24 from deprecated.sphinx
import deprecated
36 from contextlib
import contextmanager
38 from .overscan
import OverscanCorrectionTask, OverscanCorrectionTaskConfig
42 """Make a double Gaussian PSF. 47 FWHM of double Gaussian smoothing kernel. 51 psf : `lsst.meas.algorithms.DoubleGaussianPsf` 52 The created smoothing kernel. 54 ksize = 4*int(fwhm) + 1
55 return measAlg.DoubleGaussianPsf(ksize, ksize, fwhm/(2*math.sqrt(2*math.log(2))))
59 """Make a transposed copy of a masked image. 63 maskedImage : `lsst.afw.image.MaskedImage` 68 transposed : `lsst.afw.image.MaskedImage` 69 The transposed copy of the input image. 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
79 """Interpolate over defects specified in a defect list. 83 maskedImage : `lsst.afw.image.MaskedImage` 85 defectList : `lsst.meas.algorithms.Defects` 86 List of defects to interpolate over. 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. 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)
103 """Mask pixels based on threshold detection. 107 maskedImage : `lsst.afw.image.MaskedImage` 108 Image to process. Only the mask plane is updated. 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 118 defectList : `lsst.meas.algorithms.Defects` 119 Defect list constructed from pixels above the threshold. 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()
130 mask = maskedImage.getMask()
131 bitmask = mask.getPlaneBitMask(maskName)
132 afwDetection.setMaskFromFootprintList(mask, fpList, bitmask)
134 return measAlg.Defects.fromFootprintList(fpList)
137 def growMasks(mask, radius=0, maskNameList=['BAD'], maskValue="BAD"):
138 """Grow a mask by an amount and add to the requested plane. 142 mask : `lsst.afw.image.Mask` 143 Mask image to process. 145 Amount to grow the mask. 146 maskNameList : `str` or `list` [`str`] 147 Mask names that should be grown. 149 Mask plane to assign the newly masked pixels to. 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)
159 maskNameList=['SAT'], fallbackValue=None):
160 """Interpolate over defects identified by a particular set of mask planes. 164 maskedImage : `lsst.afw.image.MaskedImage` 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 172 fallbackValue : scalar, optional 173 Value of last resort for interpolation. 175 mask = maskedImage.getMask()
177 if growSaturatedFootprints > 0
and "SAT" in maskNameList:
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())
191 def saturationCorrection(maskedImage, saturation, fwhm, growFootprints=1, interpolate=True, maskName='SAT',
193 """Mark saturated pixels and optionally interpolate over them 197 maskedImage : `lsst.afw.image.MaskedImage` 200 Saturation level used as the detection threshold. 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 209 fallbackValue : scalar, optional 210 Value of last resort for interpolation. 213 maskedImage=maskedImage,
214 threshold=saturation,
215 growFootprints=growFootprints,
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 234 rawMaskedImage : `lsst.afw.image.MaskedImage` 236 calibMaskedImage : `lsst.afw.image.MaskedImage` 237 Calibration image to draw new bounding box from. 241 replacementMaskedImage : `lsst.afw.image.MaskedImage` 242 ``rawMaskedImage`` trimmed to the appropriate size 246 Rasied if ``rawMaskedImage`` cannot be symmetrically trimmed to 247 match ``calibMaskedImage``. 249 nx, ny = rawMaskedImage.getBBox().getDimensions() - calibMaskedImage.getBBox().getDimensions()
251 raise RuntimeError(
"Raw and calib maskedImages are trimmed differently in X and Y.")
253 raise RuntimeError(
"Calibration maskedImage is trimmed unevenly in X.")
255 raise RuntimeError(
"Calibration maskedImage is larger than raw data.")
259 replacementMaskedImage = rawMaskedImage[nEdge:-nEdge, nEdge:-nEdge, afwImage.LOCAL]
260 SourceDetectionTask.setEdgeBits(
262 replacementMaskedImage.getBBox(),
263 rawMaskedImage.getMask().getPlaneBitMask(
"EDGE")
266 replacementMaskedImage = rawMaskedImage
268 return replacementMaskedImage
272 """Apply bias correction in place. 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 287 Raised if ``maskedImage`` and ``biasMaskedImage`` do not have 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
300 def darkCorrection(maskedImage, darkMaskedImage, expScale, darkScale, invert=False, trimToFit=False):
301 """Apply dark correction in place. 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``. 310 Dark exposure time for ``maskedImage``. 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 322 Raised if ``maskedImage`` and ``darkMaskedImage`` do not have 327 The dark correction is applied by calculating: 328 maskedImage -= dark * expScaling / darkScaling 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
339 maskedImage.scaledMinus(scale, darkMaskedImage)
341 maskedImage.scaledPlus(scale, darkMaskedImage)
345 """Set the variance plane based on the image plane. 349 maskedImage : `lsst.afw.image.MaskedImage` 350 Image to process. The variance plane is modified. 352 The amplifier gain in electrons/ADU. 354 The amplifier read nmoise in ADU/pixel. 356 var = maskedImage.getVariance()
357 var[:] = maskedImage.getImage()
362 def flatCorrection(maskedImage, flatMaskedImage, scalingType, userScale=1.0, invert=False, trimToFit=False):
363 """Apply flat correction in place. 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`` 372 Flat scale computation method. Allowed values are 'MEAN', 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 385 Raised if ``maskedImage`` and ``flatMaskedImage`` do not have 386 the same size or if ``scalingType`` is not an allowed value. 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)))
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
404 raise RuntimeError(
'%s : %s not implemented' % (
"flatCorrection", scalingType))
407 maskedImage.scaledDivides(1.0/flatScale, flatMaskedImage)
409 maskedImage.scaledMultiplies(1.0/flatScale, flatMaskedImage)
413 """Apply illumination correction in place. 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``. 422 Scale factor for the illumination correction. 423 trimToFit : `Bool`, optional 424 If True, raw data is symmetrically trimmed to match 430 Raised if ``maskedImage`` and ``illumMaskedImage`` do not have 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)
443 def overscanCorrection(ampMaskedImage, overscanImage, fitType='MEDIAN', order=1, collapseRej=3.0,
444 statControl=None, overscanIsInt=True):
445 """Apply overscan correction in place. 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. 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. 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. 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`) 490 Raised if ``fitType`` is not an allowed value. 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). 504 ampImage = ampMaskedImage.getImage()
508 config.fitType = fitType
512 config.numSigmaClip = collapseRej
514 config.overscanIsInt =
True 517 return overscanTask.run(ampImage, overscanImage)
521 """Apply brighter fatter correction in place for the image. 525 exposure : `lsst.afw.image.Exposure` 526 Exposure to have brighter-fatter correction applied. Modified 528 kernel : `numpy.ndarray` 529 Brighter-fatter kernel to apply. 531 Number of correction iterations to run. 533 Convergence threshold in terms of the sum of absolute 534 deviations between an iteration and the previous one. 536 If True, then the exposure values are scaled by the gain prior 538 gains : `dict` [`str`, `float`] 539 A dictionary, keyed by amplifier name, of the gains to use. 540 If gains is None, the nominal gains in the amplifier object are used. 545 Final difference between iterations achieved in correction. 547 Number of iterations used to calculate correction. 551 This correction takes a kernel that has been derived from flat 552 field images to redistribute the charge. The gradient of the 553 kernel is the deflection field due to the accumulated charge. 555 Given the original image I(x) and the kernel K(x) we can compute 556 the corrected image Ic(x) using the following equation: 558 Ic(x) = I(x) + 0.5*d/dx(I(x)*d/dx(int( dy*K(x-y)*I(y)))) 560 To evaluate the derivative term we expand it as follows: 562 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))) ) 564 Because we use the measured counts instead of the incident counts 565 we apply the correction iteratively to reconstruct the original 566 counts and the correction. We stop iterating when the summed 567 difference between the current corrected image and the one from 568 the previous iteration is below the threshold. We do not require 569 convergence because the number of iterations is too large a 570 computational cost. How we define the threshold still needs to be 571 evaluated, the current default was shown to work reasonably well 572 on a small set of images. For more information on the method see 573 DocuShare Document-19407. 575 The edges as defined by the kernel are not corrected because they 576 have spurious values due to the convolution. 578 image = exposure.getMaskedImage().getImage()
581 with
gainContext(exposure, image, applyGain, gains):
583 kLx = numpy.shape(kernel)[0]
584 kLy = numpy.shape(kernel)[1]
585 kernelImage = afwImage.ImageD(kLx, kLy)
586 kernelImage.getArray()[:, :] = kernel
587 tempImage = image.clone()
589 nanIndex = numpy.isnan(tempImage.getArray())
590 tempImage.getArray()[nanIndex] = 0.
592 outImage = afwImage.ImageF(image.getDimensions())
593 corr = numpy.zeros_like(image.getArray())
594 prev_image = numpy.zeros_like(image.getArray())
595 convCntrl = afwMath.ConvolutionControl(
False,
True, 1)
596 fixedKernel = afwMath.FixedKernel(kernelImage)
606 for iteration
in range(maxIter):
608 afwMath.convolve(outImage, tempImage, fixedKernel, convCntrl)
609 tmpArray = tempImage.getArray()
610 outArray = outImage.getArray()
612 with numpy.errstate(invalid=
"ignore", over=
"ignore"):
614 gradTmp = numpy.gradient(tmpArray[startY:endY, startX:endX])
615 gradOut = numpy.gradient(outArray[startY:endY, startX:endX])
616 first = (gradTmp[0]*gradOut[0] + gradTmp[1]*gradOut[1])[1:-1, 1:-1]
619 diffOut20 = numpy.diff(outArray, 2, 0)[startY:endY, startX + 1:endX - 1]
620 diffOut21 = numpy.diff(outArray, 2, 1)[startY + 1:endY - 1, startX:endX]
621 second = tmpArray[startY + 1:endY - 1, startX + 1:endX - 1]*(diffOut20 + diffOut21)
623 corr[startY + 1:endY - 1, startX + 1:endX - 1] = 0.5*(first + second)
625 tmpArray[:, :] = image.getArray()[:, :]
626 tmpArray[nanIndex] = 0.
627 tmpArray[startY:endY, startX:endX] += corr[startY:endY, startX:endX]
630 diff = numpy.sum(numpy.abs(prev_image - tmpArray))
634 prev_image[:, :] = tmpArray[:, :]
636 image.getArray()[startY + 1:endY - 1, startX + 1:endX - 1] += \
637 corr[startY + 1:endY - 1, startX + 1:endX - 1]
639 return diff, iteration
644 """Context manager that applies and removes gain. 648 exp : `lsst.afw.image.Exposure` 649 Exposure to apply/remove gain. 650 image : `lsst.afw.image.Image` 651 Image to apply/remove gain. 653 If True, apply and remove the amplifier gain. 654 gains : `dict` [`str`, `float`] 655 A dictionary, keyed by amplifier name, of the gains to use. 656 If gains is None, the nominal gains in the amplifier object are used. 660 exp : `lsst.afw.image.Exposure` 661 Exposure with the gain applied. 665 if gains
and apply
is True:
666 ampNames = [amp.getName()
for amp
in exp.getDetector()]
667 for ampName
in ampNames:
668 if ampName
not in gains.keys():
669 raise RuntimeError(f
"Gains provided to gain context, but no entry found for amp {ampName}")
672 ccd = exp.getDetector()
674 sim = image.Factory(image, amp.getBBox())
676 gain = gains[amp.getName()]
685 ccd = exp.getDetector()
687 sim = image.Factory(image, amp.getBBox())
689 gain = gains[amp.getName()]
696 sensorTransmission=None, atmosphereTransmission=None):
697 """Attach a TransmissionCurve to an Exposure, given separate curves for 698 different components. 702 exposure : `lsst.afw.image.Exposure` 703 Exposure object to modify by attaching the product of all given 704 ``TransmissionCurves`` in post-assembly trimmed detector coordinates. 705 Must have a valid ``Detector`` attached that matches the detector 706 associated with sensorTransmission. 707 opticsTransmission : `lsst.afw.image.TransmissionCurve` 708 A ``TransmissionCurve`` that represents the throughput of the optics, 709 to be evaluated in focal-plane coordinates. 710 filterTransmission : `lsst.afw.image.TransmissionCurve` 711 A ``TransmissionCurve`` that represents the throughput of the filter 712 itself, to be evaluated in focal-plane coordinates. 713 sensorTransmission : `lsst.afw.image.TransmissionCurve` 714 A ``TransmissionCurve`` that represents the throughput of the sensor 715 itself, to be evaluated in post-assembly trimmed detector coordinates. 716 atmosphereTransmission : `lsst.afw.image.TransmissionCurve` 717 A ``TransmissionCurve`` that represents the throughput of the 718 atmosphere, assumed to be spatially constant. 722 combined : `lsst.afw.image.TransmissionCurve` 723 The TransmissionCurve attached to the exposure. 727 All ``TransmissionCurve`` arguments are optional; if none are provided, the 728 attached ``TransmissionCurve`` will have unit transmission everywhere. 730 combined = afwImage.TransmissionCurve.makeIdentity()
731 if atmosphereTransmission
is not None:
732 combined *= atmosphereTransmission
733 if opticsTransmission
is not None:
734 combined *= opticsTransmission
735 if filterTransmission
is not None:
736 combined *= filterTransmission
737 detector = exposure.getDetector()
738 fpToPix = detector.getTransform(fromSys=camGeom.FOCAL_PLANE,
739 toSys=camGeom.PIXELS)
740 combined = combined.transformedBy(fpToPix)
741 if sensorTransmission
is not None:
742 combined *= sensorTransmission
743 exposure.getInfo().setTransmissionCurve(combined)
747 @deprecated(reason=
"Camera geometry-based SkyWcs are now set when reading raws. To be removed after v19.",
748 category=FutureWarning)
750 """!Update the WCS in exposure with a distortion model based on camera 755 exposure : `lsst.afw.image.Exposure` 756 Exposure to process. Must contain a Detector and WCS. The 757 exposure is modified. 758 camera : `lsst.afw.cameraGeom.Camera` 764 Raised if ``exposure`` is lacking a Detector or WCS, or if 768 Add a model for optical distortion based on geometry found in ``camera`` 769 and the ``exposure``'s detector. The raw input exposure is assumed 770 have a TAN WCS that has no compensation for optical distortion. 771 Two other possibilities are: 772 - The raw input exposure already has a model for optical distortion, 773 as is the case for raw DECam data. 774 In that case you should set config.doAddDistortionModel False. 775 - The raw input exposure has a model for distortion, but it has known 776 deficiencies severe enough to be worth fixing (e.g. because they 777 cause problems for fitting a better WCS). In that case you should 778 override this method with a version suitable for your raw data. 781 wcs = exposure.getWcs()
783 raise RuntimeError(
"exposure has no WCS")
785 raise RuntimeError(
"camera is None")
786 detector = exposure.getDetector()
788 raise RuntimeError(
"exposure has no Detector")
789 pixelToFocalPlane = detector.getTransform(camGeom.PIXELS, camGeom.FOCAL_PLANE)
790 focalPlaneToFieldAngle = camera.getTransformMap().getTransform(camGeom.FOCAL_PLANE,
792 distortedWcs = makeDistortedTanWcs(wcs, pixelToFocalPlane, focalPlaneToFieldAngle)
793 exposure.setWcs(distortedWcs)
797 """Scale an exposure by the amplifier gains. 801 exposure : `lsst.afw.image.Exposure` 802 Exposure to process. The image is modified. 803 normalizeGains : `Bool`, optional 804 If True, then amplifiers are scaled to force the median of 805 each amplifier to equal the median of those medians. 807 ccd = exposure.getDetector()
808 ccdImage = exposure.getMaskedImage()
812 sim = ccdImage.Factory(ccdImage, amp.getBBox())
816 medians.append(numpy.median(sim.getImage().getArray()))
819 median = numpy.median(numpy.array(medians))
820 for index, amp
in enumerate(ccd):
821 sim = ccdImage.Factory(ccdImage, amp.getBBox())
822 if medians[index] != 0.0:
823 sim *= median/medians[index]
827 """Grow the saturation trails by an amount dependent on the width of the trail. 831 mask : `lsst.afw.image.Mask` 832 Mask which will have the saturated areas grown. 836 for i
in range(1, 6):
838 for i
in range(6, 8):
840 for i
in range(8, 10):
844 if extraGrowMax <= 0:
847 saturatedBit = mask.getPlaneBitMask(
"SAT")
849 xmin, ymin = mask.getBBox().getMin()
850 width = mask.getWidth()
852 thresh = afwDetection.Threshold(saturatedBit, afwDetection.Threshold.BITMASK)
853 fpList = afwDetection.FootprintSet(mask, thresh).getFootprints()
856 for s
in fp.getSpans():
857 x0, x1 = s.getX0(), s.getX1()
859 extraGrow = extraGrowDict.get(x1 - x0 + 1, extraGrowMax)
862 x0 -= xmin + extraGrow
863 x1 -= xmin - extraGrow
870 mask.array[y, x0:x1+1] |= saturatedBit
874 """Set all BAD areas of the chip to the average of the rest of the exposure 878 exposure : `lsst.afw.image.Exposure` 879 Exposure to mask. The exposure mask is modified. 880 badStatistic : `str`, optional 881 Statistic to use to generate the replacement value from the 882 image data. Allowed values are 'MEDIAN' or 'MEANCLIP'. 886 badPixelCount : scalar 887 Number of bad pixels masked. 888 badPixelValue : scalar 889 Value substituted for bad pixels. 894 Raised if `badStatistic` is not an allowed value. 896 if badStatistic ==
"MEDIAN":
897 statistic = afwMath.MEDIAN
898 elif badStatistic ==
"MEANCLIP":
899 statistic = afwMath.MEANCLIP
901 raise RuntimeError(
"Impossible method %s of bad region correction" % badStatistic)
903 mi = exposure.getMaskedImage()
905 BAD = mask.getPlaneBitMask(
"BAD")
906 INTRP = mask.getPlaneBitMask(
"INTRP")
908 sctrl = afwMath.StatisticsControl()
909 sctrl.setAndMask(BAD)
910 value = afwMath.makeStatistics(mi, statistic, sctrl).getValue()
912 maskArray = mask.getArray()
913 imageArray = mi.getImage().getArray()
914 badPixels = numpy.logical_and((maskArray & BAD) > 0, (maskArray & INTRP) == 0)
915 imageArray[:] = numpy.where(badPixels, value, imageArray)
917 return badPixels.sum(), value
def addDistortionModel(exposure, camera)
Update the WCS in exposure with a distortion model based on camera geometry.
def saturationCorrection(maskedImage, saturation, fwhm, growFootprints=1, interpolate=True, maskName='SAT', fallbackValue=None)
def setBadRegions(exposure, badStatistic="MEDIAN")
def interpolateFromMask(maskedImage, fwhm, growSaturatedFootprints=1, maskNameList=['SAT'], fallbackValue=None)
def interpolateDefectList(maskedImage, defectList, fwhm, fallbackValue=None)
def brighterFatterCorrection(exposure, kernel, maxIter, threshold, applyGain, gains=None)
def transposeMaskedImage(maskedImage)
def trimToMatchCalibBBox(rawMaskedImage, calibMaskedImage)
def attachTransmissionCurve(exposure, opticsTransmission=None, filterTransmission=None, sensorTransmission=None, atmosphereTransmission=None)
def flatCorrection(maskedImage, flatMaskedImage, scalingType, userScale=1.0, invert=False, trimToFit=False)
def makeThresholdMask(maskedImage, threshold, growFootprints=1, maskName='SAT')
def growMasks(mask, radius=0, maskNameList=['BAD'], maskValue="BAD")
def widenSaturationTrails(mask)
def applyGains(exposure, normalizeGains=False)
def darkCorrection(maskedImage, darkMaskedImage, expScale, darkScale, invert=False, trimToFit=False)
def biasCorrection(maskedImage, biasMaskedImage, trimToFit=False)
def gainContext(exp, image, apply, gains=None)
def illuminationCorrection(maskedImage, illumMaskedImage, illumScale, trimToFit=True)
def updateVariance(maskedImage, gain, readNoise)
def overscanCorrection(ampMaskedImage, overscanImage, fitType='MEDIAN', order=1, collapseRej=3.0, statControl=None, overscanIsInt=True)