def showPsfResiduals(exposure,
sourceSet,
magType="psf",
scale=10,
frame=None,
showAmps=False):
mimIn = exposure.getMaskedImage()
mimIn = mimIn.Factory(mimIn, True) # make a copy to subtract from
psf = exposure.getPsf()
psfWidth, psfHeight = psf.getLocalKernel().getDimensions()
#
# Make the image that we'll paste our residuals into. N.b. they can overlap the edges
#
w, h = int(mimIn.getWidth() / scale), int(mimIn.getHeight() / scale)
im = mimIn.Factory(w + psfWidth, h + psfHeight)
cenPos = []
for s in sourceSet:
x, y = s.getX(), s.getY()
sx, sy = int(x / scale + 0.5), int(y / scale + 0.5)
smim = im.Factory(
im,
afwGeom.BoxI(afwGeom.PointI(sx, sy),
afwGeom.ExtentI(psfWidth, psfHeight)),
afwImage.PARENT)
sim = smim.getImage()
try:
if magType == "ap":
flux = s.getApFlux()
elif magType == "model":
flux = s.getModelFlux()
elif magType == "psf":
flux = s.getPsfFlux()
else:
raise RuntimeError("Unknown flux type %s" % magType)
algorithmsLib.subtractPsf(psf, mimIn, x, y, flux)
except Exception, e:
print e
try:
expIm = mimIn.getImage().Factory(
mimIn.getImage(),
afwGeom.BoxI(
afwGeom.PointI(
int(x) - psfWidth // 2,
int(y) - psfHeight // 2),
afwGeom.ExtentI(psfWidth, psfHeight)), afwImage.PARENT)
except pexExcept.LsstCppException:
continue
cenPos.append([x - expIm.getX0() + sx, y - expIm.getY0() + sy])
sim += expIm
def showPsfResiduals(exposure, sourceSet, magType="psf", scale=10, frame=None, showAmps=False):
mimIn = exposure.getMaskedImage()
mimIn = mimIn.Factory(mimIn, True) # make a copy to subtract from
psf = exposure.getPsf()
psfWidth, psfHeight = psf.getLocalKernel().getDimensions()
#
# Make the image that we'll paste our residuals into. N.b. they can overlap the edges
#
w, h = int(mimIn.getWidth()/scale), int(mimIn.getHeight()/scale)
im = mimIn.Factory(w + psfWidth, h + psfHeight)
cenPos = []
for s in sourceSet:
x, y = s.getX(), s.getY()
sx, sy = int(x/scale + 0.5), int(y/scale + 0.5)
smim = im.Factory(im, afwGeom.BoxI(afwGeom.PointI(sx, sy), afwGeom.ExtentI(psfWidth, psfHeight)),
afwImage.PARENT)
sim = smim.getImage()
try:
if magType == "ap":
flux = s.getApFlux()
elif magType == "model":
flux = s.getModelFlux()
elif magType == "psf":
flux = s.getPsfFlux()
else:
raise RuntimeError("Unknown flux type %s" % magType)
algorithmsLib.subtractPsf(psf, mimIn, x, y, flux)
except Exception, e:
print e
try:
expIm = mimIn.getImage().Factory(mimIn.getImage(),
afwGeom.BoxI(afwGeom.PointI(int(x) - psfWidth//2,
int(y) - psfHeight//2),
afwGeom.ExtentI(psfWidth, psfHeight)),
afwImage.PARENT)
except pexExcept.LsstCppException:
continue
cenPos.append([x - expIm.getX0() + sx, y - expIm.getY0() + sy])
sim += expIm
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
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
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
