lsst.ip.isr.isrFunctions Namespace Reference

Functions
def	createPsf (fwhm)
def	transposeMaskedImage (maskedImage)
def	interpolateDefectList (maskedImage, defectList, fwhm, fallbackValue=None)
def	makeThresholdMask (maskedImage, threshold, growFootprints=1, maskName='SAT')
def	growMasks (mask, radius=0, maskNameList=['BAD'], maskValue="BAD")
def	interpolateFromMask (maskedImage, fwhm, growSaturatedFootprints=1, maskNameList=['SAT'], fallbackValue=None)
def	saturationCorrection (maskedImage, saturation, fwhm, growFootprints=1, interpolate=True, maskName='SAT', fallbackValue=None)
def	trimToMatchCalibBBox (rawMaskedImage, calibMaskedImage)
def	biasCorrection (maskedImage, biasMaskedImage, trimToFit=False)
def	darkCorrection (maskedImage, darkMaskedImage, expScale, darkScale, invert=False, trimToFit=False)
def	updateVariance (maskedImage, gain, readNoise)
def	flatCorrection (maskedImage, flatMaskedImage, scalingType, userScale=1.0, invert=False, trimToFit=False)
def	illuminationCorrection (maskedImage, illumMaskedImage, illumScale, trimToFit=True)
def	overscanCorrection (ampMaskedImage, overscanImage, fitType='MEDIAN', order=1, collapseRej=3.0, statControl=None, overscanIsInt=True)
def	brighterFatterCorrection (exposure, kernel, maxIter, threshold, applyGain, gains=None)
def	gainContext (exp, image, apply, gains=None)
def	attachTransmissionCurve (exposure, opticsTransmission=None, filterTransmission=None, sensorTransmission=None, atmosphereTransmission=None)
def	applyGains (exposure, normalizeGains=False, ptcGains=None)
def	widenSaturationTrails (mask)
def	setBadRegions (exposure, badStatistic="MEDIAN")
def	checkFilter (exposure, filterList, log)
def	getPhysicalFilter (filterLabel, log)

Function Documentation

applyGains()

def lsst.ip.isr.isrFunctions.applyGains	(		exposure,
			normalizeGains = False,
			ptcGains = None
	)

Scale an exposure by the amplifier gains.

Parameters
----------
exposure : `lsst.afw.image.Exposure`
    Exposure to process.  The image is modified.
normalizeGains : `Bool`, optional
    If True, then amplifiers are scaled to force the median of
    each amplifier to equal the median of those medians.
ptcGains : `dict`[`str`], optional
    Dictionary keyed by amp name containing the PTC gains.

Definition at line 746 of file isrFunctions.py.

def applyGains(exposure, normalizeGains=False, ptcGains=None):
    """Scale an exposure by the amplifier gains.
 
    Parameters
    ----------
    exposure : `lsst.afw.image.Exposure`
        Exposure to process.  The image is modified.
    normalizeGains : `Bool`, optional
        If True, then amplifiers are scaled to force the median of
        each amplifier to equal the median of those medians.
    ptcGains : `dict`[`str`], optional
        Dictionary keyed by amp name containing the PTC gains.
    """
    ccd = exposure.getDetector()
    ccdImage = exposure.getMaskedImage()
 
    medians = []
    for amp in ccd:
        sim = ccdImage.Factory(ccdImage, amp.getBBox())
        if ptcGains:
            sim *= ptcGains[amp.getName()]
        else:
            sim *= amp.getGain()
 
        if normalizeGains:
            medians.append(numpy.median(sim.getImage().getArray()))
 
    if normalizeGains:
        median = numpy.median(numpy.array(medians))
        for index, amp in enumerate(ccd):
            sim = ccdImage.Factory(ccdImage, amp.getBBox())
            if medians[index] != 0.0:
                sim *= median/medians[index]
 
 

attachTransmissionCurve()

def lsst.ip.isr.isrFunctions.attachTransmissionCurve	(		exposure,
			opticsTransmission = None,
			filterTransmission = None,
			sensorTransmission = None,
			atmosphereTransmission = None
	)

Attach a TransmissionCurve to an Exposure, given separate curves for
different components.

Parameters
----------
exposure : `lsst.afw.image.Exposure`
    Exposure object to modify by attaching the product of all given
    ``TransmissionCurves`` in post-assembly trimmed detector coordinates.
    Must have a valid ``Detector`` attached that matches the detector
    associated with sensorTransmission.
opticsTransmission : `lsst.afw.image.TransmissionCurve`
    A ``TransmissionCurve`` that represents the throughput of the optics,
    to be evaluated in focal-plane coordinates.
filterTransmission : `lsst.afw.image.TransmissionCurve`
    A ``TransmissionCurve`` that represents the throughput of the filter
    itself, to be evaluated in focal-plane coordinates.
sensorTransmission : `lsst.afw.image.TransmissionCurve`
    A ``TransmissionCurve`` that represents the throughput of the sensor
    itself, to be evaluated in post-assembly trimmed detector coordinates.
atmosphereTransmission : `lsst.afw.image.TransmissionCurve`
    A ``TransmissionCurve`` that represents the throughput of the
    atmosphere, assumed to be spatially constant.

Returns
-------
combined : `lsst.afw.image.TransmissionCurve`
    The TransmissionCurve attached to the exposure.

Notes
-----
All ``TransmissionCurve`` arguments are optional; if none are provided, the
attached ``TransmissionCurve`` will have unit transmission everywhere.

Definition at line 694 of file isrFunctions.py.

                            sensorTransmission=None, atmosphereTransmission=None):
    """Attach a TransmissionCurve to an Exposure, given separate curves for
    different components.
 
    Parameters
    ----------
    exposure : `lsst.afw.image.Exposure`
        Exposure object to modify by attaching the product of all given
        ``TransmissionCurves`` in post-assembly trimmed detector coordinates.
        Must have a valid ``Detector`` attached that matches the detector
        associated with sensorTransmission.
    opticsTransmission : `lsst.afw.image.TransmissionCurve`
        A ``TransmissionCurve`` that represents the throughput of the optics,
        to be evaluated in focal-plane coordinates.
    filterTransmission : `lsst.afw.image.TransmissionCurve`
        A ``TransmissionCurve`` that represents the throughput of the filter
        itself, to be evaluated in focal-plane coordinates.
    sensorTransmission : `lsst.afw.image.TransmissionCurve`
        A ``TransmissionCurve`` that represents the throughput of the sensor
        itself, to be evaluated in post-assembly trimmed detector coordinates.
    atmosphereTransmission : `lsst.afw.image.TransmissionCurve`
        A ``TransmissionCurve`` that represents the throughput of the
        atmosphere, assumed to be spatially constant.
 
    Returns
    -------
    combined : `lsst.afw.image.TransmissionCurve`
        The TransmissionCurve attached to the exposure.
 
    Notes
    -----
    All ``TransmissionCurve`` arguments are optional; if none are provided, the
    attached ``TransmissionCurve`` will have unit transmission everywhere.
    """
    combined = afwImage.TransmissionCurve.makeIdentity()
    if atmosphereTransmission is not None:
        combined *= atmosphereTransmission
    if opticsTransmission is not None:
        combined *= opticsTransmission
    if filterTransmission is not None:
        combined *= filterTransmission
    detector = exposure.getDetector()
    fpToPix = detector.getTransform(fromSys=camGeom.FOCAL_PLANE,
                                    toSys=camGeom.PIXELS)
    combined = combined.transformedBy(fpToPix)
    if sensorTransmission is not None:
        combined *= sensorTransmission
    exposure.getInfo().setTransmissionCurve(combined)
    return combined
 
 

biasCorrection()

def lsst.ip.isr.isrFunctions.biasCorrection	(		maskedImage,
			biasMaskedImage,
			trimToFit = False
	)

Apply bias correction in place.

Parameters
----------
maskedImage : `lsst.afw.image.MaskedImage`
   Image to process.  The image is modified by this method.
biasMaskedImage : `lsst.afw.image.MaskedImage`
    Bias image of the same size as ``maskedImage``
trimToFit : `Bool`, optional
    If True, raw data is symmetrically trimmed to match
    calibration size.

Raises
------
RuntimeError
    Raised if ``maskedImage`` and ``biasMaskedImage`` do not have
    the same size.

Definition at line 270 of file isrFunctions.py.

def biasCorrection(maskedImage, biasMaskedImage, trimToFit=False):
    """Apply bias correction in place.
 
    Parameters
    ----------
    maskedImage : `lsst.afw.image.MaskedImage`
       Image to process.  The image is modified by this method.
    biasMaskedImage : `lsst.afw.image.MaskedImage`
        Bias image of the same size as ``maskedImage``
    trimToFit : `Bool`, optional
        If True, raw data is symmetrically trimmed to match
        calibration size.
 
    Raises
    ------
    RuntimeError
        Raised if ``maskedImage`` and ``biasMaskedImage`` do not have
        the same size.
 
    """
    if trimToFit:
        maskedImage = trimToMatchCalibBBox(maskedImage, biasMaskedImage)
 
    if maskedImage.getBBox(afwImage.LOCAL) != biasMaskedImage.getBBox(afwImage.LOCAL):
        raise RuntimeError("maskedImage bbox %s != biasMaskedImage bbox %s" %
                           (maskedImage.getBBox(afwImage.LOCAL), biasMaskedImage.getBBox(afwImage.LOCAL)))
    maskedImage -= biasMaskedImage
 
 

brighterFatterCorrection()

def lsst.ip.isr.isrFunctions.brighterFatterCorrection	(		exposure,
			kernel,
			maxIter,
			threshold,
			applyGain,
			gains = None
	)

Apply brighter fatter correction in place for the image.

Parameters
----------
exposure : `lsst.afw.image.Exposure`
    Exposure to have brighter-fatter correction applied.  Modified
    by this method.
kernel : `numpy.ndarray`
    Brighter-fatter kernel to apply.
maxIter : scalar
    Number of correction iterations to run.
threshold : scalar
    Convergence threshold in terms of the sum of absolute
    deviations between an iteration and the previous one.
applyGain : `Bool`
    If True, then the exposure values are scaled by the gain prior
    to correction.
gains : `dict` [`str`, `float`]
    A dictionary, keyed by amplifier name, of the gains to use.
    If gains is None, the nominal gains in the amplifier object are used.

Returns
-------
diff : `float`
    Final difference between iterations achieved in correction.
iteration : `int`
    Number of iterations used to calculate correction.

Notes
-----
This correction takes a kernel that has been derived from flat
field images to redistribute the charge.  The gradient of the
kernel is the deflection field due to the accumulated charge.

Given the original image I(x) and the kernel K(x) we can compute
the corrected image Ic(x) using the following equation:

Ic(x) = I(x) + 0.5*d/dx(I(x)*d/dx(int( dy*K(x-y)*I(y))))

To evaluate the derivative term we expand it as follows:

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))) )

Because we use the measured counts instead of the incident counts
we apply the correction iteratively to reconstruct the original
counts and the correction.  We stop iterating when the summed
difference between the current corrected image and the one from
the previous iteration is below the threshold.  We do not require
convergence because the number of iterations is too large a
computational cost.  How we define the threshold still needs to be
evaluated, the current default was shown to work reasonably well
on a small set of images.  For more information on the method see
DocuShare Document-19407.

The edges as defined by the kernel are not corrected because they
have spurious values due to the convolution.

Definition at line 519 of file isrFunctions.py.

def brighterFatterCorrection(exposure, kernel, maxIter, threshold, applyGain, gains=None):
    """Apply brighter fatter correction in place for the image.
 
    Parameters
    ----------
    exposure : `lsst.afw.image.Exposure`
        Exposure to have brighter-fatter correction applied.  Modified
        by this method.
    kernel : `numpy.ndarray`
        Brighter-fatter kernel to apply.
    maxIter : scalar
        Number of correction iterations to run.
    threshold : scalar
        Convergence threshold in terms of the sum of absolute
        deviations between an iteration and the previous one.
    applyGain : `Bool`
        If True, then the exposure values are scaled by the gain prior
        to correction.
    gains : `dict` [`str`, `float`]
        A dictionary, keyed by amplifier name, of the gains to use.
        If gains is None, the nominal gains in the amplifier object are used.
 
    Returns
    -------
    diff : `float`
        Final difference between iterations achieved in correction.
    iteration : `int`
        Number of iterations used to calculate correction.
 
    Notes
    -----
    This correction takes a kernel that has been derived from flat
    field images to redistribute the charge.  The gradient of the
    kernel is the deflection field due to the accumulated charge.
 
    Given the original image I(x) and the kernel K(x) we can compute
    the corrected image Ic(x) using the following equation:
 
    Ic(x) = I(x) + 0.5*d/dx(I(x)*d/dx(int( dy*K(x-y)*I(y))))
 
    To evaluate the derivative term we expand it as follows:
 
    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))) )
 
    Because we use the measured counts instead of the incident counts
    we apply the correction iteratively to reconstruct the original
    counts and the correction.  We stop iterating when the summed
    difference between the current corrected image and the one from
    the previous iteration is below the threshold.  We do not require
    convergence because the number of iterations is too large a
    computational cost.  How we define the threshold still needs to be
    evaluated, the current default was shown to work reasonably well
    on a small set of images.  For more information on the method see
    DocuShare Document-19407.
 
    The edges as defined by the kernel are not corrected because they
    have spurious values due to the convolution.
    """
    image = exposure.getMaskedImage().getImage()
 
    # The image needs to be units of electrons/holes
    with gainContext(exposure, image, applyGain, gains):
 
        kLx = numpy.shape(kernel)[0]
        kLy = numpy.shape(kernel)[1]
        kernelImage = afwImage.ImageD(kLx, kLy)
        kernelImage.getArray()[:, :] = kernel
        tempImage = image.clone()
 
        nanIndex = numpy.isnan(tempImage.getArray())
        tempImage.getArray()[nanIndex] = 0.
 
        outImage = afwImage.ImageF(image.getDimensions())
        corr = numpy.zeros_like(image.getArray())
        prev_image = numpy.zeros_like(image.getArray())
        convCntrl = afwMath.ConvolutionControl(False, True, 1)
        fixedKernel = afwMath.FixedKernel(kernelImage)
 
        # Define boundary by convolution region.  The region that the correction will be
        # calculated for is one fewer in each dimension because of the second derivative terms.
        # NOTE: these need to use integer math, as we're using start:end as numpy index ranges.
        startX = kLx//2
        endX = -kLx//2
        startY = kLy//2
        endY = -kLy//2
 
        for iteration in range(maxIter):
 
            afwMath.convolve(outImage, tempImage, fixedKernel, convCntrl)
            tmpArray = tempImage.getArray()
            outArray = outImage.getArray()
 
            with numpy.errstate(invalid="ignore", over="ignore"):
                # First derivative term
                gradTmp = numpy.gradient(tmpArray[startY:endY, startX:endX])
                gradOut = numpy.gradient(outArray[startY:endY, startX:endX])
                first = (gradTmp[0]*gradOut[0] + gradTmp[1]*gradOut[1])[1:-1, 1:-1]
 
                # Second derivative term
                diffOut20 = numpy.diff(outArray, 2, 0)[startY:endY, startX + 1:endX - 1]
                diffOut21 = numpy.diff(outArray, 2, 1)[startY + 1:endY - 1, startX:endX]
                second = tmpArray[startY + 1:endY - 1, startX + 1:endX - 1]*(diffOut20 + diffOut21)
 
                corr[startY + 1:endY - 1, startX + 1:endX - 1] = 0.5*(first + second)
 
                tmpArray[:, :] = image.getArray()[:, :]
                tmpArray[nanIndex] = 0.
                tmpArray[startY:endY, startX:endX] += corr[startY:endY, startX:endX]
 
            if iteration > 0:
                diff = numpy.sum(numpy.abs(prev_image - tmpArray))
 
                if diff < threshold:
                    break
                prev_image[:, :] = tmpArray[:, :]
 
        image.getArray()[startY + 1:endY - 1, startX + 1:endX - 1] += \
            corr[startY + 1:endY - 1, startX + 1:endX - 1]
 
    return diff, iteration
 
 
@contextmanager

checkFilter()

def lsst.ip.isr.isrFunctions.checkFilter	(		exposure,
			filterList,
			log
	)

Check to see if an exposure is in a filter specified by a list.

The goal of this is to provide a unified filter checking interface
for all filter dependent stages.

Parameters
----------
exposure : `lsst.afw.image.Exposure`
    Exposure to examine.
filterList : `list` [`str`]
    List of physical_filter names to check.
log : `logging.Logger`
    Logger to handle messages.

Returns
-------
result : `bool`
    True if the exposure's filter is contained in the list.

Definition at line 875 of file isrFunctions.py.

def checkFilter(exposure, filterList, log):
    """Check to see if an exposure is in a filter specified by a list.
 
    The goal of this is to provide a unified filter checking interface
    for all filter dependent stages.
 
    Parameters
    ----------
    exposure : `lsst.afw.image.Exposure`
        Exposure to examine.
    filterList : `list` [`str`]
        List of physical_filter names to check.
    log : `logging.Logger`
        Logger to handle messages.
 
    Returns
    -------
    result : `bool`
        True if the exposure's filter is contained in the list.
    """
    thisFilter = exposure.getFilterLabel()
    if thisFilter is None:
        log.warning("No FilterLabel attached to this exposure!")
        return False
 
    thisPhysicalFilter = getPhysicalFilter(thisFilter, log)
    if thisPhysicalFilter in filterList:
        return True
    elif thisFilter.bandLabel in filterList:
        if log:
            log.warning("Physical filter (%s) should be used instead of band %s for filter configurations"
                        " (%s)", thisPhysicalFilter, thisFilter.bandLabel, filterList)
        return True
    else:
        return False
 
 

createPsf()

def lsst.ip.isr.isrFunctions.createPsf

(

fwhm

)

Make a double Gaussian PSF.

Parameters
----------
fwhm : scalar
    FWHM of double Gaussian smoothing kernel.

Returns
-------
psf : `lsst.meas.algorithms.DoubleGaussianPsf`
    The created smoothing kernel.

Definition at line 40 of file isrFunctions.py.

def createPsf(fwhm):
    """Make a double Gaussian PSF.
 
    Parameters
    ----------
    fwhm : scalar
        FWHM of double Gaussian smoothing kernel.
 
    Returns
    -------
    psf : `lsst.meas.algorithms.DoubleGaussianPsf`
        The created smoothing kernel.
    """
    ksize = 4*int(fwhm) + 1
    return measAlg.DoubleGaussianPsf(ksize, ksize, fwhm/(2*math.sqrt(2*math.log(2))))
 
 

darkCorrection()

def lsst.ip.isr.isrFunctions.darkCorrection	(		maskedImage,
			darkMaskedImage,
			expScale,
			darkScale,
			invert = False,
			trimToFit = False
	)

Apply dark correction in place.

Parameters
----------
maskedImage : `lsst.afw.image.MaskedImage`
   Image to process.  The image is modified by this method.
darkMaskedImage : `lsst.afw.image.MaskedImage`
    Dark image of the same size as ``maskedImage``.
expScale : scalar
    Dark exposure time for ``maskedImage``.
darkScale : scalar
    Dark exposure time for ``darkMaskedImage``.
invert : `Bool`, optional
    If True, re-add the dark to an already corrected image.
trimToFit : `Bool`, optional
    If True, raw data is symmetrically trimmed to match
    calibration size.

Raises
------
RuntimeError
    Raised if ``maskedImage`` and ``darkMaskedImage`` do not have
    the same size.

Notes
-----
The dark correction is applied by calculating:
    maskedImage -= dark * expScaling / darkScaling

Definition at line 299 of file isrFunctions.py.

def darkCorrection(maskedImage, darkMaskedImage, expScale, darkScale, invert=False, trimToFit=False):
    """Apply dark correction in place.
 
    Parameters
    ----------
    maskedImage : `lsst.afw.image.MaskedImage`
       Image to process.  The image is modified by this method.
    darkMaskedImage : `lsst.afw.image.MaskedImage`
        Dark image of the same size as ``maskedImage``.
    expScale : scalar
        Dark exposure time for ``maskedImage``.
    darkScale : scalar
        Dark exposure time for ``darkMaskedImage``.
    invert : `Bool`, optional
        If True, re-add the dark to an already corrected image.
    trimToFit : `Bool`, optional
        If True, raw data is symmetrically trimmed to match
        calibration size.
 
    Raises
    ------
    RuntimeError
        Raised if ``maskedImage`` and ``darkMaskedImage`` do not have
        the same size.
 
    Notes
    -----
    The dark correction is applied by calculating:
        maskedImage -= dark * expScaling / darkScaling
    """
    if trimToFit:
        maskedImage = trimToMatchCalibBBox(maskedImage, darkMaskedImage)
 
    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
    if not invert:
        maskedImage.scaledMinus(scale, darkMaskedImage)
    else:
        maskedImage.scaledPlus(scale, darkMaskedImage)
 
 

flatCorrection()

def lsst.ip.isr.isrFunctions.flatCorrection	(		maskedImage,
			flatMaskedImage,
			scalingType,
			userScale = 1.0,
			invert = False,
			trimToFit = False
	)

Apply flat correction in place.

Parameters
----------
maskedImage : `lsst.afw.image.MaskedImage`
    Image to process.  The image is modified.
flatMaskedImage : `lsst.afw.image.MaskedImage`
    Flat image of the same size as ``maskedImage``
scalingType : str
    Flat scale computation method.  Allowed values are 'MEAN',
    'MEDIAN', or 'USER'.
userScale : scalar, optional
    Scale to use if ``scalingType``='USER'.
invert : `Bool`, optional
    If True, unflatten an already flattened image.
trimToFit : `Bool`, optional
    If True, raw data is symmetrically trimmed to match
    calibration size.

Raises
------
RuntimeError
    Raised if ``maskedImage`` and ``flatMaskedImage`` do not have
    the same size or if ``scalingType`` is not an allowed value.

Definition at line 361 of file isrFunctions.py.

def flatCorrection(maskedImage, flatMaskedImage, scalingType, userScale=1.0, invert=False, trimToFit=False):
    """Apply flat correction in place.
 
    Parameters
    ----------
    maskedImage : `lsst.afw.image.MaskedImage`
        Image to process.  The image is modified.
    flatMaskedImage : `lsst.afw.image.MaskedImage`
        Flat image of the same size as ``maskedImage``
    scalingType : str
        Flat scale computation method.  Allowed values are 'MEAN',
        'MEDIAN', or 'USER'.
    userScale : scalar, optional
        Scale to use if ``scalingType``='USER'.
    invert : `Bool`, optional
        If True, unflatten an already flattened image.
    trimToFit : `Bool`, optional
        If True, raw data is symmetrically trimmed to match
        calibration size.
 
    Raises
    ------
    RuntimeError
        Raised if ``maskedImage`` and ``flatMaskedImage`` do not have
        the same size or if ``scalingType`` is not an allowed value.
    """
    if trimToFit:
        maskedImage = trimToMatchCalibBBox(maskedImage, flatMaskedImage)
 
    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 pipeline, but this allows some flexibility
    # in the case that the flat is created by some other mechanism.
    if scalingType in ('MEAN', 'MEDIAN'):
        scalingType = afwMath.stringToStatisticsProperty(scalingType)
        flatScale = afwMath.makeStatistics(flatMaskedImage.image, scalingType).getValue()
    elif scalingType == 'USER':
        flatScale = userScale
    else:
        raise RuntimeError('%s : %s not implemented' % ("flatCorrection", scalingType))
 
    if not invert:
        maskedImage.scaledDivides(1.0/flatScale, flatMaskedImage)
    else:
        maskedImage.scaledMultiplies(1.0/flatScale, flatMaskedImage)
 
 

gainContext()

def lsst.ip.isr.isrFunctions.gainContext	(		exp,
			image,
			apply,
			gains = None
	)

Context manager that applies and removes gain.

Parameters
----------
exp : `lsst.afw.image.Exposure`
    Exposure to apply/remove gain.
image : `lsst.afw.image.Image`
    Image to apply/remove gain.
apply : `Bool`
    If True, apply and remove the amplifier gain.
gains : `dict` [`str`, `float`]
    A dictionary, keyed by amplifier name, of the gains to use.
    If gains is None, the nominal gains in the amplifier object are used.

Yields
------
exp : `lsst.afw.image.Exposure`
    Exposure with the gain applied.

Definition at line 642 of file isrFunctions.py.

def gainContext(exp, image, apply, gains=None):
    """Context manager that applies and removes gain.
 
    Parameters
    ----------
    exp : `lsst.afw.image.Exposure`
        Exposure to apply/remove gain.
    image : `lsst.afw.image.Image`
        Image to apply/remove gain.
    apply : `Bool`
        If True, apply and remove the amplifier gain.
    gains : `dict` [`str`, `float`]
        A dictionary, keyed by amplifier name, of the gains to use.
        If gains is None, the nominal gains in the amplifier object are used.
 
    Yields
    ------
    exp : `lsst.afw.image.Exposure`
        Exposure with the gain applied.
    """
    # check we have all of them if provided because mixing and matching would
    # be a real mess
    if gains and apply is True:
        ampNames = [amp.getName() for amp in exp.getDetector()]
        for ampName in ampNames:
            if ampName not in gains.keys():
                raise RuntimeError(f"Gains provided to gain context, but no entry found for amp {ampName}")
 
    if apply:
        ccd = exp.getDetector()
        for amp in ccd:
            sim = image.Factory(image, amp.getBBox())
            if gains:
                gain = gains[amp.getName()]
            else:
                gain = amp.getGain()
            sim *= gain
 
    try:
        yield exp
    finally:
        if apply:
            ccd = exp.getDetector()
            for amp in ccd:
                sim = image.Factory(image, amp.getBBox())
                if gains:
                    gain = gains[amp.getName()]
                else:
                    gain = amp.getGain()
                sim /= gain
 
 

getPhysicalFilter()

def lsst.ip.isr.isrFunctions.getPhysicalFilter	(		filterLabel,
			log
	)

Get the physical filter label associated with the given filterLabel.

If ``filterLabel`` is `None` or there is no physicalLabel attribute
associated with the given ``filterLabel``, the returned label will be
"Unknown".

Parameters
----------
filterLabel : `lsst.afw.image.FilterLabel`
    The `lsst.afw.image.FilterLabel` object from which to derive the
    physical filter label.
log : `logging.Logger`
    Logger to handle messages.

Returns
-------
physicalFilter : `str`
    The value returned by the physicalLabel attribute of ``filterLabel`` if
    it exists, otherwise set to \"Unknown\".

Definition at line 912 of file isrFunctions.py.

def getPhysicalFilter(filterLabel, log):
    """Get the physical filter label associated with the given filterLabel.
 
    If ``filterLabel`` is `None` or there is no physicalLabel attribute
    associated with the given ``filterLabel``, the returned label will be
    "Unknown".
 
    Parameters
    ----------
    filterLabel : `lsst.afw.image.FilterLabel`
        The `lsst.afw.image.FilterLabel` object from which to derive the
        physical filter label.
    log : `logging.Logger`
        Logger to handle messages.
 
    Returns
    -------
    physicalFilter : `str`
        The value returned by the physicalLabel attribute of ``filterLabel`` if
        it exists, otherwise set to \"Unknown\".
    """
    if filterLabel is None:
        physicalFilter = "Unknown"
        log.warning("filterLabel is None.  Setting physicalFilter to \"Unknown\".")
    else:
        try:
            physicalFilter = filterLabel.physicalLabel
        except RuntimeError:
            log.warning("filterLabel has no physicalLabel attribute.  Setting physicalFilter to \"Unknown\".")
            physicalFilter = "Unknown"
    return physicalFilter

growMasks()

def lsst.ip.isr.isrFunctions.growMasks	(		mask,
			radius = 0,
			maskNameList = ['BAD'],
			maskValue = "BAD"
	)

Grow a mask by an amount and add to the requested plane.

Parameters
----------
mask : `lsst.afw.image.Mask`
    Mask image to process.
radius : scalar
    Amount to grow the mask.
maskNameList : `str` or `list` [`str`]
    Mask names that should be grown.
maskValue : `str`
    Mask plane to assign the newly masked pixels to.

Definition at line 136 of file isrFunctions.py.

def growMasks(mask, radius=0, maskNameList=['BAD'], maskValue="BAD"):
    """Grow a mask by an amount and add to the requested plane.
 
    Parameters
    ----------
    mask : `lsst.afw.image.Mask`
        Mask image to process.
    radius : scalar
        Amount to grow the mask.
    maskNameList : `str` or `list` [`str`]
        Mask names that should be grown.
    maskValue : `str`
        Mask plane to assign the newly masked pixels to.
    """
    if radius > 0:
        thresh = afwDetection.Threshold(mask.getPlaneBitMask(maskNameList), afwDetection.Threshold.BITMASK)
        fpSet = afwDetection.FootprintSet(mask, thresh)
        fpSet = afwDetection.FootprintSet(fpSet, rGrow=radius, isotropic=False)
        fpSet.setMask(mask, maskValue)
 
 

illuminationCorrection()

def lsst.ip.isr.isrFunctions.illuminationCorrection	(		maskedImage,
			illumMaskedImage,
			illumScale,
			trimToFit = True
	)

Apply illumination correction in place.

Parameters
----------
maskedImage : `lsst.afw.image.MaskedImage`
    Image to process.  The image is modified.
illumMaskedImage : `lsst.afw.image.MaskedImage`
    Illumination correction image of the same size as ``maskedImage``.
illumScale : scalar
    Scale factor for the illumination correction.
trimToFit : `Bool`, optional
    If True, raw data is symmetrically trimmed to match
    calibration size.

Raises
------
RuntimeError
    Raised if ``maskedImage`` and ``illumMaskedImage`` do not have
    the same size.

Definition at line 411 of file isrFunctions.py.

def illuminationCorrection(maskedImage, illumMaskedImage, illumScale, trimToFit=True):
    """Apply illumination correction in place.
 
    Parameters
    ----------
    maskedImage : `lsst.afw.image.MaskedImage`
        Image to process.  The image is modified.
    illumMaskedImage : `lsst.afw.image.MaskedImage`
        Illumination correction image of the same size as ``maskedImage``.
    illumScale : scalar
        Scale factor for the illumination correction.
    trimToFit : `Bool`, optional
        If True, raw data is symmetrically trimmed to match
        calibration size.
 
    Raises
    ------
    RuntimeError
        Raised if ``maskedImage`` and ``illumMaskedImage`` do not have
        the same size.
    """
    if trimToFit:
        maskedImage = trimToMatchCalibBBox(maskedImage, illumMaskedImage)
 
    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(1.0/illumScale, illumMaskedImage)
 
 

interpolateDefectList()

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

Interpolate over defects specified in a defect list.

Parameters
----------
maskedImage : `lsst.afw.image.MaskedImage`
    Image to process.
defectList : `lsst.meas.algorithms.Defects`
    List of defects to interpolate over.
fwhm : scalar
    FWHM of double Gaussian smoothing kernel.
fallbackValue : scalar, optional
    Fallback value if an interpolated value cannot be determined.
    If None, then the clipped mean of the image is used.

Definition at line 77 of file isrFunctions.py.

def interpolateDefectList(maskedImage, defectList, fwhm, fallbackValue=None):
    """Interpolate over defects specified in a defect list.
 
    Parameters
    ----------
    maskedImage : `lsst.afw.image.MaskedImage`
        Image to process.
    defectList : `lsst.meas.algorithms.Defects`
        List of defects to interpolate over.
    fwhm : scalar
        FWHM of double Gaussian smoothing kernel.
    fallbackValue : scalar, optional
        Fallback value if an interpolated value cannot be determined.
        If None, then the clipped mean of the image is used.
    """
    psf = createPsf(fwhm)
    if fallbackValue is None:
        fallbackValue = afwMath.makeStatistics(maskedImage.getImage(), afwMath.MEANCLIP).getValue()
    if 'INTRP' not in maskedImage.getMask().getMaskPlaneDict():
        maskedImage.getMask().addMaskPlane('INTRP')
    measAlg.interpolateOverDefects(maskedImage, psf, defectList, fallbackValue, True)
    return maskedImage
 
 

interpolateFromMask()

def lsst.ip.isr.isrFunctions.interpolateFromMask	(		maskedImage,
			fwhm,
			growSaturatedFootprints = 1,
			maskNameList = ['SAT'],
			fallbackValue = None
	)

Interpolate over defects identified by a particular set of mask planes.

Parameters
----------
maskedImage : `lsst.afw.image.MaskedImage`
    Image to process.
fwhm : scalar
    FWHM of double Gaussian smoothing kernel.
growSaturatedFootprints : scalar, optional
    Number of pixels to grow footprints for saturated pixels.
maskNameList : `List` of `str`, optional
    Mask plane name.
fallbackValue : scalar, optional
    Value of last resort for interpolation.

Definition at line 157 of file isrFunctions.py.

                        maskNameList=['SAT'], fallbackValue=None):
    """Interpolate over defects identified by a particular set of mask planes.
 
    Parameters
    ----------
    maskedImage : `lsst.afw.image.MaskedImage`
        Image to process.
    fwhm : scalar
        FWHM of double Gaussian smoothing kernel.
    growSaturatedFootprints : scalar, optional
        Number of pixels to grow footprints for saturated pixels.
    maskNameList : `List` of `str`, optional
        Mask plane name.
    fallbackValue : scalar, optional
        Value of last resort for interpolation.
    """
    mask = maskedImage.getMask()
 
    if growSaturatedFootprints > 0 and "SAT" in maskNameList:
        # If we are interpolating over an area larger than the original masked region, we need
        # to expand the original mask bit to the full area to explain why we interpolated there.
        growMasks(mask, radius=growSaturatedFootprints, maskNameList=['SAT'], maskValue="SAT")
 
    thresh = afwDetection.Threshold(mask.getPlaneBitMask(maskNameList), afwDetection.Threshold.BITMASK)
    fpSet = afwDetection.FootprintSet(mask, thresh)
    defectList = Defects.fromFootprintList(fpSet.getFootprints())
 
    interpolateDefectList(maskedImage, defectList, fwhm, fallbackValue=fallbackValue)
 
    return maskedImage
 
 

makeThresholdMask()

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

Mask pixels based on threshold detection.

Parameters
----------
maskedImage : `lsst.afw.image.MaskedImage`
    Image to process.  Only the mask plane is updated.
threshold : scalar
    Detection threshold.
growFootprints : scalar, optional
    Number of pixels to grow footprints of detected regions.
maskName : str, optional
    Mask plane name, or list of names to convert

Returns
-------
defectList : `lsst.meas.algorithms.Defects`
    Defect list constructed from pixels above the threshold.

Definition at line 101 of file isrFunctions.py.

def makeThresholdMask(maskedImage, threshold, growFootprints=1, maskName='SAT'):
    """Mask pixels based on threshold detection.
 
    Parameters
    ----------
    maskedImage : `lsst.afw.image.MaskedImage`
        Image to process.  Only the mask plane is updated.
    threshold : scalar
        Detection threshold.
    growFootprints : scalar, optional
        Number of pixels to grow footprints of detected regions.
    maskName : str, optional
        Mask plane name, or list of names to convert
 
    Returns
    -------
    defectList : `lsst.meas.algorithms.Defects`
        Defect list constructed from pixels above the threshold.
    """
    # find saturated regions
    thresh = afwDetection.Threshold(threshold)
    fs = afwDetection.FootprintSet(maskedImage, thresh)
 
    if growFootprints > 0:
        fs = afwDetection.FootprintSet(fs, rGrow=growFootprints, isotropic=False)
    fpList = fs.getFootprints()
 
    # set mask
    mask = maskedImage.getMask()
    bitmask = mask.getPlaneBitMask(maskName)
    afwDetection.setMaskFromFootprintList(mask, fpList, bitmask)
 
    return Defects.fromFootprintList(fpList)
 
 

overscanCorrection()

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

Apply overscan correction in place.

Parameters
----------
ampMaskedImage : `lsst.afw.image.MaskedImage`
    Image of amplifier to correct; modified.
overscanImage : `lsst.afw.image.Image` or `lsst.afw.image.MaskedImage`
    Image of overscan; modified.
fitType : `str`
    Type of fit for overscan correction. May be one of:

    - ``MEAN``: use mean of overscan.
    - ``MEANCLIP``: use clipped mean of overscan.
    - ``MEDIAN``: use median of overscan.
    - ``MEDIAN_PER_ROW``: use median per row of overscan.
    - ``POLY``: fit with ordinary polynomial.
    - ``CHEB``: fit with Chebyshev polynomial.
    - ``LEG``: fit with Legendre polynomial.
    - ``NATURAL_SPLINE``: fit with natural spline.
    - ``CUBIC_SPLINE``: fit with cubic spline.
    - ``AKIMA_SPLINE``: fit with Akima spline.

order : `int`
    Polynomial order or number of spline knots; ignored unless
    ``fitType`` indicates a polynomial or spline.
statControl : `lsst.afw.math.StatisticsControl`
    Statistics control object.  In particular, we pay attention to numSigmaClip
overscanIsInt : `bool`
    Treat the overscan region as consisting of integers, even if it's been
    converted to float.  E.g. handle ties properly.

Returns
-------
result : `lsst.pipe.base.Struct`
    Result struct with components:

    - ``imageFit``: Value(s) removed from image (scalar or
        `lsst.afw.image.Image`)
    - ``overscanFit``: Value(s) removed from overscan (scalar or
        `lsst.afw.image.Image`)
    - ``overscanImage``: Overscan corrected overscan region
        (`lsst.afw.image.Image`)
Raises
------
RuntimeError
    Raised if ``fitType`` is not an allowed value.

Notes
-----
The ``ampMaskedImage`` and ``overscanImage`` are modified, with the fit
subtracted. Note that the ``overscanImage`` should not be a subimage of
the ``ampMaskedImage``, to avoid being subtracted twice.

Debug plots are available for the SPLINE fitTypes by setting the
`debug.display` for `name` == "lsst.ip.isr.isrFunctions".  These
plots show the scatter plot of the overscan data (collapsed along
the perpendicular dimension) as a function of position on the CCD
(normalized between +/-1).

Definition at line 442 of file isrFunctions.py.

                       statControl=None, overscanIsInt=True):
    """Apply overscan correction in place.
 
    Parameters
    ----------
    ampMaskedImage : `lsst.afw.image.MaskedImage`
        Image of amplifier to correct; modified.
    overscanImage : `lsst.afw.image.Image` or `lsst.afw.image.MaskedImage`
        Image of overscan; modified.
    fitType : `str`
        Type of fit for overscan correction. May be one of:
 
        - ``MEAN``: use mean of overscan.
        - ``MEANCLIP``: use clipped mean of overscan.
        - ``MEDIAN``: use median of overscan.
        - ``MEDIAN_PER_ROW``: use median per row of overscan.
        - ``POLY``: fit with ordinary polynomial.
        - ``CHEB``: fit with Chebyshev polynomial.
        - ``LEG``: fit with Legendre polynomial.
        - ``NATURAL_SPLINE``: fit with natural spline.
        - ``CUBIC_SPLINE``: fit with cubic spline.
        - ``AKIMA_SPLINE``: fit with Akima spline.
 
    order : `int`
        Polynomial order or number of spline knots; ignored unless
        ``fitType`` indicates a polynomial or spline.
    statControl : `lsst.afw.math.StatisticsControl`
        Statistics control object.  In particular, we pay attention to numSigmaClip
    overscanIsInt : `bool`
        Treat the overscan region as consisting of integers, even if it's been
        converted to float.  E.g. handle ties properly.
 
    Returns
    -------
    result : `lsst.pipe.base.Struct`
        Result struct with components:
 
        - ``imageFit``: Value(s) removed from image (scalar or
            `lsst.afw.image.Image`)
        - ``overscanFit``: Value(s) removed from overscan (scalar or
            `lsst.afw.image.Image`)
        - ``overscanImage``: Overscan corrected overscan region
            (`lsst.afw.image.Image`)
    Raises
    ------
    RuntimeError
        Raised if ``fitType`` is not an allowed value.
 
    Notes
    -----
    The ``ampMaskedImage`` and ``overscanImage`` are modified, with the fit
    subtracted. Note that the ``overscanImage`` should not be a subimage of
    the ``ampMaskedImage``, to avoid being subtracted twice.
 
    Debug plots are available for the SPLINE fitTypes by setting the
    `debug.display` for `name` == "lsst.ip.isr.isrFunctions".  These
    plots show the scatter plot of the overscan data (collapsed along
    the perpendicular dimension) as a function of position on the CCD
    (normalized between +/-1).
    """
    ampImage = ampMaskedImage.getImage()
 
    config = OverscanCorrectionTaskConfig()
    if fitType:
        config.fitType = fitType
    if order:
        config.order = order
    if collapseRej:
        config.numSigmaClip = collapseRej
    if overscanIsInt:
        config.overscanIsInt = True
 
    overscanTask = OverscanCorrectionTask(config=config)
    return overscanTask.run(ampImage, overscanImage)
 
 

saturationCorrection()

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

Mark saturated pixels and optionally interpolate over them

Parameters
----------
maskedImage : `lsst.afw.image.MaskedImage`
    Image to process.
saturation  : scalar
    Saturation level used as the detection threshold.
fwhm : scalar
    FWHM of double Gaussian smoothing kernel.
growFootprints : scalar, optional
    Number of pixels to grow footprints of detected regions.
interpolate : Bool, optional
    If True, saturated pixels are interpolated over.
maskName : str, optional
    Mask plane name.
fallbackValue : scalar, optional
    Value of last resort for interpolation.

Definition at line 190 of file isrFunctions.py.

                         fallbackValue=None):
    """Mark saturated pixels and optionally interpolate over them
 
    Parameters
    ----------
    maskedImage : `lsst.afw.image.MaskedImage`
        Image to process.
    saturation  : scalar
        Saturation level used as the detection threshold.
    fwhm : scalar
        FWHM of double Gaussian smoothing kernel.
    growFootprints : scalar, optional
        Number of pixels to grow footprints of detected regions.
    interpolate : Bool, optional
        If True, saturated pixels are interpolated over.
    maskName : str, optional
        Mask plane name.
    fallbackValue : scalar, optional
        Value of last resort for interpolation.
    """
    defectList = makeThresholdMask(
        maskedImage=maskedImage,
        threshold=saturation,
        growFootprints=growFootprints,
        maskName=maskName,
    )
    if interpolate:
        interpolateDefectList(maskedImage, defectList, fwhm, fallbackValue=fallbackValue)
 
    return maskedImage
 
 

setBadRegions()

def lsst.ip.isr.isrFunctions.setBadRegions	(		exposure,
			badStatistic = "MEDIAN"
	)

Set all BAD areas of the chip to the average of the rest of the exposure

Parameters
----------
exposure : `lsst.afw.image.Exposure`
    Exposure to mask.  The exposure mask is modified.
badStatistic : `str`, optional
    Statistic to use to generate the replacement value from the
    image data.  Allowed values are 'MEDIAN' or 'MEANCLIP'.

Returns
-------
badPixelCount : scalar
    Number of bad pixels masked.
badPixelValue : scalar
    Value substituted for bad pixels.

Raises
------
RuntimeError
    Raised if `badStatistic` is not an allowed value.

Definition at line 828 of file isrFunctions.py.

def setBadRegions(exposure, badStatistic="MEDIAN"):
    """Set all BAD areas of the chip to the average of the rest of the exposure
 
    Parameters
    ----------
    exposure : `lsst.afw.image.Exposure`
        Exposure to mask.  The exposure mask is modified.
    badStatistic : `str`, optional
        Statistic to use to generate the replacement value from the
        image data.  Allowed values are 'MEDIAN' or 'MEANCLIP'.
 
    Returns
    -------
    badPixelCount : scalar
        Number of bad pixels masked.
    badPixelValue : scalar
        Value substituted for bad pixels.
 
    Raises
    ------
    RuntimeError
        Raised if `badStatistic` is not an allowed value.
    """
    if badStatistic == "MEDIAN":
        statistic = afwMath.MEDIAN
    elif badStatistic == "MEANCLIP":
        statistic = afwMath.MEANCLIP
    else:
        raise RuntimeError("Impossible method %s of bad region correction" % badStatistic)
 
    mi = exposure.getMaskedImage()
    mask = mi.getMask()
    BAD = mask.getPlaneBitMask("BAD")
    INTRP = mask.getPlaneBitMask("INTRP")
 
    sctrl = afwMath.StatisticsControl()
    sctrl.setAndMask(BAD)
    value = afwMath.makeStatistics(mi, statistic, sctrl).getValue()
 
    maskArray = mask.getArray()
    imageArray = mi.getImage().getArray()
    badPixels = numpy.logical_and((maskArray & BAD) > 0, (maskArray & INTRP) == 0)
    imageArray[:] = numpy.where(badPixels, value, imageArray)
 
    return badPixels.sum(), value
 
 

transposeMaskedImage()

def lsst.ip.isr.isrFunctions.transposeMaskedImage

(

maskedImage

)

Make a transposed copy of a masked image.

Parameters
----------
maskedImage : `lsst.afw.image.MaskedImage`
    Image to process.

Returns
-------
transposed : `lsst.afw.image.MaskedImage`
    The transposed copy of the input image.

Definition at line 57 of file isrFunctions.py.

def transposeMaskedImage(maskedImage):
    """Make a transposed copy of a masked image.
 
    Parameters
    ----------
    maskedImage : `lsst.afw.image.MaskedImage`
        Image to process.
 
    Returns
    -------
    transposed : `lsst.afw.image.MaskedImage`
        The transposed copy of the input image.
    """
    transposed = maskedImage.Factory(lsst.geom.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
 
 

trimToMatchCalibBBox()

def lsst.ip.isr.isrFunctions.trimToMatchCalibBBox	(		rawMaskedImage,
			calibMaskedImage
	)

Compute number of edge trim pixels to match the calibration data.

Use the dimension difference between the raw exposure and the
calibration exposure to compute the edge trim pixels.  This trim
is applied symmetrically, with the same number of pixels masked on
each side.

Parameters
----------
rawMaskedImage : `lsst.afw.image.MaskedImage`
    Image to trim.
calibMaskedImage : `lsst.afw.image.MaskedImage`
    Calibration image to draw new bounding box from.

Returns
-------
replacementMaskedImage : `lsst.afw.image.MaskedImage`
    ``rawMaskedImage`` trimmed to the appropriate size
Raises
------
RuntimeError
   Rasied if ``rawMaskedImage`` cannot be symmetrically trimmed to
   match ``calibMaskedImage``.

Definition at line 223 of file isrFunctions.py.

def trimToMatchCalibBBox(rawMaskedImage, calibMaskedImage):
    """Compute number of edge trim pixels to match the calibration data.
 
    Use the dimension difference between the raw exposure and the
    calibration exposure to compute the edge trim pixels.  This trim
    is applied symmetrically, with the same number of pixels masked on
    each side.
 
    Parameters
    ----------
    rawMaskedImage : `lsst.afw.image.MaskedImage`
        Image to trim.
    calibMaskedImage : `lsst.afw.image.MaskedImage`
        Calibration image to draw new bounding box from.
 
    Returns
    -------
    replacementMaskedImage : `lsst.afw.image.MaskedImage`
        ``rawMaskedImage`` trimmed to the appropriate size
    Raises
    ------
    RuntimeError
       Rasied if ``rawMaskedImage`` cannot be symmetrically trimmed to
       match ``calibMaskedImage``.
    """
    nx, ny = rawMaskedImage.getBBox().getDimensions() - calibMaskedImage.getBBox().getDimensions()
    if nx != ny:
        raise RuntimeError("Raw and calib maskedImages are trimmed differently in X and Y.")
    if nx % 2 != 0:
        raise RuntimeError("Calibration maskedImage is trimmed unevenly in X.")
    if nx < 0:
        raise RuntimeError("Calibration maskedImage is larger than raw data.")
 
    nEdge = nx//2
    if nEdge > 0:
        replacementMaskedImage = rawMaskedImage[nEdge:-nEdge, nEdge:-nEdge, afwImage.LOCAL]
        SourceDetectionTask.setEdgeBits(
            rawMaskedImage,
            replacementMaskedImage.getBBox(),
            rawMaskedImage.getMask().getPlaneBitMask("EDGE")
        )
    else:
        replacementMaskedImage = rawMaskedImage
 
    return replacementMaskedImage
 
 

updateVariance()

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

Set the variance plane based on the image plane.

Parameters
----------
maskedImage : `lsst.afw.image.MaskedImage`
    Image to process.  The variance plane is modified.
gain : scalar
    The amplifier gain in electrons/ADU.
readNoise : scalar
    The amplifier read nmoise in ADU/pixel.

Definition at line 343 of file isrFunctions.py.

def updateVariance(maskedImage, gain, readNoise):
    """Set the variance plane based on the image plane.
 
    Parameters
    ----------
    maskedImage : `lsst.afw.image.MaskedImage`
        Image to process.  The variance plane is modified.
    gain : scalar
        The amplifier gain in electrons/ADU.
    readNoise : scalar
        The amplifier read nmoise in ADU/pixel.
    """
    var = maskedImage.getVariance()
    var[:] = maskedImage.getImage()
    var /= gain
    var += readNoise**2
 
 

widenSaturationTrails()

def lsst.ip.isr.isrFunctions.widenSaturationTrails

(

mask

)

Grow the saturation trails by an amount dependent on the width of the trail.

Parameters
----------
mask : `lsst.afw.image.Mask`
    Mask which will have the saturated areas grown.

Definition at line 781 of file isrFunctions.py.

def widenSaturationTrails(mask):
    """Grow the saturation trails by an amount dependent on the width of the trail.
 
    Parameters
    ----------
    mask : `lsst.afw.image.Mask`
        Mask which will have the saturated areas grown.
    """
 
    extraGrowDict = {}
    for i in range(1, 6):
        extraGrowDict[i] = 0
    for i in range(6, 8):
        extraGrowDict[i] = 1
    for i in range(8, 10):
        extraGrowDict[i] = 3
    extraGrowMax = 4
 
    if extraGrowMax <= 0:
        return
 
    saturatedBit = mask.getPlaneBitMask("SAT")
 
    xmin, ymin = mask.getBBox().getMin()
    width = mask.getWidth()
 
    thresh = afwDetection.Threshold(saturatedBit, afwDetection.Threshold.BITMASK)
    fpList = afwDetection.FootprintSet(mask, thresh).getFootprints()
 
    for fp in fpList:
        for s in fp.getSpans():
            x0, x1 = s.getX0(), s.getX1()
 
            extraGrow = extraGrowDict.get(x1 - x0 + 1, extraGrowMax)
            if extraGrow > 0:
                y = s.getY() - ymin
                x0 -= xmin + extraGrow
                x1 -= xmin - extraGrow
 
                if x0 < 0:
                    x0 = 0
                if x1 >= width - 1:
                    x1 = width - 1
 
                mask.array[y, x0:x1+1] |= saturatedBit