lsst.ip.diffim.diffimTools Namespace Reference

Classes
class	NbasisEvaluator

Functions
def	makeFlatNoiseImage (mi, seedStat=afwMath.MAX)
	Add noise. More...
def	makePoissonNoiseImage (im)
def	fakeCoeffs ()
	Make fake images for testing; one is a delta function (or narrow gaussian) and the other is a convolution of this with a spatially varying kernel. More...
def	makeFakeKernelSet (sizeCell=128, nCell=3, deltaFunctionCounts=1.e4, tGaussianWidth=1.0, addNoise=True, bgValue=100., display=False)
def	backgroundSubtract (config, maskedImages)
	Background subtraction for ip_diffim. More...
def	writeKernelCellSet (kernelCellSet, psfMatchingKernel, backgroundModel, outdir)
	More coarse debugging. More...
def	sourceToFootprintList (candidateInList, templateExposure, scienceExposure, kernelSize, config, log)
	Converting types. More...
def	sourceTableToCandidateList (sourceTable, templateExposure, scienceExposure, kConfig, dConfig, log, basisList, doBuild=False)

Function Documentation

backgroundSubtract()

def lsst.ip.diffim.diffimTools.backgroundSubtract	(		config,
			maskedImages
	)

Background subtraction for ip_diffim.

Subtract the background from masked images.

Parameters
----------
config : TODO: DM-17458
    TODO: DM-17458
maskedImages : `list` of `lsst.afw.image.MaskedImage`
    TODO: DM-17458

Returns
-------
TODO: DM-17458
    TODO: DM-17458

Definition at line 293 of file diffimTools.py.

def backgroundSubtract(config, maskedImages):
    """Subtract the background from masked images.
 
    Parameters
    ----------
    config : TODO: DM-17458
        TODO: DM-17458
    maskedImages : `list` of `lsst.afw.image.MaskedImage`
        TODO: DM-17458
 
    Returns
    -------
    TODO: DM-17458
        TODO: DM-17458
    """
    backgrounds = []
    t0 = time.time()
    algorithm = config.algorithm
    binsize = config.binSize
    undersample = config.undersampleStyle
    bctrl = afwMath.BackgroundControl(algorithm)
    bctrl.setUndersampleStyle(undersample)
    for maskedImage in maskedImages:
        bctrl.setNxSample(maskedImage.getWidth()//binsize + 1)
        bctrl.setNySample(maskedImage.getHeight()//binsize + 1)
        image = maskedImage.getImage()
        backobj = afwMath.makeBackground(image, bctrl)
 
        image -= backobj.getImageF()
        backgrounds.append(backobj.getImageF())
        del backobj
 
    t1 = time.time()
    logger = Log.getLogger("lsst.ip.diffim.backgroundSubtract")
    logger.debug("Total time for background subtraction : %.2f s", (t1 - t0))
    return backgrounds
 

fakeCoeffs()

def lsst.ip.diffim.diffimTools.fakeCoeffs

(

)

Make fake images for testing; one is a delta function (or narrow gaussian) and the other is a convolution of this with a spatially varying kernel.

Definition at line 99 of file diffimTools.py.

def fakeCoeffs():
    kCoeffs = ((1.0, 0.0, 0.0),
               (0.005, -0.000001, 0.000001),
               (0.005, 0.000004, 0.000004),
               (-0.001, -0.000030, 0.000030),
               (-0.001, 0.000015, 0.000015),
               (-0.005, -0.000050, 0.000050))
    return kCoeffs
 
 

makeFakeKernelSet()

def lsst.ip.diffim.diffimTools.makeFakeKernelSet	(		sizeCell = 128,
			nCell = 3,
			deltaFunctionCounts = 1.e4,
			tGaussianWidth = 1.0,
			addNoise = True,
			bgValue = 100.,
			display = False
	)

Generate test template and science images with sources.

Parameters
----------
sizeCell : `int`, optional
    Size of the square spatial cells in pixels.
nCell : `int`, optional
    Number of adjacent spatial cells in both direction in both images.
deltaFunctionCounts : `float`, optional
    Flux value for the template image sources.
tGaussianWidth : `float`, optional
    Sigma of the generated Gaussian PSF sources in the template image.
addNoise : `bool`, optional
    If `True`, Poisson noise is added to both the generated template
    and science images.
bgValue : `float`, optional
    Background level to be added to the generated science image.
display : `bool`, optional
    If `True` displays the generated template and science images by
    `lsst.afw.display.Display`.

Notes
-----
- The generated images consist of adjacent ``nCell x nCell`` cells, each
  of pixel size ``sizeCell x sizeCell``.
- The sources in the science image are generated by convolving the
  template by ``sKernel``. ``sKernel`` is a spatial `LinearCombinationKernel`
  of hard wired kernel bases functions. The linear combination has first
  order polynomial spatial dependence with polynomial parameters from ``fakeCoeffs()``.
- The template image sources are generated in the center of each spatial
  cell from one pixel, set to `deltaFunctionCounts` counts, then convolved
  by a 2D Gaussian with sigma of `tGaussianWidth` along each axis.
- The sources are also returned in ``kernelCellSet`` each source is "detected"
  exactly at the center of a cell.

Returns
-------
tMi : `lsst.afw.image.MaskedImage`
    Generated template image.
sMi : `lsst.afw.image.MaskedImage`
    Generated science image.
sKernel : `lsst.afw.math.LinearCombinationKernel`
    The spatial kernel used to generate the sources in the science image.
kernelCellSet : `lsst.afw.math.SpatialCellSet`
    Cell grid of `lsst.afw.math.SpatialCell` instances, containing
    `lsst.ip.diffim.KernelCandidate` instances around all the generated sources
    in the science image.
configFake : `lsst.ip.diffim.ImagePsfMatchConfig`
    Config instance used in the image generation.

Definition at line 109 of file diffimTools.py.

                      addNoise=True, bgValue=100., display=False):
    """Generate test template and science images with sources.
 
    Parameters
    ----------
    sizeCell : `int`, optional
        Size of the square spatial cells in pixels.
    nCell : `int`, optional
        Number of adjacent spatial cells in both direction in both images.
    deltaFunctionCounts : `float`, optional
        Flux value for the template image sources.
    tGaussianWidth : `float`, optional
        Sigma of the generated Gaussian PSF sources in the template image.
    addNoise : `bool`, optional
        If `True`, Poisson noise is added to both the generated template
        and science images.
    bgValue : `float`, optional
        Background level to be added to the generated science image.
    display : `bool`, optional
        If `True` displays the generated template and science images by
        `lsst.afw.display.Display`.
 
    Notes
    -----
    - The generated images consist of adjacent ``nCell x nCell`` cells, each
      of pixel size ``sizeCell x sizeCell``.
    - The sources in the science image are generated by convolving the
      template by ``sKernel``. ``sKernel`` is a spatial `LinearCombinationKernel`
      of hard wired kernel bases functions. The linear combination has first
      order polynomial spatial dependence with polynomial parameters from ``fakeCoeffs()``.
    - The template image sources are generated in the center of each spatial
      cell from one pixel, set to `deltaFunctionCounts` counts, then convolved
      by a 2D Gaussian with sigma of `tGaussianWidth` along each axis.
    - The sources are also returned in ``kernelCellSet`` each source is "detected"
      exactly at the center of a cell.
 
    Returns
    -------
    tMi : `lsst.afw.image.MaskedImage`
        Generated template image.
    sMi : `lsst.afw.image.MaskedImage`
        Generated science image.
    sKernel : `lsst.afw.math.LinearCombinationKernel`
        The spatial kernel used to generate the sources in the science image.
    kernelCellSet : `lsst.afw.math.SpatialCellSet`
        Cell grid of `lsst.afw.math.SpatialCell` instances, containing
        `lsst.ip.diffim.KernelCandidate` instances around all the generated sources
        in the science image.
    configFake : `lsst.ip.diffim.ImagePsfMatchConfig`
        Config instance used in the image generation.
    """
    from . import imagePsfMatch
    configFake = imagePsfMatch.ImagePsfMatchConfig()
    configFake.kernel.name = "AL"
    subconfigFake = configFake.kernel.active
    subconfigFake.alardNGauss = 1
    subconfigFake.alardSigGauss = [2.5, ]
    subconfigFake.alardDegGauss = [2, ]
    subconfigFake.sizeCellX = sizeCell
    subconfigFake.sizeCellY = sizeCell
    subconfigFake.spatialKernelOrder = 1
    subconfigFake.spatialModelType = "polynomial"
    subconfigFake.singleKernelClipping = False   # variance is a hack
    subconfigFake.spatialKernelClipping = False  # variance is a hack
    if bgValue > 0.0:
        subconfigFake.fitForBackground = True
 
    psFake = pexConfig.makePropertySet(subconfigFake)
 
    basisList = makeKernelBasisList(subconfigFake)
    kSize = subconfigFake.kernelSize
 
    # This sets the final extent of each convolved delta function
    gaussKernelWidth = sizeCell//2
 
    # This sets the scale over which pixels are correlated in the
    # spatial convolution; should be at least as big as the kernel you
    # are trying to fit for
    spatialKernelWidth = kSize
 
    # Number of bad pixels due to convolutions
    border = (gaussKernelWidth + spatialKernelWidth)//2
 
    # Make a fake image with a matrix of delta functions
    totalSize = nCell*sizeCell + 2*border
    tim = afwImage.ImageF(geom.Extent2I(totalSize, totalSize))
    for x in range(nCell):
        for y in range(nCell):
            tim[x*sizeCell + sizeCell//2 + border - 1,
                y*sizeCell + sizeCell//2 + border - 1,
                afwImage.LOCAL] = deltaFunctionCounts
 
    # Turn this into stars with a narrow width; conserve counts
    gaussFunction = afwMath.GaussianFunction2D(tGaussianWidth, tGaussianWidth)
    gaussKernel = afwMath.AnalyticKernel(gaussKernelWidth, gaussKernelWidth, gaussFunction)
    cim = afwImage.ImageF(tim.getDimensions())
    convolutionControl = afwMath.ConvolutionControl()
    convolutionControl.setDoNormalize(True)
    afwMath.convolve(cim, tim, gaussKernel, convolutionControl)
    tim = cim
 
    # Trim off border pixels
    bbox = gaussKernel.shrinkBBox(tim.getBBox(afwImage.LOCAL))
    tim = afwImage.ImageF(tim, bbox, afwImage.LOCAL)
 
    # Now make a science image which is this convolved with some
    # spatial function.  Use input basis list.
    polyFunc = afwMath.PolynomialFunction2D(1)
    kCoeffs = fakeCoeffs()
    nToUse = min(len(kCoeffs), len(basisList))
 
    # Make the full convolved science image
    sKernel = afwMath.LinearCombinationKernel(basisList[:nToUse], polyFunc)
    sKernel.setSpatialParameters(kCoeffs[:nToUse])
    sim = afwImage.ImageF(tim.getDimensions())
    convolutionControl = afwMath.ConvolutionControl()
    convolutionControl.setDoNormalize(True)
    afwMath.convolve(sim, tim, sKernel, convolutionControl)
 
    # Get the good subregion
    bbox = sKernel.shrinkBBox(sim.getBBox(afwImage.LOCAL))
 
    # Add background
    sim += bgValue
 
    # Watch out for negative values
    tim += 2*np.abs(np.min(tim.getArray()))
 
    # Add noise?
    if addNoise:
        sim = makePoissonNoiseImage(sim)
        tim = makePoissonNoiseImage(tim)
 
    # And turn into MaskedImages
    sim = afwImage.ImageF(sim, bbox, afwImage.LOCAL)
    svar = afwImage.ImageF(sim, True)
    smask = afwImage.Mask(sim.getDimensions())
    smask.set(0x0)
    sMi = afwImage.MaskedImageF(sim, smask, svar)
 
    tim = afwImage.ImageF(tim, bbox, afwImage.LOCAL)
    tvar = afwImage.ImageF(tim, True)
    tmask = afwImage.Mask(tim.getDimensions())
    tmask.set(0x0)
    tMi = afwImage.MaskedImageF(tim, tmask, tvar)
 
    if display:
        import lsst.afw.display as afwDisplay
        afwDisplay.Display(frame=1).mtv(tMi)
        afwDisplay.Display(frame=2).mtv(sMi)
 
    # Finally, make a kernelSet from these 2 images
    kernelCellSet = afwMath.SpatialCellSet(geom.Box2I(geom.Point2I(0, 0),
                                                      geom.Extent2I(sizeCell*nCell,
                                                                    sizeCell*nCell)),
                                           sizeCell,
                                           sizeCell)
    stampHalfWidth = 2*kSize
    for x in range(nCell):
        for y in range(nCell):
            xCoord = x*sizeCell + sizeCell//2
            yCoord = y*sizeCell + sizeCell//2
            p0 = geom.Point2I(xCoord - stampHalfWidth,
                              yCoord - stampHalfWidth)
            p1 = geom.Point2I(xCoord + stampHalfWidth,
                              yCoord + stampHalfWidth)
            bbox = geom.Box2I(p0, p1)
            tsi = afwImage.MaskedImageF(tMi, bbox, origin=afwImage.LOCAL)
            ssi = afwImage.MaskedImageF(sMi, bbox, origin=afwImage.LOCAL)
 
            kc = diffimLib.makeKernelCandidate(xCoord, yCoord, tsi, ssi, psFake)
            kernelCellSet.insertCandidate(kc)
 
    tMi.setXY0(0, 0)
    sMi.setXY0(0, 0)
    return tMi, sMi, sKernel, kernelCellSet, configFake
 
 

makeFlatNoiseImage()

def lsst.ip.diffim.diffimTools.makeFlatNoiseImage	(		mi,
			seedStat = afwMath.MAX
	)

Add noise.

Definition at line 52 of file diffimTools.py.

def makeFlatNoiseImage(mi, seedStat=afwMath.MAX):
    img = mi.getImage()
    seed = int(10.*afwMath.makeStatistics(mi.getImage(), seedStat).getValue() + 1)
    rdm = afwMath.Random(afwMath.Random.MT19937, seed)
    rdmImage = img.Factory(img.getDimensions())
    afwMath.randomGaussianImage(rdmImage, rdm)
    return rdmImage
 
 

makePoissonNoiseImage()

def lsst.ip.diffim.diffimTools.makePoissonNoiseImage

(

im

)

Return a Poisson noise image based on im

Parameters
----------
im : `lsst.afw.image.Image`
    image; the output image has the same dtype, dimensions, and shape
    and its expectation value is the value of ``im`` at each pixel

Returns
-------
noiseIm : `lsst.afw.image.Image`
    Newly constructed image instance, same type as ``im``.

Notes
-----
- Warning: This uses an undocumented numpy API (the documented API
    uses a single float expectation value instead of an array).

- Uses numpy.random; you may wish to call numpy.random.seed first.

Definition at line 61 of file diffimTools.py.

def makePoissonNoiseImage(im):
    """Return a Poisson noise image based on im
 
    Parameters
    ----------
    im : `lsst.afw.image.Image`
        image; the output image has the same dtype, dimensions, and shape
        and its expectation value is the value of ``im`` at each pixel
 
    Returns
    -------
    noiseIm : `lsst.afw.image.Image`
        Newly constructed image instance, same type as ``im``.
 
    Notes
    -----
    - Warning: This uses an undocumented numpy API (the documented API
        uses a single float expectation value instead of an array).
 
    - Uses numpy.random; you may wish to call numpy.random.seed first.
    """
    import numpy.random as rand
    imArr = im.getArray()
    noiseIm = im.Factory(im.getBBox())
    noiseArr = noiseIm.getArray()
 
    intNoiseArr = rand.poisson(np.where(np.isfinite(imArr), imArr, 0.0))
 
    noiseArr[:, :] = intNoiseArr.astype(noiseArr.dtype)
    return noiseIm
 

sourceTableToCandidateList()

def lsst.ip.diffim.diffimTools.sourceTableToCandidateList	(		sourceTable,
			templateExposure,
			scienceExposure,
			kConfig,
			dConfig,
			log,
			basisList,
			doBuild = False
	)

Convert a list of Sources into KernelCandidates.

The KernelCandidates are used for fitting the Psf-matching kernel.

Parameters
----------
sourceTable : TODO: DM-17458
    TODO: DM-17458
templateExposure : TODO: DM-17458
    TODO: DM-17458
scienceExposure : TODO: DM-17458
    TODO: DM-17458
kConfig : TODO: DM-17458
    TODO: DM-17458
dConfig : TODO: DM-17458
    TODO: DM-17458
log : TODO: DM-17458
    TODO: DM-17458
basisList : TODO: DM-17458
    TODO: DM-17458
doBuild : `bool`, optional
    TODO: DM-17458

Returns
-------
TODO: DM-17458
    TODO: DM-17458

Definition at line 476 of file diffimTools.py.

                               basisList, doBuild=False):
    """Convert a list of Sources into KernelCandidates.
 
    The KernelCandidates are used for fitting the Psf-matching kernel.
 
    Parameters
    ----------
    sourceTable : TODO: DM-17458
        TODO: DM-17458
    templateExposure : TODO: DM-17458
        TODO: DM-17458
    scienceExposure : TODO: DM-17458
        TODO: DM-17458
    kConfig : TODO: DM-17458
        TODO: DM-17458
    dConfig : TODO: DM-17458
        TODO: DM-17458
    log : TODO: DM-17458
        TODO: DM-17458
    basisList : TODO: DM-17458
        TODO: DM-17458
    doBuild : `bool`, optional
        TODO: DM-17458
 
    Returns
    -------
    TODO: DM-17458
        TODO: DM-17458
    """
    kernelSize = basisList[0].getWidth()
    footprintList = sourceToFootprintList(list(sourceTable), templateExposure, scienceExposure,
                                          kernelSize, dConfig, log)
    candList = []
 
    if doBuild and not basisList:
        doBuild = False
    else:
        ps = pexConfig.makePropertySet(kConfig)
        visitor = diffimLib.BuildSingleKernelVisitorF(basisList, ps)
 
    ps = pexConfig.makePropertySet(kConfig)
    for cand in footprintList:
        bbox = cand['footprint'].getBBox()
        tmi = afwImage.MaskedImageF(templateExposure.getMaskedImage(), bbox)
        smi = afwImage.MaskedImageF(scienceExposure.getMaskedImage(), bbox)
        kCand = diffimLib.makeKernelCandidate(cand['source'], tmi, smi, ps)
        if doBuild:
            visitor.processCandidate(kCand)
            kCand.setStatus(afwMath.SpatialCellCandidate.UNKNOWN)
        candList.append(kCand)
    return candList
 
 

sourceToFootprintList()

def lsst.ip.diffim.diffimTools.sourceToFootprintList	(		candidateInList,
			templateExposure,
			scienceExposure,
			kernelSize,
			config,
			log
	)

Converting types.

Convert a list of sources for the PSF-matching Kernel to Footprints.

Parameters
----------
candidateInList : TODO: DM-17458
    Input list of Sources
templateExposure : TODO: DM-17458
    Template image, to be checked for Mask bits in Source Footprint
scienceExposure : TODO: DM-17458
    Science image, to be checked for Mask bits in Source Footprint
kernelSize : TODO: DM-17458
    TODO: DM-17458
config : TODO: DM-17458
    Config that defines the Mask planes that indicate an invalid Source and Bbox grow radius
log : TODO: DM-17458
    Log for output

Returns
-------
candidateOutList : `list`
    a list of dicts having a "source" and "footprint" field, to be used for Psf-matching

Raises
------
RuntimeError
    TODO: DM-17458

Notes
-----
Takes an input list of Sources that were selected to constrain
the Psf-matching Kernel and turns them into a List of Footprints,
which are used to seed a set of KernelCandidates.  The function
checks both the template and science image for masked pixels,
rejecting the Source if certain Mask bits (defined in config) are
set within the Footprint.

Definition at line 376 of file diffimTools.py.

def sourceToFootprintList(candidateInList, templateExposure, scienceExposure, kernelSize, config, log):
    """Convert a list of sources for the PSF-matching Kernel to Footprints.
 
    Parameters
    ----------
    candidateInList : TODO: DM-17458
        Input list of Sources
    templateExposure : TODO: DM-17458
        Template image, to be checked for Mask bits in Source Footprint
    scienceExposure : TODO: DM-17458
        Science image, to be checked for Mask bits in Source Footprint
    kernelSize : TODO: DM-17458
        TODO: DM-17458
    config : TODO: DM-17458
        Config that defines the Mask planes that indicate an invalid Source and Bbox grow radius
    log : TODO: DM-17458
        Log for output
 
    Returns
    -------
    candidateOutList : `list`
        a list of dicts having a "source" and "footprint" field, to be used for Psf-matching
 
    Raises
    ------
    RuntimeError
        TODO: DM-17458
 
    Notes
    -----
    Takes an input list of Sources that were selected to constrain
    the Psf-matching Kernel and turns them into a List of Footprints,
    which are used to seed a set of KernelCandidates.  The function
    checks both the template and science image for masked pixels,
    rejecting the Source if certain Mask bits (defined in config) are
    set within the Footprint.
    """
 
    candidateOutList = []
    fsb = diffimLib.FindSetBitsU()
    badBitMask = 0
    for mp in config.badMaskPlanes:
        badBitMask |= afwImage.Mask.getPlaneBitMask(mp)
    bbox = scienceExposure.getBBox()
 
    # Size to grow Sources
    if config.scaleByFwhm:
        fpGrowPix = int(config.fpGrowKernelScaling*kernelSize + 0.5)
    else:
        fpGrowPix = config.fpGrowPix
    log.info("Growing %d kernel candidate stars by %d pixels", len(candidateInList), fpGrowPix)
 
    for kernelCandidate in candidateInList:
        if not type(kernelCandidate) == afwTable.SourceRecord:
            raise RuntimeError("Candiate not of type afwTable.SourceRecord")
        bm1 = 0
        bm2 = 0
        center = geom.Point2I(scienceExposure.getWcs().skyToPixel(kernelCandidate.getCoord()))
        if center[0] < bbox.getMinX() or center[0] > bbox.getMaxX():
            continue
        if center[1] < bbox.getMinY() or center[1] > bbox.getMaxY():
            continue
 
        xmin = center[0] - fpGrowPix
        xmax = center[0] + fpGrowPix
        ymin = center[1] - fpGrowPix
        ymax = center[1] + fpGrowPix
 
        # Keep object centered
        if (xmin - bbox.getMinX()) < 0:
            xmax += (xmin - bbox.getMinX())
            xmin -= (xmin - bbox.getMinX())
        if (ymin - bbox.getMinY()) < 0:
            ymax += (ymin - bbox.getMinY())
            ymin -= (ymin - bbox.getMinY())
        if (bbox.getMaxX() - xmax) < 0:
            xmin -= (bbox.getMaxX() - xmax)
            xmax += (bbox.getMaxX() - xmax)
        if (bbox.getMaxY() - ymax) < 0:
            ymin -= (bbox.getMaxY() - ymax)
            ymax += (bbox.getMaxY() - ymax)
        if xmin > xmax or ymin > ymax:
            continue
 
        kbbox = geom.Box2I(geom.Point2I(xmin, ymin), geom.Point2I(xmax, ymax))
        try:
            fsb.apply(afwImage.MaskedImageF(templateExposure.getMaskedImage(), kbbox, deep=False).getMask())
            bm1 = fsb.getBits()
            fsb.apply(afwImage.MaskedImageF(scienceExposure.getMaskedImage(), kbbox, deep=False).getMask())
            bm2 = fsb.getBits()
        except Exception:
            pass
        else:
            if not((bm1 & badBitMask) or (bm2 & badBitMask)):
                candidateOutList.append({'source': kernelCandidate,
                                         'footprint': afwDetect.Footprint(afwGeom.SpanSet(kbbox))})
    log.info("Selected %d / %d sources for KernelCandidacy", len(candidateOutList), len(candidateInList))
    return candidateOutList
 
 

writeKernelCellSet()

def lsst.ip.diffim.diffimTools.writeKernelCellSet	(		kernelCellSet,
			psfMatchingKernel,
			backgroundModel,
			outdir
	)

More coarse debugging.

TODO: DM-17458

Parameters
----------
kernelCellSet : TODO: DM-17458
    TODO: DM-17458
psfMatchingKernel : TODO: DM-17458
    TODO: DM-17458
backgroundModel : TODO: DM-17458
    TODO: DM-17458
outdir : TODO: DM-17458
    TODO: DM-17458

Definition at line 335 of file diffimTools.py.

def writeKernelCellSet(kernelCellSet, psfMatchingKernel, backgroundModel, outdir):
    """TODO: DM-17458
 
    Parameters
    ----------
    kernelCellSet : TODO: DM-17458
        TODO: DM-17458
    psfMatchingKernel : TODO: DM-17458
        TODO: DM-17458
    backgroundModel : TODO: DM-17458
        TODO: DM-17458
    outdir : TODO: DM-17458
        TODO: DM-17458
    """
    if not os.path.isdir(outdir):
        os.makedirs(outdir)
 
    for cell in kernelCellSet.getCellList():
        for cand in cell.begin(False):  # False = include bad candidates
            if cand.getStatus() == afwMath.SpatialCellCandidate.GOOD:
                xCand = int(cand.getXCenter())
                yCand = int(cand.getYCenter())
                idCand = cand.getId()
                diffIm = cand.getDifferenceImage(diffimLib.KernelCandidateF.ORIG)
                kernel = cand.getKernelImage(diffimLib.KernelCandidateF.ORIG)
                diffIm.writeFits(os.path.join(outdir, 'diffim_c%d_x%d_y%d.fits' % (idCand, xCand, yCand)))
                kernel.writeFits(os.path.join(outdir, 'kernel_c%d_x%d_y%d.fits' % (idCand, xCand, yCand)))
 
                # Diffim from spatial model
                ski = afwImage.ImageD(kernel.getDimensions())
                psfMatchingKernel.computeImage(ski, False, xCand, yCand)
                sk = afwMath.FixedKernel(ski)
                sbg = backgroundModel(xCand, yCand)
                sdmi = cand.getDifferenceImage(sk, sbg)
                sdmi.writeFits(os.path.join(outdir, 'sdiffim_c%d_x%d_y%d.fits' % (idCand, xCand, yCand)))