LSSTApplications: lsst.ip.isr.isr Namespace Reference

def lsst.ip.isr.isr.calcEffectiveGain ( maskedImage )

Calculate effective gain

@param[in] maskedImage  afw.image.MaskedImage to process
@return (median gain, mean gain) in e-/ADU

Definition at line 42 of file isr.py.

 
 def calcEffectiveGain(maskedImage):
     """Calculate effective gain
 
     @param[in] maskedImage  afw.image.MaskedImage to process
     @return (median gain, mean gain) in e-/ADU
     """
     im = afwImage.ImageF(maskedImage.getImage(), True)
     var = maskedImage.getVariance()
     im /= var
     medgain = afwMath.makeStatistics(im, afwMath.MEDIAN).getValue()
     meangain = afwMath.makeStatistics(im, afwMath.MEANCLIP).getValue()
     return medgain, meangain

lsst::ip::isr.isr.calcEffectiveGain

def calcEffectiveGain

Definition: isr.py:42

lsst.afw.math::makeStatistics

Statistics makeStatistics(afwImage::Mask< afwImage::MaskPixel > const &msk, int const flags, StatisticsControl const &sctrl)

Specialization to handle Masks.

Definition: Statistics.cc:1082

def lsst.ip.isr.isr.createPsf ( fwhm )

Make a double Gaussian PSF

@param[in] fwhm  FWHM of double Gaussian smoothing kernel
@return measAlg.DoubleGaussianPsf

Definition at line 33 of file isr.py.

 
 def createPsf(fwhm):
     """Make a double Gaussian PSF
 
     @param[in] fwhm  FWHM of double Gaussian smoothing kernel
     @return measAlg.DoubleGaussianPsf
     """
     ksize = 4*int(fwhm) + 1
     return measAlg.DoubleGaussianPsf(ksize, ksize, fwhm/(2*math.sqrt(2*math.log(2))))

lsst::meas::algorithms::DoubleGaussianPsf

Represent a Psf as a circularly symmetrical double Gaussian.

Definition: DoubleGaussianPsf.h:35

lsst::ip::isr.isr.createPsf

def createPsf

Definition: isr.py:33

def lsst.ip.isr.isr.darkCorrection	(	maskedImage,
		darkMaskedImage,
		expScale,
		darkScale
	)

Apply dark correction in place

maskedImage -= dark * expScaling / darkScaling

@param[in,out] maskedImage  afw.image.MaskedImage to correct
@param[in] darkMaskedImage  dark afw.image.MaskedImage
@param[in] expScale  exposure scale
@param[in] darkScale  dark scale

Definition at line 217 of file isr.py.

 
 def darkCorrection(maskedImage, darkMaskedImage, expScale, darkScale):
     """Apply dark correction in place
 
     maskedImage -= dark * expScaling / darkScaling
 
     @param[in,out] maskedImage  afw.image.MaskedImage to correct
     @param[in] darkMaskedImage  dark afw.image.MaskedImage
     @param[in] expScale  exposure scale
     @param[in] darkScale  dark scale
     """
     if maskedImage.getBBox(afwImage.LOCAL) != darkMaskedImage.getBBox(afwImage.LOCAL):
         raise RuntimeError("maskedImage bbox %s != darkMaskedImage bbox %s" % \
             (maskedImage.getBBox(afwImage.LOCAL), darkMaskedImage.getBBox(afwImage.LOCAL)))
 
     scale = expScale / darkScale
     maskedImage.scaledMinus(scale, darkMaskedImage)

lsst::ip::isr.isr.darkCorrection

def darkCorrection

Definition: isr.py:217

def lsst.ip.isr.isr.defectListFromFootprintList	(	fpList,
		growFootprints = `1`
	)

Compute a defect list from a footprint list, optionally growing the footprints

@param[in] fpList  footprint list
@param[in] growFootprints  amount by which to grow footprints of detected regions
@return meas.algorithms.DefectListT

Definition at line 83 of file isr.py.

 
 def defectListFromFootprintList(fpList, growFootprints=1):
     """Compute a defect list from a footprint list, optionally growing the footprints
 
     @param[in] fpList  footprint list
     @param[in] growFootprints  amount by which to grow footprints of detected regions
     @return meas.algorithms.DefectListT
     """
     defectList = measAlg.DefectListT()
     for fp in fpList:
         if growFootprints > 0:
             # if "True", growing requires a convolution
             # if "False", its faster
             fpGrow = afwDetection.growFootprint(fp, growFootprints, False)
         else:
             fpGrow = fp
         for bbox in afwDetection.footprintToBBoxList(fpGrow):
             defect = measAlg.Defect(bbox)
             defectList.push_back(defect)
     return defectList

lsst.afw.detection::growFootprint

boost::shared_ptr< Footprint > growFootprint(Footprint const &foot, int nGrow, bool left, bool right, bool up, bool down)

Grow a Footprint in at least one of the cardinal directions, returning a new Footprint.

lsst.afw.detection::footprintToBBoxList

std::vector< lsst::afw::geom::Box2I > footprintToBBoxList(Footprint const &foot)

lsst::meas::algorithms::Defect

Encapsulate information about a bad portion of a detector.

Definition: Interp.h:70

lsst::ip::isr.isr.defectListFromFootprintList

def defectListFromFootprintList

Definition: isr.py:83

def lsst.ip.isr.isr.flatCorrection	(	maskedImage,
		flatMaskedImage,
		scalingType,
		userScale = `1.0`
	)

Apply flat correction in place

@param[in,out] maskedImage  afw.image.MaskedImage to correct
@param[in] flatMaskedImage  flat field afw.image.MaskedImage
@param[in] scalingType  how to compute flat scale; one of 'MEAN', 'MEDIAN' or 'USER'
@param[in] userScale  scale to use if scalingType is 'USER', else ignored

Definition at line 246 of file isr.py.

 
 def flatCorrection(maskedImage, flatMaskedImage, scalingType, userScale=1.0):
     """Apply flat correction in place
 
     @param[in,out] maskedImage  afw.image.MaskedImage to correct
     @param[in] flatMaskedImage  flat field afw.image.MaskedImage
     @param[in] scalingType  how to compute flat scale; one of 'MEAN', 'MEDIAN' or 'USER'
     @param[in] userScale  scale to use if scalingType is 'USER', else ignored
     """
     if maskedImage.getBBox(afwImage.LOCAL) != flatMaskedImage.getBBox(afwImage.LOCAL):
         raise RuntimeError("maskedImage bbox %s != flatMaskedImage bbox %s" % \
             (maskedImage.getBBox(afwImage.LOCAL), flatMaskedImage.getBBox(afwImage.LOCAL)))
 
     # Figure out scale from the data
     # Ideally the flats are normalized by the calibration product pipelin, but this allows some flexibility
     # in the case that the flat is created by some other mechanism.
     if scalingType == 'MEAN':
         flatScale = afwMath.makeStatistics(flatMaskedImage.getImage(), afwMath.MEAN).getValue(afwMath.MEAN)
     elif scalingType == 'MEDIAN':
         flatScale = afwMath.makeStatistics(flatMaskedImage.getImage(), afwMath.MEDIAN).getValue(afwMath.MEDIAN)
     elif scalingType == 'USER':
         flatScale = userScale
     else:
         raise pexExcept.Exception, '%s : %s not implemented' % ("flatCorrection", scalingType)
 
     maskedImage.scaledDivides(1.0/flatScale, flatMaskedImage)

lsst::ip::isr.isr.flatCorrection

def flatCorrection

Definition: isr.py:246

lsst::pex::exceptions::Exception

Definition: Exception.h:99

lsst.afw.math::makeStatistics

Statistics makeStatistics(afwImage::Mask< afwImage::MaskPixel > const &msk, int const flags, StatisticsControl const &sctrl)

Specialization to handle Masks.

Definition: Statistics.cc:1082

def lsst.ip.isr.isr.getDefectListFromMask	(	maskedImage,
		maskName,
		growFootprints = `1`
	)

Compute a defect list from a specified mask plane

@param[in] maskedImage  masked image to process
@param[in] maskName  mask plane name, or list of names
@param[in] growFootprints  amount by which to grow footprints of detected regions
@return meas.algrithms.DefectListT of regions in mask

Definition at line 131 of file isr.py.

 
 def getDefectListFromMask(maskedImage, maskName, growFootprints=1):
     """Compute a defect list from a specified mask plane
 
     @param[in] maskedImage  masked image to process
     @param[in] maskName  mask plane name, or list of names
     @param[in] growFootprints  amount by which to grow footprints of detected regions
     @return meas.algrithms.DefectListT of regions in mask
     """
     mask = maskedImage.getMask()
     workmask = afwImage.MaskU(mask, True)
     workmask &= mask.getPlaneBitMask(maskName)
     thresh = afwDetection.Threshold(0.5)
     maskimg = afwImage.ImageU(workmask.getBBox())
     maskimg <<= workmask
     ds = afwDetection.FootprintSet(maskimg, thresh)
     fpList = ds.getFootprints()
     return defectListFromFootprintList(fpList, growFootprints)

lsst.afw.detection::Threshold

A Threshold is used to pass a threshold value to detection algorithms.

Definition: Threshold.h:44

lsst::ip::isr.isr.getDefectListFromMask

def getDefectListFromMask

Definition: isr.py:131

lsst.afw.detection::FootprintSet

A set of Footprints, associated with a MaskedImage.

Definition: FootprintSet.h:53

lsst::ip::isr.isr.defectListFromFootprintList

def defectListFromFootprintList

Definition: isr.py:83

def lsst.ip.isr.isr.illuminationCorrection	(	maskedImage,
		illumMaskedImage,
		illumScale
	)

Apply illumination correction in place

@param[in,out] maskedImage  afw.image.MaskedImage to correct
@param[in] illumMaskedImage  illumination correction masked image
@param[in] illumScale  scale value for illumination correction

Definition at line 272 of file isr.py.

 
 def illuminationCorrection(maskedImage, illumMaskedImage, illumScale):
     """Apply illumination correction in place
 
     @param[in,out] maskedImage  afw.image.MaskedImage to correct
     @param[in] illumMaskedImage  illumination correction masked image
     @param[in] illumScale  scale value for illumination correction
     """
     if maskedImage.getBBox(afwImage.LOCAL) != illumMaskedImage.getBBox(afwImage.LOCAL):
         raise RuntimeError("maskedImage bbox %s != illumMaskedImage bbox %s" % \
             (maskedImage.getBBox(afwImage.LOCAL), illumMaskedImage.getBBox(afwImage.LOCAL)))
 
     maskedImage.scaledDivides(1./illumScale, illumMaskedImage)

lsst::ip::isr.isr.illuminationCorrection

def illuminationCorrection

Definition: isr.py:272

def lsst.ip.isr.isr.interpolateDefectList	(	maskedImage,
		defectList,
		fwhm,
		fallbackValue = `None`
	)

Interpolate over defects specified in a defect list

@param[in,out] maskedImage  masked image to process
@param[in] defectList  defect list
@param[in] fwhm  FWHM of double Gaussian smoothing kernel
@param[in] fallbackValue  fallback value if an interpolated value cannot be determined;
                          if None then use clipped mean image value

Definition at line 67 of file isr.py.

 
 def interpolateDefectList(maskedImage, defectList, fwhm, fallbackValue=None):
     """Interpolate over defects specified in a defect list
 
     @param[in,out] maskedImage  masked image to process
     @param[in] defectList  defect list
     @param[in] fwhm  FWHM of double Gaussian smoothing kernel
     @param[in] fallbackValue  fallback value if an interpolated value cannot be determined;
                               if None then use clipped mean image value
     """
     psf = createPsf(fwhm)
     if fallbackValue is None:
         fallbackValue = afwMath.makeStatistics(maskedImage.getImage(), afwMath.MEANCLIP).getValue()
     if 'INTRP' not in maskedImage.getMask().getMaskPlaneDict().keys():
         maskedImage.getMask.addMaskPlane('INTRP')
     measAlg.interpolateOverDefects(maskedImage, psf, defectList, fallbackValue, True)

lsst::meas::algorithms::interpolateOverDefects

void interpolateOverDefects(MaskedImageT &image, lsst::afw::detection::Psf const &psf, std::vector< Defect::Ptr > &badList, double fallbackValue=0.0, bool useFallbackValueAtEdge=false)

Process a set of known bad pixels in an image.

Definition: Interp.cc:2058

lsst::ip::isr.isr.interpolateDefectList

def interpolateDefectList

Definition: isr.py:67

lsst::ip::isr.isr.createPsf

def createPsf

Definition: isr.py:33

lsst.afw.math::makeStatistics

Statistics makeStatistics(afwImage::Mask< afwImage::MaskPixel > const &msk, int const flags, StatisticsControl const &sctrl)

Specialization to handle Masks.

Definition: Statistics.cc:1082

def lsst.ip.isr.isr.interpolateFromMask	(	maskedImage,
		fwhm,
		growFootprints = `1`,
		maskName = `'SAT'`,
		fallbackValue = `None`
	)

Interpolate over defects identified by a particular mask plane

@param[in,out] maskedImage  afw.image.MaskedImage to process
@param[in] fwhm  FWHM of double Gaussian smoothing kernel
@param[in] growFootprints  amount by which to grow footprints of detected regions
@param[in] maskName  mask plane name
@param[in] fallbackValue  value of last resort for interpolation

Definition at line 173 of file isr.py.

 
 def interpolateFromMask(maskedImage, fwhm, growFootprints=1, maskName='SAT', fallbackValue=None):
     """Interpolate over defects identified by a particular mask plane
 
     @param[in,out] maskedImage  afw.image.MaskedImage to process
     @param[in] fwhm  FWHM of double Gaussian smoothing kernel
     @param[in] growFootprints  amount by which to grow footprints of detected regions
     @param[in] maskName  mask plane name
     @param[in] fallbackValue  value of last resort for interpolation
     """
     defectList = getDefectListFromMask(maskedImage, maskName, growFootprints)
     interpolateDefectList(maskedImage, defectList, fwhm, fallbackValue=fallbackValue)

lsst::ip::isr.isr.interpolateDefectList

def interpolateDefectList

Definition: isr.py:67

lsst::ip::isr.isr.getDefectListFromMask

def getDefectListFromMask

Definition: isr.py:131

lsst::ip::isr.isr.interpolateFromMask

def interpolateFromMask

Definition: isr.py:173

def lsst.ip.isr.isr.makeThresholdMask	(	maskedImage,
		threshold,
		growFootprints = `1`,
		maskName = `'SAT'`
	)

Mask pixels based on threshold detection

@param[in,out] maskedImage  afw.image.MaskedImage to process; the mask is altered
@param[in] threshold  detection threshold
@param[in] growFootprints  amount by which to grow footprints of detected regions
@param[in] maskName  mask plane name
@return meas.algorihtms.DefectListT of regions set in the mask.

Definition at line 149 of file isr.py.

 
 def makeThresholdMask(maskedImage, threshold, growFootprints=1, maskName = 'SAT'):
     """Mask pixels based on threshold detection
 
     @param[in,out] maskedImage  afw.image.MaskedImage to process; the mask is altered
     @param[in] threshold  detection threshold
     @param[in] growFootprints  amount by which to grow footprints of detected regions
     @param[in] maskName  mask plane name
     @return meas.algorihtms.DefectListT of regions set in the mask.
     """
     # find saturated regions
     thresh = afwDetection.Threshold(threshold)
     fs = afwDetection.FootprintSet(maskedImage, thresh)
 
     if growFootprints > 0:
         fs = afwDetection.FootprintSet(fs, growFootprints)
 
     fpList = fs.getFootprints()
     # set mask
     mask = maskedImage.getMask()
     bitmask = mask.getPlaneBitMask(maskName)
     afwDetection.setMaskFromFootprintList(mask, fpList, bitmask)
 
     return defectListFromFootprintList(fpList, growFootprints=0)

lsst.afw.detection::Threshold

A Threshold is used to pass a threshold value to detection algorithms.

Definition: Threshold.h:44

lsst::ip::isr.isr.makeThresholdMask

def makeThresholdMask

Definition: isr.py:149

lsst.afw.detection::FootprintSet

A set of Footprints, associated with a MaskedImage.

Definition: FootprintSet.h:53

lsst.afw.detection::setMaskFromFootprintList

MaskT setMaskFromFootprintList(lsst::afw::image::Mask< MaskT > *mask, boost::shared_ptr< std::vector< boost::shared_ptr< Footprint >> const > const &footprints, MaskT const bitmask)

OR bitmask into all the Mask's pixels which are in the set of Footprints.

lsst::ip::isr.isr.defectListFromFootprintList

def defectListFromFootprintList

Definition: isr.py:83

def lsst.ip.isr.isr.maskPixelsFromDefectList	(	maskedImage,
		defectList,
		maskName = `'BAD'`
	)

Set mask plane based on a defect list

@param[in,out] maskedImage  afw.image.MaskedImage to process; mask plane is updated
@param[in] defectList  meas.algorithms.DefectListT
@param[in] maskName  mask plane name

Definition at line 117 of file isr.py.

 
 def maskPixelsFromDefectList(maskedImage, defectList, maskName='BAD'):
     """Set mask plane based on a defect list
 
     @param[in,out] maskedImage  afw.image.MaskedImage to process; mask plane is updated
     @param[in] defectList  meas.algorithms.DefectListT
     @param[in] maskName  mask plane name
     """
     # mask bad pixels
     mask = maskedImage.getMask()
     bitmask = mask.getPlaneBitMask(maskName)
     for defect in defectList:
         bbox = defect.getBBox()
         afwDetection.setMaskFromFootprint(mask, afwDetection.Footprint(bbox), bitmask)

lsst::ip::isr.isr.maskPixelsFromDefectList

def maskPixelsFromDefectList

Definition: isr.py:117

lsst.afw.detection::Footprint

A set of pixels in an Image.

Definition: Footprint.h:62

lsst.afw.detection::setMaskFromFootprint

MaskT setMaskFromFootprint(lsst::afw::image::Mask< MaskT > *mask, Footprint const &footprint, MaskT const bitmask)

OR bitmask into all the Mask's pixels that are in the Footprint.

def lsst.ip.isr.isr.overscanCorrection	(	ampMaskedImage,
		overscanImage,
		fitType = `'MEDIAN'`,
		order = `1`,
		collapseRej = `3.0`,
		statControl = `None`
	)

Apply overscan correction in place

@param[in,out] ampMaskedImage  masked image to correct
@param[in] overscanImage  overscan data as an afw.image.Image or afw.image.MaskedImage.
                          If a masked image is passed in the mask plane will be used
                          to constrain the fit of the bias level.
@param[in] fitType  type of fit for overscan correction; one of:
                    - 'MEAN'
                    - 'MEDIAN'
                    - 'POLY' (ordinary polynomial)
                    - 'CHEB' (Chebyshev polynomial)
                    - 'LEG' (Legendre polynomial)
                    - 'NATURAL_SPLINE', 'CUBIC_SPLINE', 'AKIMA_SPLINE' (splines)
@param[in] order  polynomial order or spline knots (ignored unless fitType
                  indicates a polynomial or spline)
@param[in] collapseRej  Rejection threshold (sigma) for collapsing dimension of overscan
@param[in] statControl  Statistics control object

Definition at line 286 of file isr.py.

 
                        statControl=None):
     """Apply overscan correction in place
 
     @param[in,out] ampMaskedImage  masked image to correct
     @param[in] overscanImage  overscan data as an afw.image.Image or afw.image.MaskedImage.
                               If a masked image is passed in the mask plane will be used
                               to constrain the fit of the bias level.
     @param[in] fitType  type of fit for overscan correction; one of:
                         - 'MEAN'
                         - 'MEDIAN'
                         - 'POLY' (ordinary polynomial)
                         - 'CHEB' (Chebyshev polynomial)
                         - 'LEG' (Legendre polynomial)
                         - 'NATURAL_SPLINE', 'CUBIC_SPLINE', 'AKIMA_SPLINE' (splines)
     @param[in] order  polynomial order or spline knots (ignored unless fitType
                       indicates a polynomial or spline)
     @param[in] collapseRej  Rejection threshold (sigma) for collapsing dimension of overscan
     @param[in] statControl  Statistics control object
     """
     ampImage = ampMaskedImage.getImage()
     if statControl is None:
         statControl = afwMath.StatisticsControl()
     if fitType == 'MEAN':
         offImage = afwMath.makeStatistics(overscanImage, afwMath.MEAN, statControl).getValue(afwMath.MEAN)
     elif fitType == 'MEDIAN':
         offImage = afwMath.makeStatistics(overscanImage, afwMath.MEDIAN, statControl).getValue(afwMath.MEDIAN)
     elif fitType in ('POLY', 'CHEB', 'LEG', 'NATURAL_SPLINE', 'CUBIC_SPLINE', 'AKIMA_SPLINE'):
         if hasattr(overscanImage, "getImage"):
             biasArray = overscanImage.getImage().getArray()
             biasArray = numpy.ma.masked_where(overscanImage.getMask().getArray() & statControl.getAndMask(),
                                               biasArray)
         else:
             biasArray = overscanImage.getArray()
         # Fit along the long axis, so collapse along each short row and fit the resulting array
         shortInd = numpy.argmin(biasArray.shape)
         if shortInd == 0:
             # Convert to some 'standard' representation to make things easier
             biasArray = numpy.transpose(biasArray)
 
         # Do a single round of clipping to weed out CR hits and signal leaking into the overscan
         percentiles = numpy.percentile(biasArray, [25.0, 50.0, 75.0], axis=1)
         medianBiasArr = percentiles[1]
         stdevBiasArr = 0.74*(percentiles[2] - percentiles[0]) # robust stdev
         diff = numpy.abs(biasArray - medianBiasArr[:,numpy.newaxis])
         biasMaskedArr = numpy.ma.masked_where(diff > collapseRej*stdevBiasArr[:,numpy.newaxis], biasArray)
         collapsed = numpy.mean(biasMaskedArr, axis=1)
         del biasArray, percentiles, stdevBiasArr, diff, biasMaskedArr
 
         if shortInd == 0:
             collapsed = numpy.transpose(collapsed)
 
         num = len(collapsed)
         indices = 2.0*numpy.arange(num)/float(num) - 1.0
 
         if fitType in ('POLY', 'CHEB', 'LEG'):
             # A numpy polynomial
             poly = numpy.polynomial
             fitter, evaler = {"POLY": (poly.polynomial.polyfit, poly.polynomial.polyval),
                               "CHEB": (poly.chebyshev.chebfit, poly.chebyshev.chebval),
                               "LEG":  (poly.legendre.legfit, poly.legendre.legval),
                               }[fitType]
 
             coeffs = fitter(indices, collapsed, order)
             fitBiasArr = evaler(indices, coeffs)
         elif 'SPLINE' in fitType:
             # An afw interpolation
             numBins = order
             #
             # numpy.histogram needs a real array for the mask, but numpy.ma "optimises" the case
             # no-values-are-masked by replacing the mask array by a scalar, numpy.ma.nomask
             #
             # Issue DM-415
             #
             collapsedMask = collapsed.mask
             try:
                 if collapsedMask == numpy.ma.nomask:
                     collapsedMask = numpy.array(len(collapsed)*[numpy.ma.nomask])
             except ValueError:      # If collapsedMask is an array the test fails [needs .all()]
                 pass
 
             numPerBin, binEdges = numpy.histogram(indices, bins=numBins,
                                                   weights=1-collapsedMask.astype(int))
             # Binning is just a histogram, with weights equal to the values.
             # Use a similar trick to get the bin centers (this deals with different numbers per bin).
             values = numpy.histogram(indices, bins=numBins, weights=collapsed)[0]/numPerBin
             binCenters = numpy.histogram(indices, bins=numBins, weights=indices)[0]/numPerBin
             interp = afwMath.makeInterpolate(binCenters.astype(float)[numPerBin > 0],
                                              values.astype(float)[numPerBin > 0],
                                              afwMath.stringToInterpStyle(fitType))
             fitBiasArr = numpy.array([interp.interpolate(i) for i in indices])
 
         import lsstDebug
         if lsstDebug.Info(__name__).display:
             import matplotlib.pyplot as plot
             figure = plot.figure(1)
             figure.clear()
             axes = figure.add_axes((0.1, 0.1, 0.8, 0.8))
             axes.plot(indices, collapsed, 'k+')
             axes.plot(indices, fitBiasArr, 'r-')
             figure.show()
             prompt = "Press Enter or c to continue [chp]... "
             while True:
                 ans = raw_input(prompt).lower()
                 if ans in ("", "c",):
                     break
                 if ans in ("p",):
                     import pdb; pdb.set_trace()
                 elif ans in ("h", ):
                     print "h[elp] c[ontinue] p[db]"
                 figure.close()
 
         offImage = ampImage.Factory(ampImage.getDimensions())
         offArray = offImage.getArray()
         if shortInd == 1:
             offArray[:,:] = fitBiasArr[:,numpy.newaxis]
         else:
             offArray[:,:] = fitBiasArr[numpy.newaxis,:]
 
         # We don't trust any extrapolation: mask those pixels as SUSPECT
         # This will occur when the top and or bottom edges of the overscan
         # contain saturated values. The values will be extrapolated from
         # the surrounding pixels, but we cannot entirely trust the value of
         # the extrapolation, and will mark the image mask plane to flag the
         # image as such.
         mask = ampMaskedImage.getMask()
         maskArray = mask.getArray() if shortInd == 1 else mask.getArray().transpose()
         suspect = mask.getPlaneBitMask("SUSPECT")
         try:
             if collapsed.mask == numpy.ma.nomask:
                 # There is no mask, so the whole array is fine
                 pass
         except ValueError:      # If collapsed.mask is an array the test fails [needs .all()]
             for low in xrange(num):
                 if not collapsed.mask[low]:
                     break
             if low > 0:
                 maskArray[:low,:] |= suspect
             for high in xrange(1, num):
                 if not collapsed.mask[-high]:
                     break
             if high > 1:
                 maskArray[-high:,:] |= suspect
 
     else:
         raise pexExcept.Exception, '%s : %s an invalid overscan type' % \
             ("overscanCorrection", fitType)
     ampImage -= offImage
 

def lsst.ip.isr.isr.saturationCorrection	(	maskedImage,
		saturation,
		fwhm,
		growFootprints = `1`,
		interpolate = `True`,
		maskName = `'SAT'`,
		fallbackValue = `None`
	)

Mark saturated pixels and optionally interpolate over them

@param[in,out] maskedImage  afw.image.MaskedImage to process
@param[in] saturation  saturation level (used as a detection threshold)
@param[in] fwhm  FWHM of double Gaussian smoothing kernel
@param[in] growFootprints  amount by which to grow footprints of detected regions
@param[in] interpolate  interpolate over saturated pixels?
@param[in] maskName  mask plane name
@param[in] fallbackValue  value of last resort for interpolation

Definition at line 186 of file isr.py.

 
                          fallbackValue=None):
     """Mark saturated pixels and optionally interpolate over them
 
     @param[in,out] maskedImage  afw.image.MaskedImage to process
     @param[in] saturation  saturation level (used as a detection threshold)
     @param[in] fwhm  FWHM of double Gaussian smoothing kernel
     @param[in] growFootprints  amount by which to grow footprints of detected regions
     @param[in] interpolate  interpolate over saturated pixels?
     @param[in] maskName  mask plane name
     @param[in] fallbackValue  value of last resort for interpolation
     """
     defectList = makeThresholdMask(
         maskedImage = maskedImage,
         threshold = saturation,
         growFootprints = growFootprints,
         maskName = maskName,
     )
     if interpolate:
         interpolateDefectList(maskedImage, defectList, fwhm, fallbackValue=fallbackValue)

lsst::ip::isr.isr.interpolateDefectList

def interpolateDefectList

Definition: isr.py:67

lsst::ip::isr.isr.makeThresholdMask

def makeThresholdMask

Definition: isr.py:149

def lsst.ip.isr.isr.transposeDefectList ( defectList )

Make a transposed copy of a defect list

@param[in] defectList  defect list
@return meas.algorithms.DefectListT with transposed defects

Definition at line 103 of file isr.py.

 
 def transposeDefectList(defectList):
     """Make a transposed copy of a defect list
 
     @param[in] defectList  defect list
     @return meas.algorithms.DefectListT with transposed defects
     """
     retDefectList = measAlg.DefectListT()
     for defect in defectList:
         bbox = defect.getBBox()
         nbbox = afwGeom.Box2I(afwGeom.Point2I(bbox.getMinY(), bbox.getMinX()), 
              afwGeom.Extent2I(bbox.getDimensions()[1], bbox.getDimensions()[0]))
         retDefectList.push_back(measAlg.Defect(nbbox))
     return retDefectList

lsst.afw.geom::Extent< int, 2 >

lsst::ip::isr.isr.transposeDefectList

def transposeDefectList

Definition: isr.py:103

lsst.afw.geom::Point< int, 2 >

lsst.afw.geom::Box2I

An integer coordinate rectangle.

Definition: Box.h:53

lsst::meas::algorithms::Defect

Encapsulate information about a bad portion of a detector.

Definition: Interp.h:70

def lsst.ip.isr.isr.transposeMaskedImage ( maskedImage )

Make a transposed copy of a masked image

@param[in] maskedImage  afw.image.MaskedImage to process
@return transposed masked image

Definition at line 55 of file isr.py.

 
 def transposeMaskedImage(maskedImage):
     """Make a transposed copy of a masked image
 
     @param[in] maskedImage  afw.image.MaskedImage to process
     @return transposed masked image
     """
     transposed = maskedImage.Factory(afwGeom.Extent2I(maskedImage.getHeight(), maskedImage.getWidth()))
     transposed.getImage().getArray()[:] = maskedImage.getImage().getArray().T
     transposed.getMask().getArray()[:] = maskedImage.getMask().getArray().T
     transposed.getVariance().getArray()[:] = maskedImage.getVariance().getArray().T
     return transposed

lsst.afw.geom::Extent< int, 2 >

lsst::ip::isr.isr.transposeMaskedImage

def transposeMaskedImage

Definition: isr.py:55

def lsst.ip.isr.isr.updateVariance	(	maskedImage,
		gain,
		readNoise
	)

Set the variance plane based on the image plane

@param[in,out] maskedImage  afw.image.MaskedImage; image plane is read and variance plane is written
@param[in] gain  amplifier gain (e-/ADU)
@param[in] readNoise  amplifier read noise (ADU/pixel)

Definition at line 234 of file isr.py.

 
 def updateVariance(maskedImage, gain, readNoise):
     """Set the variance plane based on the image plane
 
     @param[in,out] maskedImage  afw.image.MaskedImage; image plane is read and variance plane is written
     @param[in] gain  amplifier gain (e-/ADU)
     @param[in] readNoise  amplifier read noise (ADU/pixel)
     """
     var = maskedImage.getVariance()
     var <<= maskedImage.getImage()
     var /= gain
     var += readNoise**2

lsst::ip::isr.isr.updateVariance

def updateVariance

Definition: isr.py:234

Functions

Function Documentation

Functions
def	createPsf

def	calcEffectiveGain

def	transposeMaskedImage

def	interpolateDefectList

def	defectListFromFootprintList

def	transposeDefectList

def	maskPixelsFromDefectList

def	getDefectListFromMask

def	makeThresholdMask

def	interpolateFromMask

def	saturationCorrection

def	biasCorrection

def	darkCorrection

def	updateVariance

def	flatCorrection

def	illuminationCorrection

def	overscanCorrection

def lsst.ip.isr.isr.biasCorrection	(	maskedImage,
		biasMaskedImage
	)