Returns
115 -------
116 defectList : `lsst.meas.algorithms.Defects`
117 Defect list constructed from pixels above the threshold.
118 """
119 # find saturated regions
120 thresh = afwDetection.Threshold(threshold)
121 fs = afwDetection.FootprintSet(maskedImage, thresh)
122
123 if growFootprints > 0:
124 fs = afwDetection.FootprintSet(fs, rGrow=growFootprints, isotropic=False)
125 fpList = fs.getFootprints()
126
127 # set mask
128 mask = maskedImage.getMask()
129 bitmask = mask.getPlaneBitMask(maskName)
130 afwDetection.setMaskFromFootprintList(mask, fpList, bitmask)
131
132 return Defects.fromFootprintList(fpList)
133
134
135def growMasks(mask, radius=0, maskNameList=['BAD'], maskValue="BAD"):
136 """Grow a mask by an amount and add to the requested plane.
137
138 Parameters
139 ----------
140 mask : `lsst.afw.image.Mask`
141 Mask image to process.
142 radius : scalar
143 Amount to grow the mask.
144 maskNameList : `str` or `list` [`str`]
145 Mask names that should be grown.
146 maskValue : `str`
147 Mask plane to assign the newly masked pixels to.
148 """
149 if radius > 0:
150 thresh = afwDetection.Threshold(mask.getPlaneBitMask(maskNameList), afwDetection.Threshold.BITMASK)
151 fpSet = afwDetection.FootprintSet(mask, thresh)
152 fpSet = afwDetection.FootprintSet(fpSet, rGrow=radius, isotropic=False)
153 fpSet.setMask(mask, maskValue)
154
155
156def interpolateFromMask(maskedImage, fwhm, growSaturatedFootprints=1,
157 maskNameList=['SAT'], fallbackValue=None):
158 """Interpolate over defects identified by a particular set of mask planes.
159
160 Parameters
161 ----------
162 maskedImage : `lsst.afw.image.MaskedImage`
163 Image to process.
164 fwhm : scalar
165 FWHM of double Gaussian smoothing kernel.
166 growSaturatedFootprints : scalar, optional
167 Number of pixels to grow footprints for saturated pixels.
168 maskNameList : `List` of `str`, optional
169 Mask plane name.
170 fallbackValue : scalar, optional
171 Value of last resort for interpolation.
172 """
173 mask = maskedImage.getMask()
174
175 if growSaturatedFootprints > 0 and "SAT" in maskNameList:
176 # If we are interpolating over an area larger than the original masked
177 # region, we need to expand the original mask bit to the full area to
178 # explain why we interpolated there.
179 growMasks(mask, radius=growSaturatedFootprints, maskNameList=['SAT'], maskValue="SAT")
180
181 thresh = afwDetection.Threshold(mask.getPlaneBitMask(maskNameList), afwDetection.Threshold.BITMASK)
182 fpSet = afwDetection.FootprintSet(mask, thresh)
183 defectList = Defects.fromFootprintList(fpSet.getFootprints())
184
185 interpolateDefectList(maskedImage, defectList, fwhm, fallbackValue=fallbackValue)
186
187 return maskedImage
188
189
190def saturationCorrection(maskedImage, saturation, fwhm, growFootprints=1, interpolate=True, maskName='SAT',
191 fallbackValue=None):
192 """Mark saturated pixels and optionally interpolate over them
193
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)
219
220 return maskedImage
221
222
223def trimToMatchCalibBBox(rawMaskedImage, calibMaskedImage):
224 """Compute number of edge trim pixels to match the calibration data.
225
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.
230
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.
237
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.")
255
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
266
267 return replacementMaskedImage
268
269
270def biasCorrection(maskedImage, biasMaskedImage, trimToFit=False):
271 """Apply bias correction in place.
272
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.
282
283 Raises
284 ------
285 RuntimeError
286 Raised if ``maskedImage`` and ``biasMaskedImage`` do not have
287 the same size.
288
289 """
290 if trimToFit:
291 maskedImage = trimToMatchCalibBBox(maskedImage, biasMaskedImage)
292
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
297
298
299def darkCorrection(maskedImage, darkMaskedImage, expScale, darkScale, invert=False, trimToFit=False):
300 """Apply dark correction in place.
301
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.
317
318 Raises
319 ------
320 RuntimeError
321 Raised if ``maskedImage`` and ``darkMaskedImage`` do not have
322 the same size.
323
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)
331
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)))
335
336 scale = expScale / darkScale
337 if not invert:
338 maskedImage.scaledMinus(scale, darkMaskedImage)
339 else:
340 maskedImage.scaledPlus(scale, darkMaskedImage)
341
342
343def updateVariance(maskedImage, gain, readNoise):
344 """Set the variance plane based on the image plane.
345
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
359
360
361def flatCorrection(maskedImage, flatMaskedImage, scalingType, userScale=1.0, invert=False, trimToFit=False):
362 """Apply flat correction in place.
363
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.
380
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)
389
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)))
393
394 # Figure out scale from the data
395 # Ideally the flats are normalized by the calibration product pipeline,
396 # but this allows some flexibility in the case that the flat is created by
397 # 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))
405
406 if not invert:
407 maskedImage.scaledDivides(1.0/flatScale, flatMaskedImage)
408 else:
409 maskedImage.scaledMultiplies(1.0/flatScale, flatMaskedImage)
410
411
412def illuminationCorrection(maskedImage, illumMaskedImage, illumScale, trimToFit=True):
413 """Apply illumination correction in place.
414
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.
426
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)
435
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)))
439
440 maskedImage.scaledDivides(1.0/illumScale, illumMaskedImage)
441
442
443def brighterFatterCorrection(exposure, kernel, maxIter, threshold, applyGain, gains=None):
444 """Apply brighter fatter correction in place for the image.
445
446 Parameters
447 ----------
448 exposure : `lsst.afw.image.Exposure`
449 Exposure to have brighter-fatter correction applied. Modified
450 by this method.
451 kernel : `numpy.ndarray`
452 Brighter-fatter kernel to apply.
453 maxIter : scalar
454 Number of correction iterations to run.
455 threshold : scalar
456 Convergence threshold in terms of the sum of absolute
457 deviations between an iteration and the previous one.
458 applyGain : `Bool`
459 If True, then the exposure values are scaled by the gain prior
460 to correction.
461 gains : `dict` [`str`, `float`]
462 A dictionary, keyed by amplifier name, of the gains to use.
463 If gains is None, the nominal gains in the amplifier object are used.
464
465 Returns
466 -------
467 diff : `float`
468 Final difference between iterations achieved in correction.
469 iteration : `int`
470 Number of iterations used to calculate correction.
471
472 Notes
473 -----
474 This correction takes a kernel that has been derived from flat
475 field images to redistribute the charge. The gradient of the
476 kernel is the deflection field due to the accumulated charge.
477
478 Given the original image I(x) and the kernel K(x) we can compute
479 the corrected image Ic(x) using the following equation:
480
481 Ic(x) = I(x) + 0.5*d/dx(I(x)*d/dx(int( dy*K(x-y)*I(y))))
482
483 To evaluate the derivative term we expand it as follows:
484
485 0.5 * ( d/dx(I(x))*d/dx(int(dy*K(x-y)*I(y)))
486 + I(x)*d^2/dx^2(int(dy* K(x-y)*I(y))) )
487
488 Because we use the measured counts instead of the incident counts
489 we apply the correction iteratively to reconstruct the original
490 counts and the correction. We stop iterating when the summed
491 difference between the current corrected image and the one from
492 the previous iteration is below the threshold. We do not require
493 convergence because the number of iterations is too large a
494 computational cost. How we define the threshold still needs to be
495 evaluated, the current default was shown to work reasonably well
496 on a small set of images. For more information on the method see
497 DocuShare Document-19407.
498
499 The edges as defined by the kernel are not corrected because they
500 have spurious values due to the convolution.
501 """
502 image = exposure.getMaskedImage().getImage()
503
504 # The image needs to be units of electrons/holes
505 with gainContext(exposure, image, applyGain, gains):
506
507 kLx = numpy.shape(kernel)[0]
508 kLy = numpy.shape(kernel)[1]
509 kernelImage = afwImage.ImageD(kLx, kLy)
510 kernelImage.getArray()[:, :] = kernel
511 tempImage = image.clone()
512
513 nanIndex = numpy.isnan(tempImage.getArray())
514 tempImage.getArray()[nanIndex] = 0.
515
516 outImage = afwImage.ImageF(image.getDimensions())
517 corr = numpy.zeros_like(image.getArray())
518 prev_image = numpy.zeros_like(image.getArray())
519 convCntrl = afwMath.ConvolutionControl(False, True, 1)
520 fixedKernel = afwMath.FixedKernel(kernelImage)
521
522 # Define boundary by convolution region. The region that the
523 # correction will be calculated for is one fewer in each dimension
524 # because of the second derivative terms.
525 # NOTE: these need to use integer math, as we're using start:end as
526 # numpy index ranges.
527 startX = kLx//2
528 endX = -kLx//2
529 startY = kLy//2
530 endY = -kLy//2
531
532 for iteration in range(maxIter):
533
534 afwMath.convolve(outImage, tempImage, fixedKernel, convCntrl)
535 tmpArray = tempImage.getArray()
536 outArray = outImage.getArray()
537
538 with numpy.errstate(invalid="ignore", over="ignore"):
539 # First derivative term
540 gradTmp = numpy.gradient(tmpArray[startY:endY, startX:endX])
541 gradOut = numpy.gradient(outArray[startY:endY, startX:endX])
542 first = (gradTmp[0]*gradOut[0] + gradTmp[1]*gradOut[1])[1:-1, 1:-1]
543
544 # Second derivative term
545 diffOut20 = numpy.diff(outArray, 2, 0)[startY:endY, startX + 1:endX - 1]
546 diffOut21 = numpy.diff(outArray, 2, 1)[startY + 1:endY - 1, startX:endX]
547 second = tmpArray[startY + 1:endY - 1, startX + 1:endX - 1]*(diffOut20 + diffOut21)
548
549 corr[startY + 1:endY - 1, startX + 1:endX - 1] = 0.5*(first + second)
550
551 tmpArray[:, :] = image.getArray()[:, :]
552 tmpArray[nanIndex] = 0.
553 tmpArray[startY:endY, startX:endX] += corr[startY:endY, startX:endX]
554
555 if iteration > 0:
556 diff = numpy.sum(numpy.abs(prev_image - tmpArray))
557
558 if diff < threshold:
559 break
560 prev_image[:, :] = tmpArray[:, :]
561
562 image.getArray()[startY + 1:endY - 1, startX + 1:endX - 1] += \
563 corr[startY + 1:endY - 1, startX + 1:endX - 1]
564
565 return diff, iteration
566
567
568@contextmanager
569def gainContext(exp, image, apply, gains=None):
570 """Context manager that applies and removes gain.
571
572 Parameters
573 ----------
575 Exposure to apply/remove gain.
576 image : `lsst.afw.image.Image`
577 Image to apply/remove gain.
578 apply : `Bool`
579 If True, apply and remove the amplifier gain.
580 gains : `dict` [`str`, `float`]
581 A dictionary, keyed by amplifier name, of the gains to use.
582 If gains is None, the nominal gains in the amplifier object are used.
583
584 Yields
585 ------
587 Exposure with the gain applied.
588 """
589 # check we have all of them if provided because mixing and matching would
590 # be a real mess
591 if gains and apply is True:
592 ampNames = [amp.getName() for amp in exp.getDetector()]
593 for ampName in ampNames:
594 if ampName not in gains.keys():
595 raise RuntimeError(f"Gains provided to gain context, but no entry found for amp {ampName}")
596
597 if apply:
598 ccd = exp.getDetector()
599 for amp in ccd:
600 sim = image.Factory(image, amp.getBBox())
601 if gains:
602 gain = gains[amp.getName()]
603 else:
604 gain = amp.getGain()
605 sim *= gain
606
607 try:
608 yield exp
609 finally:
610 if apply:
611 ccd = exp.getDetector()
612 for amp in ccd:
613 sim = image.Factory(image, amp.getBBox())
614 if gains:
615 gain = gains[amp.getName()]
616 else:
617 gain = amp.getGain()
618 sim /= gain
619
620
621def attachTransmissionCurve(exposure, opticsTransmission=None, filterTransmission=None,
622 sensorTransmission=None, atmosphereTransmission=None):
623 """Attach a TransmissionCurve to an Exposure, given separate curves for
624 different components.
625
626 Parameters
627 ----------
628 exposure : `lsst.afw.image.Exposure`
629 Exposure object to modify by attaching the product of all given
630 ``TransmissionCurves`` in post-assembly trimmed detector coordinates.
631 Must have a valid ``Detector`` attached that matches the detector
632 associated with sensorTransmission.
633 opticsTransmission : `lsst.afw.image.TransmissionCurve`
634 A ``TransmissionCurve`` that represents the throughput of the optics,
635 to be evaluated in focal-plane coordinates.
636 filterTransmission : `lsst.afw.image.TransmissionCurve`
637 A ``TransmissionCurve`` that represents the throughput of the filter
638 itself, to be evaluated in focal-plane coordinates.
639 sensorTransmission : `lsst.afw.image.TransmissionCurve`
640 A ``TransmissionCurve`` that represents the throughput of the sensor
641 itself, to be evaluated in post-assembly trimmed detector coordinates.
642 atmosphereTransmission : `lsst.afw.image.TransmissionCurve`
643 A ``TransmissionCurve`` that represents the throughput of the
644 atmosphere, assumed to be spatially constant.
645
646 Returns
647 -------
649 The TransmissionCurve attached to the exposure.
650
651 Notes
652 -----
653 All ``TransmissionCurve`` arguments are optional; if none are provided, the
654 attached ``TransmissionCurve`` will have unit transmission everywhere.
655 """
656 combined = afwImage.TransmissionCurve.makeIdentity()
657 if atmosphereTransmission is not None:
658 combined *= atmosphereTransmission
659 if opticsTransmission is not None:
660 combined *= opticsTransmission
661 if filterTransmission is not None:
662 combined *= filterTransmission
663 detector = exposure.getDetector()
664 fpToPix = detector.getTransform(fromSys=camGeom.FOCAL_PLANE,
665 toSys=camGeom.PIXELS)
666 combined = combined.transformedBy(fpToPix)
667 if sensorTransmission is not None:
668 combined *= sensorTransmission
669 exposure.getInfo().setTransmissionCurve(combined)
670 return combined
671
672
673def applyGains(exposure, normalizeGains=False, ptcGains=None):
674 """Scale an exposure by the amplifier gains.
675
676 Parameters
677 ----------
678 exposure : `lsst.afw.image.Exposure`
679 Exposure to process. The image is modified.
680 normalizeGains : `Bool`, optional
681 If True, then amplifiers are scaled to force the median of
682 each amplifier to equal the median of those medians.
683 ptcGains : `dict`[`str`], optional
684 Dictionary keyed by amp name containing the PTC gains.
685 """
686 ccd = exposure.getDetector()
687 ccdImage = exposure.getMaskedImage()
688
689 medians = []
690 for amp in ccd:
691 sim = ccdImage.Factory(ccdImage, amp.getBBox())
692 if ptcGains:
693 sim *= ptcGains[amp.getName()]
694 else:
695 sim *= amp.getGain()
696
697 if normalizeGains:
698 medians.append(numpy.median(sim.getImage().getArray()))
699
700 if normalizeGains:
701 median = numpy.median(numpy.array(medians))
702 for index, amp in enumerate(ccd):
703 sim = ccdImage.Factory(ccdImage, amp.getBBox())
704 if medians[index] != 0.0:
705 sim *= median/medians[index]
706
707
709 """Grow the saturation trails by an amount dependent on the width of the
710 trail.
711
712 Parameters
713 ----------
714 mask : `lsst.afw.image.Mask`
715 Mask which will have the saturated areas grown.
716 """
717
718 extraGrowDict = {}
719 for i in range(1, 6):
720 extraGrowDict[i] = 0
721 for i in range(6, 8):
722 extraGrowDict[i] = 1
723 for i in range(8, 10):
724 extraGrowDict[i] = 3
725 extraGrowMax = 4
726
727 if extraGrowMax <= 0:
728 return
729
730 saturatedBit = mask.getPlaneBitMask("SAT")
731
732 xmin, ymin = mask.getBBox().getMin()
733 width = mask.getWidth()
734
735 thresh = afwDetection.Threshold(saturatedBit, afwDetection.Threshold.BITMASK)
736 fpList = afwDetection.FootprintSet(mask, thresh).getFootprints()
737
738 for fp in fpList:
739 for s in fp.getSpans():
740 x0, x1 = s.getX0(), s.getX1()
741
742 extraGrow = extraGrowDict.get(x1 - x0 + 1, extraGrowMax)
743 if extraGrow > 0:
744 y = s.getY() - ymin
745 x0 -= xmin + extraGrow
746 x1 -= xmin - extraGrow
747
748 if x0 < 0:
749 x0 = 0
750 if x1 >= width - 1:
751 x1 = width - 1
752
753 mask.array[y, x0:x1+1] |= saturatedBit
754
755
756def setBadRegions(exposure, badStatistic="MEDIAN"):
757 """Set all BAD areas of the chip to the average of the rest of the exposure
758
759 Parameters
760 ----------
761 exposure : `lsst.afw.image.Exposure`
762 Exposure to mask. The exposure mask is modified.
763 badStatistic : `str`, optional
764 Statistic to use to generate the replacement value from the
765 image data. Allowed values are 'MEDIAN' or 'MEANCLIP'.
766
767 Returns
768 -------
769 badPixelCount : scalar
770 Number of bad pixels masked.
771 badPixelValue : scalar
772 Value substituted for bad pixels.
773
774 Raises
775 ------
776 RuntimeError
777 Raised if `badStatistic` is not an allowed value.
778 """
779 if badStatistic == "MEDIAN":
780 statistic = afwMath.MEDIAN
781 elif badStatistic == "MEANCLIP":
782 statistic = afwMath.MEANCLIP
783 else:
784 raise RuntimeError("Impossible method %s of bad region correction" % badStatistic)
785
786 mi = exposure.getMaskedImage()
787 mask = mi.getMask()
788 BAD = mask.getPlaneBitMask("BAD")
789 INTRP = mask.getPlaneBitMask("INTRP")
790
791 sctrl = afwMath.StatisticsControl()
792 sctrl.setAndMask(BAD)
793 value = afwMath.makeStatistics(mi, statistic, sctrl).getValue()
794
795 maskArray = mask.getArray()
796 imageArray = mi.getImage().getArray()
797 badPixels = numpy.logical_and((maskArray & BAD) > 0, (maskArray & INTRP) == 0)
798 imageArray[:] = numpy.where(badPixels, value, imageArray)
799
800 return badPixels.sum(), value
801
802
803def checkFilter(exposure, filterList, log):
804 """Check to see if an exposure is in a filter specified by a list.
805
806 The goal of this is to provide a unified filter checking interface
807 for all filter dependent stages.
808
809 Parameters
810 ----------
811 exposure : `lsst.afw.image.Exposure`
812 Exposure to examine.
813 filterList : `list` [`str`]
814 List of physical_filter names to check.
815 log : `logging.Logger`
816 Logger to handle messages.
817
818 Returns
819 -------
820 result : `bool`
821 True if the exposure's filter is contained in the list.
822 """
823 if len(filterList) == 0:
824 return False
825 thisFilter = exposure.getFilter()
826 if thisFilter is None:
827 log.warning("No FilterLabel attached to this exposure!")
828 return False
829
830 thisPhysicalFilter = getPhysicalFilter(thisFilter, log)
831 if thisPhysicalFilter in filterList:
832 return True
833 elif thisFilter.bandLabel in filterList:
834 if log:
835 log.warning("Physical filter (%s) should be used instead of band %s for filter configurations"
836 " (%s)", thisPhysicalFilter, thisFilter.bandLabel, filterList)
837 return True
838 else:
839 return False
840
841
842def getPhysicalFilter(filterLabel, log):
843 """Get the physical filter label associated with the given filterLabel.
844
845 If ``filterLabel`` is `None` or there is no physicalLabel attribute
846 associated with the given ``filterLabel``, the returned label will be
847 "Unknown".
848
849 Parameters
850 ----------
851 filterLabel : `lsst.afw.image.FilterLabel`
852 The `lsst.afw.image.FilterLabel` object from which to derive the
853 physical filter label.
854 log : `logging.Logger`
855 Logger to handle messages.
856
857 Returns
858 -------
859 physicalFilter : `str`
860 The value returned by the physicalLabel attribute of ``filterLabel`` if
861 it exists, otherwise set to \"Unknown\".
862 """
863 if filterLabel is None:
864 physicalFilter = "Unknown"
865 log.warning("filterLabel is None. Setting physicalFilter to \"Unknown\".")
866 else:
867 try:
868 physicalFilter = filterLabel.physicalLabel
869 except RuntimeError:
870 log.warning("filterLabel has no physicalLabel attribute. Setting physicalFilter to \"Unknown\".")
871 physicalFilter = "Unknown"
872 return physicalFilter
def biasCorrection(maskedImage, biasMaskedImage, trimToFit=False)
def growMasks(mask, radius=0, maskNameList=['BAD'], maskValue="BAD")
def darkCorrection(maskedImage, darkMaskedImage, expScale, darkScale, invert=False, trimToFit=False)
def saturationCorrection(maskedImage, saturation, fwhm, growFootprints=1, interpolate=True, maskName='SAT', fallbackValue=None)
def interpolateDefectList(maskedImage, defectList, fwhm, fallbackValue=None)
Definition: isrFunctions.py:76
def illuminationCorrection(maskedImage, illumMaskedImage, illumScale, trimToFit=True)
def brighterFatterCorrection(exposure, kernel, maxIter, threshold, applyGain, gains=None)
def makeThresholdMask(maskedImage, threshold, growFootprints=1, maskName='SAT')
def setBadRegions(exposure, badStatistic="MEDIAN")
def applyGains(exposure, normalizeGains=False, ptcGains=None)
def getPhysicalFilter(filterLabel, log)
def trimToMatchCalibBBox(rawMaskedImage, calibMaskedImage)
def updateVariance(maskedImage, gain, readNoise)
def interpolateFromMask(maskedImage, fwhm, growSaturatedFootprints=1, maskNameList=['SAT'], fallbackValue=None)
def transposeMaskedImage(maskedImage)
Definition: isrFunctions.py:56
def flatCorrection(maskedImage, flatMaskedImage, scalingType, userScale=1.0, invert=False, trimToFit=False)
def attachTransmissionCurve(exposure, opticsTransmission=None, filterTransmission=None, sensorTransmission=None, atmosphereTransmission=None)