コード例 #1
0
def plotPsfSpatialModel(exposure,
                        psf,
                        psfCellSet,
                        showBadCandidates=True,
                        numSample=128,
                        matchKernelAmplitudes=False,
                        keepPlots=True):
    """Plot the PSF spatial model."""

    if plt is None:
        print >> sys.stderr, "Unable to import matplotlib"
        return

    noSpatialKernel = afwMath.cast_LinearCombinationKernel(psf.getKernel())
    candPos = list()
    candFits = list()
    badPos = list()
    badFits = list()
    candAmps = list()
    badAmps = list()
    for cell in psfCellSet.getCellList():
        for cand in cell.begin(False):
            cand = algorithmsLib.cast_PsfCandidateF(cand)
            if not showBadCandidates and cand.isBad():
                continue
            candCenter = afwGeom.PointD(cand.getXCenter(), cand.getYCenter())
            try:
                im = cand.getMaskedImage()
            except Exception, e:
                continue

            fit = algorithmsLib.fitKernelParamsToImage(noSpatialKernel, im,
                                                       candCenter)
            params = fit[0]
            kernels = fit[1]
            amp = 0.0
            for p, k in zip(params, kernels):
                amp += p * afwMath.cast_FixedKernel(k).getSum()

            targetFits = badFits if cand.isBad() else candFits
            targetPos = badPos if cand.isBad() else candPos
            targetAmps = badAmps if cand.isBad() else candAmps

            targetFits.append([x / amp for x in params])
            targetPos.append(candCenter)
            targetAmps.append(amp)
コード例 #2
0
ファイル: utils.py プロジェクト: laurenam/meas_algorithms
def plotPsfSpatialModel(exposure, psf, psfCellSet, showBadCandidates=True, numSample=128,
                        matchKernelAmplitudes=False, keepPlots=True):
    """Plot the PSF spatial model."""

    if plt is None:
        print >> sys.stderr, "Unable to import matplotlib"
        return
    
    noSpatialKernel = afwMath.cast_LinearCombinationKernel(psf.getKernel())
    candPos = list()
    candFits = list()
    badPos = list()
    badFits = list()
    candAmps = list()
    badAmps = list()
    for cell in psfCellSet.getCellList():
        for cand in cell.begin(False):
            cand = algorithmsLib.cast_PsfCandidateF(cand)
            if not showBadCandidates and cand.isBad():
                continue
            candCenter = afwGeom.PointD(cand.getXCenter(), cand.getYCenter())
            try:
                im = cand.getMaskedImage()
            except Exception, e:
                continue

            fit = algorithmsLib.fitKernelParamsToImage(noSpatialKernel, im, candCenter)
            params = fit[0]
            kernels = fit[1]
            amp = 0.0
            for p, k in zip(params, kernels):
                amp += p * afwMath.cast_FixedKernel(k).getSum()

            targetFits = badFits if cand.isBad() else candFits
            targetPos = badPos if cand.isBad() else candPos
            targetAmps = badAmps if cand.isBad() else candAmps

            targetFits.append([x / amp for x in params])
            targetPos.append(candCenter)
            targetAmps.append(amp)
コード例 #3
0
ファイル: utils.py プロジェクト: laurenam/meas_algorithms
def showPsfCandidates(exposure, psfCellSet, psf=None, frame=None, normalize=True, showBadCandidates=True,
                      fitBasisComponents=False, variance=None, chi=None):
    """Display the PSF candidates.
If psf is provided include PSF model and residuals;  if normalize is true normalize the PSFs (and residuals)

If chi is True, generate a plot of residuals/sqrt(variance), i.e. chi

If fitBasisComponents is true, also find the best linear combination of the PSF's components (if they exist)
"""
    if chi is None:
        if variance is not None:        # old name for chi
            chi = variance
    #
    # Show us the ccandidates
    #
    mos = displayUtils.Mosaic()
    #
    candidateCenters = []
    candidateCentersBad = []
    candidateIndex = 0

    for cell in psfCellSet.getCellList():
        for cand in cell.begin(False): # include bad candidates
            cand = algorithmsLib.cast_PsfCandidateF(cand)

            rchi2 = cand.getChi2()
            if rchi2 > 1e100:
                rchi2 = numpy.nan

            if not showBadCandidates and cand.isBad():
                continue

            if psf:
                im_resid = displayUtils.Mosaic(gutter=0, background=-5, mode="x")

                try:
                    im = cand.getMaskedImage() # copy of this object's image
                    xc, yc = cand.getXCenter(), cand.getYCenter()

                    margin = 0 if True else 5
                    w, h = im.getDimensions()
                    bbox = afwGeom.BoxI(afwGeom.PointI(margin, margin), im.getDimensions())

                    if margin > 0:
                        bim = im.Factory(w + 2*margin, h + 2*margin)

                        stdev = numpy.sqrt(afwMath.makeStatistics(im.getVariance(), afwMath.MEAN).getValue())
                        afwMath.randomGaussianImage(bim.getImage(), afwMath.Random())
                        bim *= stdev
                        var = bim.getVariance(); var.set(stdev**2); del var

                        sbim = im.Factory(bim, bbox)
                        sbim <<= im
                        del sbim
                        im = bim
                        xc += margin; yc += margin

                    im = im.Factory(im, True)
                    im.setXY0(cand.getMaskedImage().getXY0())
                except:
                    continue

                if not variance:
                    im_resid.append(im.Factory(im, True))

                if True:                # tweak up centroids
                    mi = im
                    psfIm = mi.getImage()

                    config = measAlg.SourceMeasurementConfig()
                    config.centroider.name = "centroid.sdss"
                    config.slots.centroid = config.centroider.name

                    schema = afwTable.SourceTable.makeMinimalSchema()
                    measureSources = config.makeMeasureSources(schema)
                    catalog = afwTable.SourceCatalog(schema)
                    config.slots.setupTable(catalog.table)

                    extra = 10          # enough margin to run the sdss centroider
                    miBig = mi.Factory(im.getWidth() + 2*extra, im.getHeight() + 2*extra)
                    miBig[extra:-extra, extra:-extra] = mi
                    miBig.setXY0(mi.getX0() - extra, mi.getY0() - extra)
                    mi = miBig; del miBig
                    
                    exp = afwImage.makeExposure(mi)
                    exp.setPsf(psf)

                    footprintSet = afwDet.FootprintSet(mi,
                                                       afwDet.Threshold(0.5*numpy.max(psfIm.getArray())),
                                                       "DETECTED")
                    footprintSet.makeSources(catalog)
                    if len(catalog) == 0:
                        raise RuntimeError("Failed to detect any objects")
                    elif len(catalog) == 1:
                        source = catalog[0]
                    else:               # more than one source; find the once closest to (xc, yc)
                        for i, s in enumerate(catalog):
                            d = numpy.hypot(xc - s.getX(), yc - s.getY())
                            if i == 0 or d < dmin:
                                source, dmin = s, d
                                                    
                    measureSources.applyWithPeak(source, exp)
                    xc, yc = source.getCentroid()

                # residuals using spatial model
                try:
                    chi2 = algorithmsLib.subtractPsf(psf, im, xc, yc)
                except:
                    chi2 = numpy.nan
                    continue
                
                resid = im
                if variance:
                    resid = resid.getImage()
                    var = im.getVariance()
                    var = var.Factory(var, True)
                    numpy.sqrt(var.getArray(), var.getArray()) # inplace sqrt
                    resid /= var
                    
                im_resid.append(resid)

                # Fit the PSF components directly to the data (i.e. ignoring the spatial model)
                if fitBasisComponents:
                    im = cand.getMaskedImage()
                    
                    im = im.Factory(im, True)
                    im.setXY0(cand.getMaskedImage().getXY0())

                    noSpatialKernel = afwMath.cast_LinearCombinationKernel(psf.getKernel())
                    candCenter = afwGeom.PointD(cand.getXCenter(), cand.getYCenter())
                    fit = algorithmsLib.fitKernelParamsToImage(noSpatialKernel, im, candCenter)
                    params = fit[0]
                    kernels = afwMath.KernelList(fit[1])
                    outputKernel = afwMath.LinearCombinationKernel(kernels, params)

                    outImage = afwImage.ImageD(outputKernel.getDimensions())
                    outputKernel.computeImage(outImage, False)

                    im -= outImage.convertF()
                    resid = im

                    if margin > 0:
                        bim = im.Factory(w + 2*margin, h + 2*margin)
                        afwMath.randomGaussianImage(bim.getImage(), afwMath.Random())
                        bim *= stdev

                        sbim = im.Factory(bim, bbox)
                        sbim <<= resid
                        del sbim
                        resid = bim

                    if variance:
                        resid = resid.getImage()
                        resid /= var

                    im_resid.append(resid)

                im = im_resid.makeMosaic()
            else:
                im = cand.getMaskedImage()

            if normalize:
                im /= afwMath.makeStatistics(im, afwMath.MAX).getValue()

            objId = splitId(cand.getSource().getId(), True)["objId"]
            if psf:
                lab = "%d chi^2 %.1f" % (objId, rchi2)
                ctype = ds9.RED if cand.isBad() else ds9.GREEN
            else:
                lab = "%d flux %8.3g" % (objId, cand.getSource().getPsfFlux())
                ctype = ds9.GREEN

            mos.append(im, lab, ctype)

            if False and numpy.isnan(rchi2):
                ds9.mtv(cand.getMaskedImage().getImage(), title="candidate", frame=1)
                print "amp",  cand.getAmplitude()

            im = cand.getMaskedImage()
            center = (candidateIndex, xc - im.getX0(), yc - im.getY0())
            candidateIndex += 1
            if cand.isBad():
                candidateCentersBad.append(center)
            else:
                candidateCenters.append(center)

    if variance:
        title = "chi(Psf fit)"
    else:
        title = "Stars & residuals"
    mosaicImage = mos.makeMosaic(frame=frame, title=title)

    with ds9.Buffering():
        for centers, color in ((candidateCenters, ds9.GREEN), (candidateCentersBad, ds9.RED)):
            for cen in centers:
                bbox = mos.getBBox(cen[0])
                ds9.dot("+", cen[1] + bbox.getMinX(), cen[2] + bbox.getMinY(), frame=frame, ctype=color)

    return mosaicImage
コード例 #4
0
ファイル: utils.py プロジェクト: rpmunoz/meas_algorithms
def plotPsfSpatialModel(exposure, psf, psfCellSet, showBadCandidates=True, numSample=128,
                        matchKernelAmplitudes=False, keepPlots=True):
    """Plot the PSF spatial model."""

    if not plt:
        print >> sys.stderr, "Unable to import matplotlib"
        return

    noSpatialKernel = afwMath.cast_LinearCombinationKernel(psf.getKernel())
    candPos = list()
    candFits = list()
    badPos = list()
    badFits = list()
    candAmps = list()
    badAmps = list()
    for cell in psfCellSet.getCellList():
        for cand in cell.begin(False):
            cand = algorithmsLib.cast_PsfCandidateF(cand)
            if not showBadCandidates and cand.isBad():
                continue
            candCenter = afwGeom.PointD(cand.getXCenter(), cand.getYCenter())
            try:
                im = cand.getMaskedImage()
            except Exception:
                continue

            fit = algorithmsLib.fitKernelParamsToImage(noSpatialKernel, im, candCenter)
            params = fit[0]
            kernels = fit[1]
            amp = 0.0
            for p, k in zip(params, kernels):
                amp += p * afwMath.cast_FixedKernel(k).getSum()

            targetFits = badFits if cand.isBad() else candFits
            targetPos = badPos if cand.isBad() else candPos
            targetAmps = badAmps if cand.isBad() else candAmps

            targetFits.append([x / amp for x in params])
            targetPos.append(candCenter)
            targetAmps.append(amp)

    numCandidates = len(candFits)
    numBasisFuncs = noSpatialKernel.getNBasisKernels()

    xGood = numpy.array([pos.getX() for pos in candPos]) - exposure.getX0()
    yGood = numpy.array([pos.getY() for pos in candPos]) - exposure.getY0()
    zGood = numpy.array(candFits)
    ampGood = numpy.array(candAmps)

    xBad = numpy.array([pos.getX() for pos in badPos]) - exposure.getX0()
    yBad = numpy.array([pos.getY() for pos in badPos]) - exposure.getY0()
    zBad = numpy.array(badFits)
    ampBad = numpy.array(badAmps)
    numBad = len(badPos)

    xRange = numpy.linspace(0, exposure.getWidth(), num=numSample)
    yRange = numpy.linspace(0, exposure.getHeight(), num=numSample)

    kernel = psf.getKernel()
    nKernelComponents = kernel.getNKernelParameters()
    #
    # Figure out how many panels we'll need
    #
    nPanelX = int(math.sqrt(nKernelComponents))
    nPanelY = nKernelComponents//nPanelX
    while nPanelY*nPanelX < nKernelComponents:
        nPanelX += 1

    fig = plt.figure(1)
    fig.clf()
    try:
        fig.canvas._tkcanvas._root().lift() # == Tk's raise, but raise is a python reserved word
    except:                                 # protect against API changes
        pass
    #
    # Generator for axes arranged in panels
    #
    subplots = makeSubplots(fig, 2, 2, Nx=nPanelX, Ny=nPanelY, xgutter=0.06, ygutter=0.06, pygutter=0.04)

    for k in range(nKernelComponents):
        func = kernel.getSpatialFunction(k)
        dfGood = zGood[:,k] - numpy.array([func(pos.getX(), pos.getY()) for pos in candPos])
        yMin = dfGood.min()
        yMax = dfGood.max()
        if numBad > 0:
            dfBad = zBad[:,k] - numpy.array([func(pos.getX(), pos.getY()) for pos in badPos])
            yMin = min([yMin, dfBad.min()])
            yMax = max([yMax, dfBad.max()])
        yMin -= 0.05 * (yMax - yMin)
        yMax += 0.05 * (yMax - yMin)

        yMin = -0.01
        yMax = 0.01

        fRange = numpy.ndarray((len(xRange), len(yRange)))
        for j, yVal in enumerate(yRange):
            for i, xVal in enumerate(xRange):
                fRange[j][i] = func(xVal, yVal)

        #-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-

        ax = subplots.next()

        ax.set_autoscale_on(False)
        ax.set_xbound(lower=0, upper=exposure.getHeight())
        ax.set_ybound(lower=yMin, upper=yMax)
        ax.plot(yGood, dfGood, 'b+')
        if numBad > 0:
            ax.plot(yBad, dfBad, 'r+')
        ax.axhline(0.0)
        ax.set_title('Residuals(y)')

        #-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-

        ax = subplots.next()

        if matchKernelAmplitudes and k == 0:
            vmin = 0.0
            vmax = 1.1
        else:
            vmin = fRange.min()
            vmax = fRange.max()

        norm = matplotlib.colors.Normalize(vmin=vmin, vmax=vmax)
        im = ax.imshow(fRange, aspect='auto', origin="lower", norm=norm,
                       extent=[0, exposure.getWidth()-1, 0, exposure.getHeight()-1])
        ax.set_title('Spatial poly')
        plt.colorbar(im, orientation='horizontal', ticks=[vmin, vmax])

        #-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-

        ax = subplots.next()
        ax.set_autoscale_on(False)
        ax.set_xbound(lower=0, upper=exposure.getWidth())
        ax.set_ybound(lower=yMin, upper=yMax)
        ax.plot(xGood, dfGood, 'b+')
        if numBad > 0:
            ax.plot(xBad, dfBad, 'r+')
        ax.axhline(0.0)
        ax.set_title('K%d Residuals(x)' % k)

        #-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-

        ax = subplots.next()

        if False:
            ax.scatter(xGood, yGood, c=dfGood, marker='o')
            ax.scatter(xBad, yBad, c=dfBad, marker='x')
            ax.set_xbound(lower=0, upper=exposure.getWidth())
            ax.set_ybound(lower=0, upper=exposure.getHeight())
            ax.set_title('Spatial residuals')
            plt.colorbar(im, orientation='horizontal')
        else:
            calib = exposure.getCalib()
            if calib.getFluxMag0()[0] <= 0:
                calib = type(calib)()
                calib.setFluxMag0(1.0)

            with CalibNoThrow():
                ax.plot(calib.getMagnitude(candAmps), zGood[:,k], 'b+')
                if numBad > 0:
                    ax.plot(calib.getMagnitude(badAmps), zBad[:,k], 'r+')

            ax.set_title('Flux variation')

    fig.show()

    global keptPlots
    if keepPlots and not keptPlots:
        # Keep plots open when done
        def show():
            print "%s: Please close plots when done." % __name__
            try:
                plt.show()
            except:
                pass
            print "Plots closed, exiting..."
        import atexit
        atexit.register(show)
        keptPlots = True
コード例 #5
0
ファイル: utils.py プロジェクト: rpmunoz/meas_algorithms
def showPsfCandidates(exposure, psfCellSet, psf=None, frame=None, normalize=True, showBadCandidates=True,
                      fitBasisComponents=False, variance=None, chi=None):
    """Display the PSF candidates.

    If psf is provided include PSF model and residuals;  if normalize is true normalize the PSFs
    (and residuals)

    If chi is True, generate a plot of residuals/sqrt(variance), i.e. chi

    If fitBasisComponents is true, also find the best linear combination of the PSF's components
    (if they exist)
    """
    if chi is None:
        if variance is not None:        # old name for chi
            chi = variance
    #
    # Show us the ccandidates
    #
    mos = displayUtils.Mosaic()
    #
    candidateCenters = []
    candidateCentersBad = []
    candidateIndex = 0

    for cell in psfCellSet.getCellList():
        for cand in cell.begin(False): # include bad candidates
            cand = algorithmsLib.cast_PsfCandidateF(cand)

            rchi2 = cand.getChi2()
            if rchi2 > 1e100:
                rchi2 = numpy.nan

            if not showBadCandidates and cand.isBad():
                continue

            if psf:
                im_resid = displayUtils.Mosaic(gutter=0, background=-5, mode="x")

                try:
                    im = cand.getMaskedImage() # copy of this object's image
                    xc, yc = cand.getXCenter(), cand.getYCenter()

                    margin = 0 if True else 5
                    w, h = im.getDimensions()
                    bbox = afwGeom.BoxI(afwGeom.PointI(margin, margin), im.getDimensions())

                    if margin > 0:
                        bim = im.Factory(w + 2*margin, h + 2*margin)

                        stdev = numpy.sqrt(afwMath.makeStatistics(im.getVariance(), afwMath.MEAN).getValue())
                        afwMath.randomGaussianImage(bim.getImage(), afwMath.Random())
                        bim.getVariance().set(stdev**2)

                        bim.assign(im, bbox)
                        im = bim
                        xc += margin
                        yc += margin

                    im = im.Factory(im, True)
                    im.setXY0(cand.getMaskedImage().getXY0())
                except:
                    continue

                if not variance:
                    im_resid.append(im.Factory(im, True))

                if True:                # tweak up centroids
                    mi = im
                    psfIm = mi.getImage()
                    config = measBase.SingleFrameMeasurementTask.ConfigClass()
                    config.slots.centroid = "base_SdssCentroid"

                    schema = afwTable.SourceTable.makeMinimalSchema()
                    measureSources =  measBase.SingleFrameMeasurementTask(schema, config=config)
                    catalog = afwTable.SourceCatalog(schema)

                    extra = 10          # enough margin to run the sdss centroider
                    miBig = mi.Factory(im.getWidth() + 2*extra, im.getHeight() + 2*extra)
                    miBig[extra:-extra, extra:-extra] = mi
                    miBig.setXY0(mi.getX0() - extra, mi.getY0() - extra)
                    mi = miBig
                    del miBig

                    exp = afwImage.makeExposure(mi)
                    exp.setPsf(psf)

                    footprintSet = afwDet.FootprintSet(mi,
                                                       afwDet.Threshold(0.5*numpy.max(psfIm.getArray())),
                                                       "DETECTED")
                    footprintSet.makeSources(catalog)

                    if len(catalog) == 0:
                        raise RuntimeError("Failed to detect any objects")

                    measureSources.run(catalog, exp)
                    if len(catalog) == 1:
                        source = catalog[0]
                    else:               # more than one source; find the once closest to (xc, yc)
                        dmin = None # an invalid value to catch logic errors
                        for i, s in enumerate(catalog):
                            d = numpy.hypot(xc - s.getX(), yc - s.getY())
                            if i == 0 or d < dmin:
                                source, dmin = s, d
                    xc, yc = source.getCentroid()

                # residuals using spatial model
                try:
                    chi2 = algorithmsLib.subtractPsf(psf, im, xc, yc)
                except:
                    chi2 = numpy.nan
                    continue

                resid = im
                if variance:
                    resid = resid.getImage()
                    var = im.getVariance()
                    var = var.Factory(var, True)
                    numpy.sqrt(var.getArray(), var.getArray()) # inplace sqrt
                    resid /= var

                im_resid.append(resid)

                # Fit the PSF components directly to the data (i.e. ignoring the spatial model)
                if fitBasisComponents:
                    im = cand.getMaskedImage()

                    im = im.Factory(im, True)
                    im.setXY0(cand.getMaskedImage().getXY0())

                    try:
                        noSpatialKernel = afwMath.cast_LinearCombinationKernel(psf.getKernel())
                    except:
                        noSpatialKernel = None

                    if noSpatialKernel:
                        candCenter = afwGeom.PointD(cand.getXCenter(), cand.getYCenter())
                        fit = algorithmsLib.fitKernelParamsToImage(noSpatialKernel, im, candCenter)
                        params = fit[0]
                        kernels = afwMath.KernelList(fit[1])
                        outputKernel = afwMath.LinearCombinationKernel(kernels, params)

                        outImage = afwImage.ImageD(outputKernel.getDimensions())
                        outputKernel.computeImage(outImage, False)

                        im -= outImage.convertF()
                        resid = im

                        if margin > 0:
                            bim = im.Factory(w + 2*margin, h + 2*margin)
                            afwMath.randomGaussianImage(bim.getImage(), afwMath.Random())
                            bim *= stdev

                            bim.assign(resid, bbox)
                            resid = bim

                        if variance:
                            resid = resid.getImage()
                            resid /= var

                        im_resid.append(resid)

                im = im_resid.makeMosaic()
            else:
                im = cand.getMaskedImage()

            if normalize:
                im /= afwMath.makeStatistics(im, afwMath.MAX).getValue()

            objId = splitId(cand.getSource().getId(), True)["objId"]
            if psf:
                lab = "%d chi^2 %.1f" % (objId, rchi2)
                ctype = ds9.RED if cand.isBad() else ds9.GREEN
            else:
                lab = "%d flux %8.3g" % (objId, cand.getSource().getPsfFlux())
                ctype = ds9.GREEN

            mos.append(im, lab, ctype)

            if False and numpy.isnan(rchi2):
                ds9.mtv(cand.getMaskedImage().getImage(), title="candidate", frame=1)
                print "amp",  cand.getAmplitude()

            im = cand.getMaskedImage()
            center = (candidateIndex, xc - im.getX0(), yc - im.getY0())
            candidateIndex += 1
            if cand.isBad():
                candidateCentersBad.append(center)
            else:
                candidateCenters.append(center)

    if variance:
        title = "chi(Psf fit)"
    else:
        title = "Stars & residuals"
    mosaicImage = mos.makeMosaic(frame=frame, title=title)

    with ds9.Buffering():
        for centers, color in ((candidateCenters, ds9.GREEN), (candidateCentersBad, ds9.RED)):
            for cen in centers:
                bbox = mos.getBBox(cen[0])
                ds9.dot("+", cen[1] + bbox.getMinX(), cen[2] + bbox.getMinY(), frame=frame, ctype=color)

    return mosaicImage
コード例 #6
0
def showPsfCandidates(exposure, psfCellSet, psf=None, frame=None, normalize=True, showBadCandidates=True,
                      variance=None, chi=None):
    """Display the PSF candidates.
If psf is provided include PSF model and residuals;  if normalize is true normalize the PSFs (and residuals)
If chi is True, generate a plot of residuals/sqrt(variance), i.e. chi
"""
    if chi is None:
        if variance is not None:        # old name for chi
            chi = variance
    #
    # Show us the ccandidates
    #
    mos = displayUtils.Mosaic()
    #
    candidateCenters = []
    candidateCentersBad = []
    candidateIndex = 0

    for cell in psfCellSet.getCellList():
        for cand in cell.begin(False): # include bad candidates
            cand = algorithmsLib.cast_PsfCandidateF(cand)

            rchi2 = cand.getChi2()
            if rchi2 > 1e100:
                rchi2 = numpy.nan

            if not showBadCandidates and cand.isBad():
                continue

            if psf:
                im_resid = displayUtils.Mosaic(gutter=0, background=-5, mode="x")

                try:
                    im = cand.getMaskedImage() # copy of this object's image
                    xc, yc = cand.getXCenter(), cand.getYCenter()

                    margin = 0 if True else 5
                    w, h = im.getDimensions()
                    bbox = afwGeom.BoxI(afwGeom.PointI(margin, margin), im.getDimensions())

                    if margin > 0:
                        bim = im.Factory(w + 2*margin, h + 2*margin)

                        stdev = numpy.sqrt(afwMath.makeStatistics(im.getVariance(), afwMath.MEAN).getValue())
                        afwMath.randomGaussianImage(bim.getImage(), afwMath.Random())
                        bim *= stdev
                        var = bim.getVariance(); var.set(stdev**2); del var

                        sbim = im.Factory(bim, bbox)
                        sbim <<= im
                        del sbim
                        im = bim
                        xc += margin; yc += margin

                    im = im.Factory(im, True)
                    im.setXY0(cand.getMaskedImage().getXY0())
                except:
                    continue

                if not variance:
                    im_resid.append(im.Factory(im, True))

                # residuals using spatial model
                chi2 = algorithmsLib.subtractPsf(psf, im, xc, yc)
                
                resid = im
                if variance:
                    resid = resid.getImage()
                    var = im.getVariance()
                    var = var.Factory(var, True)
                    numpy.sqrt(var.getArray(), var.getArray()) # inplace sqrt
                    resid /= var
                    
                im_resid.append(resid)

                # Fit the PSF components directly to the data (i.e. ignoring the spatial model)
                im = cand.getMaskedImage()

                im = im.Factory(im, True)
                im.setXY0(cand.getMaskedImage().getXY0())

                noSpatialKernel = afwMath.cast_LinearCombinationKernel(psf.getKernel())
                candCenter = afwGeom.PointD(cand.getXCenter(), cand.getYCenter())
                fit = algorithmsLib.fitKernelParamsToImage(noSpatialKernel, im, candCenter)
                params = fit[0]
                kernels = afwMath.KernelList(fit[1])
                outputKernel = afwMath.LinearCombinationKernel(kernels, params)

                outImage = afwImage.ImageD(outputKernel.getDimensions())
                outputKernel.computeImage(outImage, False)

                im -= outImage.convertF()
                resid = im

                if margin > 0:
                    bim = im.Factory(w + 2*margin, h + 2*margin)
                    afwMath.randomGaussianImage(bim.getImage(), afwMath.Random())
                    bim *= stdev

                    sbim = im.Factory(bim, bbox)
                    sbim <<= resid
                    del sbim
                    resid = bim

                if variance:
                    resid = resid.getImage()
                    resid /= var
                    
                im_resid.append(resid)

                im = im_resid.makeMosaic()
            else:
                im = cand.getMaskedImage()

            if normalize:
                im /= afwMath.makeStatistics(im, afwMath.MAX).getValue()

            objId = splitId(cand.getSource().getId(), True)["objId"]
            if psf:
                lab = "%d chi^2 %.1f" % (objId, rchi2)
                ctype = ds9.RED if cand.isBad() else ds9.GREEN
            else:
                lab = "%d flux %8.3g" % (objId, cand.getSource().getPsfFlux())
                ctype = ds9.GREEN

            mos.append(im, lab, ctype)

            if False and numpy.isnan(rchi2):
                ds9.mtv(cand.getMaskedImage().getImage(), title="candidate", frame=1)
                print "amp",  cand.getAmplitude()

            im = cand.getMaskedImage()
            center = (candidateIndex, xc - im.getX0(), yc - im.getY0())
            candidateIndex += 1
            if cand.isBad():
                candidateCentersBad.append(center)
            else:
                candidateCenters.append(center)

    if variance:
        title = "chi(Psf fit)"
    else:
        title = "Stars & residuals"
    mosaicImage = mos.makeMosaic(frame=frame, title=title)

    with ds9.Buffering():
        for centers, color in ((candidateCenters, ds9.GREEN), (candidateCentersBad, ds9.RED)):
            for cen in centers:
                bbox = mos.getBBox(cen[0])
                ds9.dot("+", cen[1] + bbox.getMinX(), cen[2] + bbox.getMinY(), frame=frame, ctype=color)

    return mosaicImage
コード例 #7
0
def plotPsfSpatialModel(exposure, psf, psfCellSet, showBadCandidates=True, numSample=128,
                        matchKernelAmplitudes=False, keepPlots=True):
    """Plot the PSF spatial model."""

    if not plt:
        print >> sys.stderr, "Unable to import matplotlib"
        return

    noSpatialKernel = afwMath.cast_LinearCombinationKernel(psf.getKernel())
    candPos = list()
    candFits = list()
    badPos = list()
    badFits = list()
    candAmps = list()
    badAmps = list()
    for cell in psfCellSet.getCellList():
        for cand in cell.begin(False):
            cand = algorithmsLib.cast_PsfCandidateF(cand)
            if not showBadCandidates and cand.isBad():
                continue
            candCenter = afwGeom.PointD(cand.getXCenter(), cand.getYCenter())
            try:
                im = cand.getMaskedImage()
            except Exception:
                continue

            fit = algorithmsLib.fitKernelParamsToImage(noSpatialKernel, im, candCenter)
            params = fit[0]
            kernels = fit[1]
            amp = 0.0
            for p, k in zip(params, kernels):
                amp += p * afwMath.cast_FixedKernel(k).getSum()

            targetFits = badFits if cand.isBad() else candFits
            targetPos = badPos if cand.isBad() else candPos
            targetAmps = badAmps if cand.isBad() else candAmps

            targetFits.append([x / amp for x in params])
            targetPos.append(candCenter)
            targetAmps.append(amp)

    numCandidates = len(candFits)
    numBasisFuncs = noSpatialKernel.getNBasisKernels()

    xGood = numpy.array([pos.getX() for pos in candPos]) - exposure.getX0()
    yGood = numpy.array([pos.getY() for pos in candPos]) - exposure.getY0()
    zGood = numpy.array(candFits)
    ampGood = numpy.array(candAmps)

    xBad = numpy.array([pos.getX() for pos in badPos]) - exposure.getX0()
    yBad = numpy.array([pos.getY() for pos in badPos]) - exposure.getY0()
    zBad = numpy.array(badFits)
    ampBad = numpy.array(badAmps)
    numBad = len(badPos)

    xRange = numpy.linspace(0, exposure.getWidth(), num=numSample)
    yRange = numpy.linspace(0, exposure.getHeight(), num=numSample)

    kernel = psf.getKernel()
    nKernelComponents = kernel.getNKernelParameters()
    #
    # Figure out how many panels we'll need
    #
    nPanelX = int(math.sqrt(nKernelComponents))
    nPanelY = nKernelComponents//nPanelX
    while nPanelY*nPanelX < nKernelComponents:
        nPanelX += 1

    fig = plt.figure(1)
    fig.clf()
    try:
        fig.canvas._tkcanvas._root().lift() # == Tk's raise, but raise is a python reserved word
    except:                                 # protect against API changes
        pass
    #
    # Generator for axes arranged in panels
    #
    subplots = makeSubplots(fig, 2, 2, Nx=nPanelX, Ny=nPanelY, xgutter=0.06, ygutter=0.06, pygutter=0.04)

    for k in range(nKernelComponents):
        func = kernel.getSpatialFunction(k)
        dfGood = zGood[:,k] - numpy.array([func(pos.getX(), pos.getY()) for pos in candPos])
        yMin = dfGood.min()
        yMax = dfGood.max()
        if numBad > 0:
            dfBad = zBad[:,k] - numpy.array([func(pos.getX(), pos.getY()) for pos in badPos])
            yMin = min([yMin, dfBad.min()])
            yMax = max([yMax, dfBad.max()])
        yMin -= 0.05 * (yMax - yMin)
        yMax += 0.05 * (yMax - yMin)

        yMin = -0.01
        yMax = 0.01

        fRange = numpy.ndarray((len(xRange), len(yRange)))
        for j, yVal in enumerate(yRange):
            for i, xVal in enumerate(xRange):
                fRange[j][i] = func(xVal, yVal)

        #-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-

        ax = subplots.next()

        ax.set_autoscale_on(False)
        ax.set_xbound(lower=0, upper=exposure.getHeight())
        ax.set_ybound(lower=yMin, upper=yMax)
        ax.plot(yGood, dfGood, 'b+')
        if numBad > 0:
            ax.plot(yBad, dfBad, 'r+')
        ax.axhline(0.0)
        ax.set_title('Residuals(y)')

        #-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-

        ax = subplots.next()

        if matchKernelAmplitudes and k == 0:
            vmin = 0.0
            vmax = 1.1
        else:
            vmin = fRange.min()
            vmax = fRange.max()

        norm = matplotlib.colors.Normalize(vmin=vmin, vmax=vmax)
        im = ax.imshow(fRange, aspect='auto', origin="lower", norm=norm,
                       extent=[0, exposure.getWidth()-1, 0, exposure.getHeight()-1])
        ax.set_title('Spatial poly')
        plt.colorbar(im, orientation='horizontal', ticks=[vmin, vmax])

        #-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-

        ax = subplots.next()
        ax.set_autoscale_on(False)
        ax.set_xbound(lower=0, upper=exposure.getWidth())
        ax.set_ybound(lower=yMin, upper=yMax)
        ax.plot(xGood, dfGood, 'b+')
        if numBad > 0:
            ax.plot(xBad, dfBad, 'r+')
        ax.axhline(0.0)
        ax.set_title('K%d Residuals(x)' % k)

        #-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-

        ax = subplots.next()

        if False:
            ax.scatter(xGood, yGood, c=dfGood, marker='o')
            ax.scatter(xBad, yBad, c=dfBad, marker='x')
            ax.set_xbound(lower=0, upper=exposure.getWidth())
            ax.set_ybound(lower=0, upper=exposure.getHeight())
            ax.set_title('Spatial residuals')
            plt.colorbar(im, orientation='horizontal')
        else:
            calib = exposure.getCalib()
            if calib.getFluxMag0()[0] <= 0:
                calib = type(calib)()
                calib.setFluxMag0(1.0)

            with CalibNoThrow():
                ax.plot(calib.getMagnitude(candAmps), zGood[:,k], 'b+')
                if numBad > 0:
                    ax.plot(calib.getMagnitude(badAmps), zBad[:,k], 'r+')

            ax.set_title('Flux variation')

    fig.show()

    global keptPlots
    if keepPlots and not keptPlots:
        # Keep plots open when done
        def show():
            print "%s: Please close plots when done." % __name__
            try:
                plt.show()
            except:
                pass
            print "Plots closed, exiting..."
        import atexit
        atexit.register(show)
        keptPlots = True