def insert(self, source):
"""Insert source into the histogram."""
ixx, iyy, ixy = source.getIxx(), source.getIyy(), source.getIxy()
if self.detector:
distorter = self.detector.getDistortion()
if distorter:
p = afwGeom.Point2D(source.getX() + self.xy0.getX(),
source.getY() + self.xy0.getY())
m = distorter.undistort(p, geomEllip.Quadrupole(ixx, iyy, ixy),
self.detector)
ixx, iyy, ixy = m.getIxx(), m.getIyy(), m.getIxy()
try:
pixel = self.momentsToPixel(ixx, iyy)
i = int(pixel[0])
j = int(pixel[1])
except:
return 0
if i in range(0, self._xSize) and j in range(0, self._ySize):
if i != 0 or j != 0:
self._psfImage.set(i, j, self._psfImage.get(i, j) + 1)
self._num += 1
return 1 # success
return 0 # failure
def testHsmSourceMomentsRound(self):
for (i, imageid) in enumerate(file_indices):
source = self.runMeasurement("ext_shapeHSM_HsmSourceMomentsRound",
imageid, x_centroid[i], y_centroid[i],
sky_var[i])
x = source.get("ext_shapeHSM_HsmSourceMomentsRound_x")
y = source.get("ext_shapeHSM_HsmSourceMomentsRound_y")
xx = source.get("ext_shapeHSM_HsmSourceMomentsRound_xx")
yy = source.get("ext_shapeHSM_HsmSourceMomentsRound_yy")
xy = source.get("ext_shapeHSM_HsmSourceMomentsRound_xy")
flux = source.get("ext_shapeHSM_HsmSourceMomentsRound_Flux")
# Centroids from GalSim use the FITS lower-left corner of 1,1
offset = self.xy0 + self.offset
self.assertAlmostEqual(x - offset.getX(),
round_moments_expected[i][4] - 1, 3)
self.assertAlmostEqual(y - offset.getY(),
round_moments_expected[i][5] - 1, 3)
expected = afwEll.Quadrupole(
afwEll.SeparableDistortionDeterminantRadius(
round_moments_expected[i][1], round_moments_expected[i][2],
round_moments_expected[i][0]))
self.assertAlmostEqual(xx, expected.getIxx(), SHAPE_DECIMALS)
self.assertAlmostEqual(xy, expected.getIxy(), SHAPE_DECIMALS)
self.assertAlmostEqual(yy, expected.getIyy(), SHAPE_DECIMALS)
self.assertAlmostEqual(flux, round_moments_expected[i][3],
SHAPE_DECIMALS)
def testHsmPsfMoments(self):
for width in (2.0, 3.0, 4.0):
psf = afwDetection.GaussianPsf(35, 35, width)
exposure = afwImage.ExposureF(45, 56)
exposure.getMaskedImage().set(1.0, 0, 1.0)
exposure.setPsf(psf)
# perform the shape measurement
msConfig = base.SingleFrameMeasurementConfig()
msConfig.algorithms.names = ["ext_shapeHSM_HsmPsfMoments"]
plugin, cat = makePluginAndCat(lsst.meas.extensions.shapeHSM.HsmPsfMomentsAlgorithm,
"ext_shapeHSM_HsmPsfMoments", centroid="centroid",
control=lsst.meas.extensions.shapeHSM.HsmPsfMomentsControl())
source = cat.addNew()
source.set("centroid_x", 23)
source.set("centroid_y", 34)
offset = afwGeom.Point2I(23, 34)
tmpSpans = afwGeom.SpanSet.fromShape(int(width), offset=offset)
source.setFootprint(afwDetection.Footprint(tmpSpans))
plugin.measure(source, exposure)
x = source.get("ext_shapeHSM_HsmPsfMoments_x")
y = source.get("ext_shapeHSM_HsmPsfMoments_y")
xx = source.get("ext_shapeHSM_HsmPsfMoments_xx")
yy = source.get("ext_shapeHSM_HsmPsfMoments_yy")
xy = source.get("ext_shapeHSM_HsmPsfMoments_xy")
self.assertAlmostEqual(x, 0.0, 3)
self.assertAlmostEqual(y, 0.0, 3)
expected = afwEll.Quadrupole(afwEll.Axes(width, width, 0.0))
self.assertAlmostEqual(xx, expected.getIxx(), SHAPE_DECIMALS)
self.assertAlmostEqual(xy, expected.getIxy(), SHAPE_DECIMALS)
self.assertAlmostEqual(yy, expected.getIyy(), SHAPE_DECIMALS)
def testCorrectWeightedMoments(self):
'''
Test that the measured moments can be corrected for the fact that the measurement
contains information on the moments of the weight function used to make the
measurement. The results should only contain the objects shape and not the moments
used to make the measurement. This is a subset of the functionality in testNoNoiseGaussians.
Because the other test tests a broader scope, it is useful to have this test happen under
more restrictive conditions.
'''
for dEllipseCore in self.ellipseCores:
for dCenter in self.centers:
dEllipse = el.Ellipse(dEllipseCore, dCenter)
dArray = self.evaluateGaussian(dEllipse)
for wEllipseCore in self.ellipseCores:
for wCenter in self.centers:
wEllipse = el.Ellipse(wEllipseCore, wCenter)
wArray = self.evaluateGaussian(wEllipse)
product = dArray * wArray
i0 = np.sum(product)
ix = np.sum(product * self.xg) / i0
iy = np.sum(product * self.yg) / i0
ixx = np.sum(product * (self.xg - ix)**2) / i0
iyy = np.sum(product * (self.yg - iy)**2) / i0
ixy = np.sum(product * (self.xg - ix) *
(self.yg - iy)) / i0
mEllipseCore = el.Quadrupole(ixx, iyy, ixy)
mCenter = lsst.afw.geom.Point2D(ix, iy)
SimpleShape.correctWeightedMoments(
wEllipseCore, mEllipseCore, mCenter)
self.assertFloatsAlmostEqual(
mEllipseCore.getParameterVector(),
dEllipseCore.getParameterVector(),
rtol=1E-8,
atol=1E-11)
def main():
x = numpy.linspace(-5, 5, 101)
y = numpy.linspace(-5, 5, 101)
ellipses.Quadrupole(ellipses.Axes(1.2, 0.8, 0.3))
hermiteBasis = lsst.shapelet.BasisEvaluator(4, lsst.shapelet.HERMITE)
laguerreBasis = lsst.shapelet.BasisEvaluator(4, lsst.shapelet.LAGUERRE)
processBasis(hermiteBasis, x, y)
processBasis(laguerreBasis, x, y)
pyplot.show()
def setUp(self):
self.ellipseCores = [
el.Quadrupole(25.0, 25.0, 0.0),
el.Quadrupole(27.0, 22.0, -5.0),
el.Quadrupole(23.0, 28.0, 2.0),
]
self.centers = [
lsst.afw.geom.Point2D(0.0, 0.0),
lsst.afw.geom.Point2D(2.0, 3.0),
lsst.afw.geom.Point2D(-1.0, 2.5),
]
for ellipseCore in self.ellipseCores:
ellipseCore.scale(2)
self.bbox = lsst.afw.geom.Box2I(lsst.afw.geom.Point2I(-500, -500),
lsst.afw.geom.Point2I(50, 50))
self.xg, self.yg = np.meshgrid(
np.arange(self.bbox.getBeginX(), self.bbox.getEndX(), dtype=float),
np.arange(self.bbox.getBeginY(), self.bbox.getEndY(), dtype=float))
def determinePsf(self, exposure, psfCandidateList, metadata=None, flagKey=None):
"""
@param[in] exposure: exposure containing the psf candidates (lsst.afw.image.Exposure)
@param[in] psfCandidateList: a sequence of PSF candidates (each an lsst.meas.algorithms.PsfCandidate);
typically obtained by detecting sources and then running them through a star selector
@param[in,out] metadata a home for interesting tidbits of information
@param[in] flagKey: schema key used to mark sources actually used in PSF determination
@return psf
"""
import lsstDebug
display = lsstDebug.Info(__name__).display
displayExposure = display and \
lsstDebug.Info(__name__).displayExposure # display the Exposure + spatialCells
displayPsfCandidates = display and \
lsstDebug.Info(__name__).displayPsfCandidates # show the viable candidates
displayPsfComponents = display and \
lsstDebug.Info(__name__).displayPsfComponents # show the basis functions
showBadCandidates = display and \
lsstDebug.Info(__name__).showBadCandidates # Include bad candidates (meaningless, methinks)
displayResiduals = display and \
lsstDebug.Info(__name__).displayResiduals # show residuals
displayPsfMosaic = display and \
lsstDebug.Info(__name__).displayPsfMosaic # show mosaic of reconstructed PSF(x,y)
matchKernelAmplitudes = lsstDebug.Info(__name__).matchKernelAmplitudes # match Kernel amplitudes for spatial plots
normalizeResiduals = lsstDebug.Info(__name__).normalizeResiduals # Normalise residuals by object amplitude
mi = exposure.getMaskedImage()
nCand = len(psfCandidateList)
if nCand == 0:
raise RuntimeError("No PSF candidates supplied.")
#
# How big should our PSF models be?
#
if display: # only needed for debug plots
# construct and populate a spatial cell set
bbox = mi.getBBox(afwImage.PARENT)
psfCellSet = afwMath.SpatialCellSet(bbox, self.config.sizeCellX, self.config.sizeCellY)
else:
psfCellSet = None
sizes = np.empty(nCand)
for i, psfCandidate in enumerate(psfCandidateList):
try:
if psfCellSet:
psfCellSet.insertCandidate(psfCandidate)
except Exception, e:
self.debugLog.debug(2, "Skipping PSF candidate %d of %d: %s" % (i, len(psfCandidateList), e))
continue
source = psfCandidate.getSource()
quad = afwEll.Quadrupole(source.getIxx(), source.getIyy(), source.getIxy())
rmsSize = quad.getTraceRadius()
sizes[i] = rmsSize
def compareMoments(basis, x, y, z):
e = basis.evaluate()
monopole = z.sum()
dipole = geom.Point2D((x * z).sum() / monopole, (y * z).sum() / monopole)
dx = x - dipole.getX()
dy = y - dipole.getY()
quadrupole = ellipses.Quadrupole((dx**2 * z).sum() / monopole,
(dy**1 * z).sum() / monopole,
(dx * dy * z).sum() / monopole)
print(ellipses.Ellipse(quadrupole, monopole))
print(e.computeMoments())
def testNullDistortionDefaultCcd(self):
"""Test that we can use a NullDistortion even if the Detector is default-constructed"""
ccd = cameraGeom.Ccd(cameraGeom.Id("dummy"))
distorter = cameraGeom.NullDistortion()
pin = afwGeom.PointD(0, 0)
pout = distorter.undistort(pin, ccd)
self.assertEqual(pin, pout)
ein = geomEllip.Quadrupole(1, 0, 1)
eout = distorter.distort(pin, ein, ccd)
self.assertEqual(ein, eout)
def testHsmPsfMoments(self, width, useSourceCentroidOffset, varyBBox,
wrongBBox, center):
psf = PyGaussianPsf(35,
35,
width,
varyBBox=varyBBox,
wrongBBox=wrongBBox)
exposure = afwImage.ExposureF(45, 56)
exposure.getMaskedImage().set(1.0, 0, 1.0)
exposure.setPsf(psf)
# perform the shape measurement
msConfig = base.SingleFrameMeasurementConfig()
msConfig.algorithms.names = ["ext_shapeHSM_HsmPsfMoments"]
control = lsst.meas.extensions.shapeHSM.HsmPsfMomentsControl()
self.assertFalse(control.useSourceCentroidOffset)
control.useSourceCentroidOffset = useSourceCentroidOffset
plugin, cat = makePluginAndCat(
lsst.meas.extensions.shapeHSM.HsmPsfMomentsAlgorithm,
"ext_shapeHSM_HsmPsfMoments",
centroid="centroid",
control=control)
source = cat.addNew()
source.set("centroid_x", center[0])
source.set("centroid_y", center[1])
offset = geom.Point2I(*center)
tmpSpans = afwGeom.SpanSet.fromShape(int(width), offset=offset)
source.setFootprint(afwDetection.Footprint(tmpSpans))
plugin.measure(source, exposure)
x = source.get("ext_shapeHSM_HsmPsfMoments_x")
y = source.get("ext_shapeHSM_HsmPsfMoments_y")
xx = source.get("ext_shapeHSM_HsmPsfMoments_xx")
yy = source.get("ext_shapeHSM_HsmPsfMoments_yy")
xy = source.get("ext_shapeHSM_HsmPsfMoments_xy")
self.assertFalse(source.get("ext_shapeHSM_HsmPsfMoments_flag"))
self.assertFalse(
source.get("ext_shapeHSM_HsmPsfMoments_flag_no_pixels"))
self.assertFalse(
source.get("ext_shapeHSM_HsmPsfMoments_flag_not_contained"))
self.assertFalse(
source.get("ext_shapeHSM_HsmPsfMoments_flag_parent_source"))
self.assertAlmostEqual(x, 0.0, 3)
self.assertAlmostEqual(y, 0.0, 3)
expected = afwEll.Quadrupole(afwEll.Axes(width, width, 0.0))
self.assertAlmostEqual(xx, expected.getIxx(), SHAPE_DECIMALS)
self.assertAlmostEqual(xy, expected.getIxy(), SHAPE_DECIMALS)
self.assertAlmostEqual(yy, expected.getIyy(), SHAPE_DECIMALS)
def testDerivative3(self):
amplitude = 3.2
radius = 2.7
psfEllipse = ellipses.Quadrupole(2.3, 1.8, 0.6)
psfAmplitude = 5.3
builder = ms.GaussianModelBuilder(self.x, self.y, amplitude, radius,
psfEllipse, psfAmplitude)
a = numpy.zeros((3, builder.getSize()), dtype=float).transpose()
builder.update(self.ellipse)
builder.computeDerivative(a)
def makeEllipse(p):
return ellipses.Axes(*p)
n = self.buildNumericalDerivative(builder,
self.ellipse.getParameterVector(),
makeEllipse)
# no hard requirement for tolerances here, but I've dialed them to the max to avoid regressions
self.assertClose(a, n, rtol=1E-15, atol=1E-9)
def determineKernelSize(self, psfCandidateList):
"""Sets self.kernelSize (logic in this routine is cut-and-paste from pcaPsfDeterminer)"""
sizes = np.zeros(len(psfCandidateList))
for i, psfCandidate in enumerate(psfCandidateList):
source = psfCandidate.getSource()
quad = afwEll.Quadrupole(source.getIxx(), source.getIyy(),
source.getIxy())
axes = afwEll.Axes(quad)
sizes[i] = axes.getA()
if self.config.kernelSize >= 15:
print >> sys.stderr, "WARNING: NOT scaling kernelSize by stellar quadrupole moment, but using absolute value"
self.kernelSize = int(self.config.kernelSize)
else:
self.kernelSize = 2 * int(self.config.kernelSize *
np.sqrt(np.median(sizes)) + 0.5) + 1
self.kernelSize = max(self.kernelSize, self.config.kernelSizeMin)
self.kernelSize = min(self.kernelSize, self.config.kernelSizeMax)
print >> sys.stderr, 'setting kernelSize =', self.kernelSize
def testModel3(self):
amplitude = 3.2
radius = 2.7
psfEllipse = ellipses.Quadrupole(2.3, 1.8, 0.6)
psfAmplitude = 5.3
mgc = ms.GaussianComponent(amplitude, radius)
shapelet = mgc.makeShapelet(ellipses.Ellipse(self.ellipse))
psf = ms.GaussianComponent(psfAmplitude, 1.0).makeShapelet(
ellipses.Ellipse(psfEllipse))
shapelet = shapelet.convolve(psf)
builder = ms.GaussianModelBuilder(self.x, self.y, amplitude, radius,
psfEllipse, psfAmplitude)
builder.update(self.ellipse)
self.ellipse.scale(radius)
ellipse = self.ellipse.convolve(psfEllipse)
self.assertClose(builder.getModel(),
amplitude * psfAmplitude * self.buildModel(ellipse))
z0 = builder.getModel()
z1 = psfAmplitude * amplitude * self.buildModel(ellipse)
z2 = self.evalShapelets(shapelet)
self.assertClose(z0, z1)
self.assertClose(z0, z2)
def testzAxisCases(self):
r = 1000.0
iqq = geomEllip.Quadrupole(1.0, 1.0, 0.0)
px = afwGeom.Point2D(r, 0.0)
py = afwGeom.Point2D(0.0, r)
pxy = afwGeom.Point2D(r / numpy.sqrt(2.0), r / numpy.sqrt(2.0))
nDist = cameraGeom.NullDistortion()
rDist = cameraGeom.RadialPolyDistortion(self.coeffs)
rcoeffs = self.coeffs[:]
rcoeffs.reverse()
r2Known = numpy.polyval(rcoeffs, r)
epsilon = 1.0e-6
drKnown = -(r2Known - numpy.polyval(rcoeffs, r + epsilon)) / epsilon
points = [px, py, pxy]
scalings = [drKnown**2, drKnown**2, 0.5 * (drKnown**2 - 1.0)]
for i in range(3):
p = points[i]
scale = scalings[i]
# check the point
p2 = rDist.distort(p, self.det)
x, y = p2.getX(), p2.getY()
r2Calc = numpy.hypot(x, y)
if self.prynt:
print "r2known,r2Calc: ", r2Known, r2Calc
self.assertAlmostEqual(r2Known, r2Calc)
# check the moment
iqq2 = rDist.distort(p, iqq, self.det)
iqqList = [iqq2.getIxx(), iqq2.getIyy(), iqq2.getIxy()]
if self.prynt:
print "scale: ", scale, iqqList[i]
self.assertAlmostEqual(scale, iqqList[i])
def setUp(self):
self.x0, self.y0 = 0, 0
self.nx, self.ny = 512, 512 #2048, 4096
self.sky = 100.0
self.nObj = 100
# make a distorter
# This is a lot of distortion ... from circle r=1, to ellipse with a=1.3 (ie. 30%)
# For suprimecam, we expect only about 5%
self.distCoeffs = [0.0, 1.0, 2.0e-04, 3.0e-8]
lanczosOrder = 3
coefficientsDistort = True
self.distorter = cameraGeom.RadialPolyDistortion(
self.distCoeffs, coefficientsDistort, lanczosOrder)
# make a detector
self.detector = cameraUtils.makeDefaultCcd(
afwGeom.Box2I(afwGeom.Point2I(0, 0),
afwGeom.Extent2I(self.nx, self.ny)))
self.detector.setDistortion(self.distorter)
self.detector.setCenter(cameraGeom.FpPoint(
255.5, 255.5)) # move boresight from center to 0,0
if False:
for x, y in [(0, 0), (0, 511), (511, 0), (511, 511)]:
p = afwGeom.Point2D(x, y)
iqq = self.distorter.distort(p, geomEllip.Quadrupole(),
self.detector)
print x, y, geomEllip.Axes(iqq)
print self.detector.getPositionFromPixel(p).getMm()
print "Max distortion on this detector: ", self.distorter.computeMaxShear(
self.detector)
# detection policies
self.detConfig = measAlg.SourceDetectionConfig()
# measurement policies
self.measSrcConfig = measAlg.SourceMeasurementConfig()
# psf star selector
starSelectorFactory = measAlg.starSelectorRegistry["secondMoment"]
starSelectorConfig = starSelectorFactory.ConfigClass()
starSelectorConfig.fluxLim = 5000.0
starSelectorConfig.histSize = 32
starSelectorConfig.clumpNSigma = 1.0
starSelectorConfig.badFlags = []
self.starSelector = starSelectorFactory(starSelectorConfig)
# psf determiner
psfDeterminerFactory = measAlg.psfDeterminerRegistry["pca"]
psfDeterminerConfig = psfDeterminerFactory.ConfigClass()
width, height = self.nx, self.ny
nEigenComponents = 3
psfDeterminerConfig.sizeCellX = width // 3
psfDeterminerConfig.sizeCellY = height // 3
psfDeterminerConfig.nEigenComponents = nEigenComponents
psfDeterminerConfig.spatialOrder = 1
psfDeterminerConfig.kernelSizeMin = 31
psfDeterminerConfig.nStarPerCell = 0
psfDeterminerConfig.nStarPerCellSpatialFit = 0 # unlimited
self.psfDeterminer = psfDeterminerFactory(psfDeterminerConfig)
def main(dataDir,
visit,
title="",
outputTxtFileName=None,
showFwhm=False,
minFwhm=None,
maxFwhm=None,
correctDistortion=False,
showEllipticity=False,
ellipticityDirection=False,
showNdataFwhm=False,
showNdataEll=False,
minNdata=None,
maxNdata=None,
gridPoints=30,
verbose=False):
butler = dafPersist.ButlerFactory(mapper=hscSim.HscSimMapper(
root=dataDir)).create()
camera = butler.get("camera")
if not (showFwhm or showEllipticity or showNdataFwhm or showNdataEll
or outputTxtFileName):
showFwhm = True
#
# Get a dict of cameraGeom::Ccd indexed by serial number
#
ccds = {}
for raft in camera:
for ccd in raft:
ccd.setTrimmed(True)
ccds[ccd.getId().getSerial()] = ccd
#
# Read all the tableSeeingMap files, converting their (x, y) to focal plane coordinates
#
xArr = []
yArr = []
ellArr = []
fwhmArr = []
paArr = []
aArr = []
bArr = []
e1Arr = []
e2Arr = []
elle1e2Arr = []
for tab in butler.subset("tableSeeingMap", visit=visit):
# we could use tab.datasetExists() but it prints a rude message
fileName = butler.get("tableSeeingMap_filename", **tab.dataId)[0]
if not os.path.exists(fileName):
continue
with open(fileName) as fd:
ccd = None
for line in fd.readlines():
if re.search(r"^\s*#", line):
continue
fields = [float(_) for _ in line.split()]
if ccd is None:
ccd = ccds[int(fields[0])]
x, y, fwhm, ell, pa, a, b = fields[1:8]
x, y = ccd.getPositionFromPixel(afwGeom.PointD(x, y)).getMm()
xArr.append(x)
yArr.append(y)
ellArr.append(ell)
fwhmArr.append(fwhm)
paArr.append(pa)
aArr.append(a)
bArr.append(b)
if len(fields) == 11:
e1 = fields[8]
e2 = fields[9]
elle1e2 = fields[10]
else:
e1 = -9999.
e2 = -9999.
elle1e2 = -9999.
e1Arr.append(e1)
e2Arr.append(e2)
elle1e2Arr.append(elle1e2)
xArr = np.array(xArr)
yArr = np.array(yArr)
ellArr = np.array(ellArr)
fwhmArr = np.array(fwhmArr) * 0.168 # arcseconds
paArr = np.radians(np.array(paArr))
aArr = np.array(aArr)
bArr = np.array(bArr)
e1Arr = np.array(e1Arr)
e2Arr = np.array(e2Arr)
elle1e2Arr = np.array(elle1e2Arr)
if correctDistortion:
import lsst.afw.geom.ellipses as afwEllipses
dist = camera.getDistortion()
for i in range(len(aArr)):
axes = afwEllipses.Axes(aArr[i], bArr[i], paArr[i])
if False: # testing only!
axes = afwEllipses.Axes(1.0, 1.0, np.arctan2(yArr[i], xArr[i]))
quad = afwEllipses.Quadrupole(axes)
quad = quad.transform(
dist.computeQuadrupoleTransform(
afwGeom.PointD(xArr[i], yArr[i]), False))
axes = afwEllipses.Axes(quad)
aArr[i], bArr[i], paArr[i] = axes.getA(), axes.getB(
), axes.getTheta()
ellArr = 1 - bArr / aArr
if len(xArr) == 0:
gridPoints = 0
xs, ys = [], []
else:
N = gridPoints * 1j
extent = [min(xArr), max(xArr), min(yArr), max(yArr)]
xs, ys = np.mgrid[extent[0]:extent[1]:N, extent[2]:extent[3]:N]
title = [
title,
]
title.append("\n#")
if outputTxtFileName:
f = open(outputTxtFileName, 'w')
f.write("# %s visit %s\n" % (" ".join(title), visit))
for x, y, ell, fwhm, pa, a, b, e1, e2, elle1e2 in zip(
xArr, yArr, ellArr, fwhmArr, paArr, aArr, bArr, e1Arr, e2Arr,
elle1e2Arr):
f.write('%f %f %f %f %f %f %f %f %f %f\n' %
(x, y, ell, fwhm, pa, a, b, e1, e2, elle1e2))
if showFwhm:
title.append("FWHM (arcsec)")
if len(xs) > 0:
fwhmResampled = griddata(xArr, yArr, fwhmArr, xs, ys)
plt.imshow(fwhmResampled.T,
extent=extent,
vmin=minFwhm,
vmax=maxFwhm,
origin='lower')
plt.colorbar()
if outputTxtFileName:
ndataGrids = getNumDataGrids(xArr, yArr, fwhmArr, xs, ys)
f = open(outputTxtFileName + '-fwhm-grid.txt', 'w')
f.write("# %s visit %s\n" % (" ".join(title), visit))
for xline, yline, fwhmline, ndataline in zip(
xs.tolist(), ys.tolist(), fwhmResampled.tolist(),
ndataGrids):
for xx, yy, fwhm, ndata in zip(xline, yline, fwhmline,
ndataline):
if fwhm is None:
fwhm = -9999
f.write('%f %f %f %d\n' % (xx, yy, fwhm, ndata))
elif showEllipticity:
title.append("Ellipticity")
scale = 4
if ellipticityDirection: # we don't care about the magnitude
ellArr = 0.1
u = -ellArr * np.cos(paArr)
v = -ellArr * np.sin(paArr)
if gridPoints > 0:
u = griddata(xArr, yArr, u, xs, ys)
v = griddata(xArr, yArr, v, xs, ys)
x, y = xs, ys
else:
x, y = xArr, yArr
Q = plt.quiver(
x,
y,
u,
v,
scale=scale,
pivot="middle",
headwidth=0,
headlength=0,
headaxislength=0,
)
keyLen = 0.10
if not ellipticityDirection: # we care about the magnitude
plt.quiverkey(Q, 0.20, 0.95, keyLen, "e=%g" % keyLen, labelpos='W')
if outputTxtFileName:
ndataGrids = getNumDataGrids(xArr, yArr, ellArr, xs, ys)
f = open(outputTxtFileName + '-ell-grid.txt', 'w')
f.write("# %s visit %s\n" % (" ".join(title), visit))
#f.write('# %f %f %f %f %f %f %f\n' % (x, y, ell, fwhm, pa, a, b))
for xline, yline, uline, vline, ndataline in zip(
x.tolist(), y.tolist(), u.tolist(), v.tolist(),
ndataGrids):
for xx, yy, uu, vv, ndata in zip(xline, yline, uline, vline,
ndataline):
if uu is None:
uu = -9999
if vv is None:
vv = -9999
f.write('%f %f %f %f %d\n' % (xx, yy, uu, vv, ndata))
elif showNdataFwhm:
title.append("N per fwhm grid")
if len(xs) > 0:
ndataGrids = getNumDataGrids(xArr, yArr, fwhmArr, xs, ys)
plt.imshow(ndataGrids,
interpolation='nearest',
extent=extent,
vmin=minNdata,
vmax=maxNdata,
origin='lower')
plt.colorbar()
else:
pass
elif showNdataEll:
title.append("N per ell grid")
if len(xs) > 0:
ndataGrids = getNumDataGrids(xArr, yArr, ellArr, xs, ys)
plt.imshow(ndataGrids,
interpolation='nearest',
extent=extent,
vmin=minNdata,
vmax=maxNdata,
origin='lower')
plt.colorbar()
else:
pass
#plt.plot(xArr, yArr, "r.")
#plt.plot(xs, ys, "b.")
plt.axes().set_aspect('equal')
plt.axis([-20000, 20000, -20000, 20000])
def frameInfoFrom(filepath):
import pyfits
with pyfits.open(filepath) as hdul:
h = hdul[0].header
'object=ABELL2163 filter=HSC-I exptime=360.0 alt=62.11143274 azm=202.32265181 hst=(23:40:08.363-23:40:48.546)'
return 'object=%s filter=%s exptime=%.1f azm=%.2f hst=%s' % (
h['OBJECT'], h['FILTER01'], h['EXPTIME'], h['AZIMUTH'],
h['HST'])
title.insert(
0,
frameInfoFrom(
butler.get('raw_filename', {
'visit': visit,
'ccd': 0
})[0]))
title.append(r'$\langle$FWHM$\rangle %4.2f$"' % np.median(fwhmArr))
plt.title("%s visit=%s" % (" ".join(title), visit), fontsize=9)
return plt
def selectStars(self, exposure, catalog, matches=None):
"""Return a list of PSF candidates that represent likely stars
A list of PSF candidates may be used by a PSF fitter to construct a PSF.
@param[in] exposure: the exposure containing the sources
@param[in] catalog: a SourceCatalog containing sources that may be stars
@param[in] matches: astrometric matches; ignored by this star selector
@return psfCandidateList: a list of PSF candidates.
"""
import lsstDebug
display = lsstDebug.Info(__name__).display
displayExposure = lsstDebug.Info(
__name__).displayExposure # display the Exposure + spatialCells
plotMagSize = lsstDebug.Info(
__name__).plotMagSize # display the magnitude-size relation
dumpData = lsstDebug.Info(
__name__).dumpData # dump data to pickle file?
# create a log for my application
logger = pexLogging.Log(pexLogging.getDefaultLog(),
"meas.algorithms.objectSizeStarSelector")
detector = exposure.getDetector()
distorter = None
xy0 = afwGeom.Point2D(0, 0)
if not detector is None:
cPix = detector.getCenterPixel()
detSize = detector.getSize()
xy0.setX(cPix.getX() - int(0.5 * detSize.getMm()[0]))
xy0.setY(cPix.getY() - int(0.5 * detSize.getMm()[1]))
distorter = detector.getDistortion()
#
# Look at the distribution of stars in the magnitude-size plane
#
flux = catalog.get(self._sourceFluxField)
xx = numpy.empty(len(catalog))
xy = numpy.empty_like(xx)
yy = numpy.empty_like(xx)
for i, source in enumerate(catalog):
Ixx, Ixy, Iyy = source.getIxx(), source.getIxy(), source.getIyy()
if distorter:
xpix, ypix = source.getX() + xy0.getX(), source.getY(
) + xy0.getY()
p = afwGeom.Point2D(xpix, ypix)
m = distorter.undistort(p, geomEllip.Quadrupole(Ixx, Iyy, Ixy),
detector)
Ixx, Ixy, Iyy = m.getIxx(), m.getIxy(), m.getIyy()
xx[i], xy[i], yy[i] = Ixx, Ixy, Iyy
width = numpy.sqrt(xx + yy)
bad = reduce(lambda x, y: numpy.logical_or(x, catalog.get(y)),
self._badFlags, False)
bad = numpy.logical_or(bad, flux < self._fluxMin)
bad = numpy.logical_or(bad, numpy.logical_not(numpy.isfinite(width)))
bad = numpy.logical_or(bad, numpy.logical_not(numpy.isfinite(flux)))
bad = numpy.logical_or(bad, width < self._widthMin)
bad = numpy.logical_or(bad, width > self._widthMax)
if self._fluxMax > 0:
bad = numpy.logical_or(bad, flux > self._fluxMax)
good = numpy.logical_not(bad)
if not numpy.any(good):
raise RuntimeError(
"No objects passed our cuts for consideration as psf stars")
mag = -2.5 * numpy.log10(flux[good])
width = width[good]
#
# Look for the maximum in the size histogram, then search upwards for the minimum that separates
# the initial peak (of, we presume, stars) from the galaxies
#
if dumpData:
import os, cPickle as pickle
_ii = 0
while True:
pickleFile = os.path.expanduser(
os.path.join("~", "widths-%d.pkl" % _ii))
if not os.path.exists(pickleFile):
break
_ii += 1
with open(pickleFile, "wb") as fd:
pickle.dump(mag, fd, -1)
pickle.dump(width, fd, -1)
centers, clusterId = _kcenters(width, nCluster=4, useMedian=True)
if display and plotMagSize and pyplot:
fig = plot(mag,
width,
centers,
clusterId,
marker="+",
markersize=3,
markeredgewidth=None,
ltype=':',
clear=True)
else:
fig = None
clusterId = _improveCluster(width, centers, clusterId)
if display and plotMagSize and pyplot:
plot(mag,
width,
centers,
clusterId,
marker="x",
markersize=3,
markeredgewidth=None)
stellar = (clusterId == 0)
#
# We know enough to plot, if so requested
#
frame = 0
if fig:
if display and displayExposure:
ds9.mtv(exposure.getMaskedImage(),
frame=frame,
title="PSF candidates")
global eventHandler
eventHandler = EventHandler(fig.get_axes()[0],
mag,
width,
catalog.getX()[good],
catalog.getY()[good],
frames=[frame])
fig.show()
#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
while True:
try:
reply = raw_input(
"continue? [c h(elp) q(uit) p(db)] ").strip()
except EOFError:
reply = "y"
if reply:
if reply[0] == "h":
print """\
We cluster the points; red are the stellar candidates and the other colours are other clusters.
Points labelled + are rejects from the cluster (only for cluster 0).
At this prompt, you can continue with almost any key; 'p' enters pdb, and 'h' prints this text
If displayExposure is true, you can put the cursor on a point and hit 'p' to see it in ds9.
"""
elif reply[0] == "p":
import pdb
pdb.set_trace()
elif reply[0] == 'q':
sys.exit(1)
else:
break
if display and displayExposure:
mi = exposure.getMaskedImage()
with ds9.Buffering():
for i, source in enumerate(catalog):
if good[i]:
ctype = ds9.GREEN # star candidate
else:
ctype = ds9.RED # not star
ds9.dot("+",
source.getX() - mi.getX0(),
source.getY() - mi.getY0(),
frame=frame,
ctype=ctype)
#
# Time to use that stellar classification to generate psfCandidateList
#
with ds9.Buffering():
psfCandidateList = []
for isStellar, source in zip(
stellar, [s for g, s in zip(good, catalog) if g]):
if not isStellar:
continue
try:
psfCandidate = algorithmsLib.makePsfCandidate(
source, exposure)
# The setXXX methods are class static, but it's convenient to call them on
# an instance as we don't know Exposure's pixel type
# (and hence psfCandidate's exact type)
if psfCandidate.getWidth() == 0:
psfCandidate.setBorderWidth(self._borderWidth)
psfCandidate.setWidth(self._kernelSize +
2 * self._borderWidth)
psfCandidate.setHeight(self._kernelSize +
2 * self._borderWidth)
im = psfCandidate.getMaskedImage().getImage()
vmax = afwMath.makeStatistics(im, afwMath.MAX).getValue()
if not numpy.isfinite(vmax):
continue
psfCandidateList.append(psfCandidate)
if display and displayExposure:
ds9.dot("o",
source.getX() - mi.getX0(),
source.getY() - mi.getY0(),
size=4,
frame=frame,
ctype=ds9.CYAN)
except Exception as err:
logger.log(
pexLogging.Log.INFO,
"Failed to make a psfCandidate from source %d: %s" %
(source.getId(), err))
return psfCandidateList
def setUp(self):
ixx, iyy, ixy = 1.0, 1.0, 0.0
self.data = geomEllip.Quadrupole(ixx, iyy, ixy)
def determinePsf(self, exposure, psfCandidateList, metadata=None, flagKey=None):
"""Determine a PSFEX PSF model for an exposure given a list of PSF
candidates.
Parameters
----------
exposure: `lsst.afw.image.Exposure`
Exposure containing the PSF candidates.
psfCandidateList: iterable of `lsst.meas.algorithms.PsfCandidate`
Sequence of PSF candidates typically obtained by detecting sources
and then running them through a star selector.
metadata: metadata, optional
A home for interesting tidbits of information.
flagKey: `lsst.afw.table.Key`, optional
Schema key used to mark sources actually used in PSF determination.
Returns
-------
psf: `lsst.meas.extensions.psfex.PsfexPsf`
The determined PSF.
"""
import lsstDebug
display = lsstDebug.Info(__name__).display
displayExposure = display and \
lsstDebug.Info(__name__).displayExposure # display the Exposure + spatialCells
displayPsfComponents = display and \
lsstDebug.Info(__name__).displayPsfComponents # show the basis functions
showBadCandidates = display and \
lsstDebug.Info(__name__).showBadCandidates # Include bad candidates (meaningless, methinks)
displayResiduals = display and \
lsstDebug.Info(__name__).displayResiduals # show residuals
displayPsfMosaic = display and \
lsstDebug.Info(__name__).displayPsfMosaic # show mosaic of reconstructed PSF(x,y)
normalizeResiduals = lsstDebug.Info(__name__).normalizeResiduals
afwDisplay.setDefaultMaskTransparency(75)
# Normalise residuals by object amplitude
mi = exposure.getMaskedImage()
nCand = len(psfCandidateList)
if nCand == 0:
raise RuntimeError("No PSF candidates supplied.")
#
# How big should our PSF models be?
#
if display: # only needed for debug plots
# construct and populate a spatial cell set
bbox = mi.getBBox(afwImage.PARENT)
psfCellSet = afwMath.SpatialCellSet(bbox, self.config.sizeCellX, self.config.sizeCellY)
else:
psfCellSet = None
sizes = np.empty(nCand)
for i, psfCandidate in enumerate(psfCandidateList):
try:
if psfCellSet:
psfCellSet.insertCandidate(psfCandidate)
except Exception as e:
self.log.debug("Skipping PSF candidate %d of %d: %s", i, len(psfCandidateList), e)
continue
source = psfCandidate.getSource()
quad = afwEll.Quadrupole(source.getIxx(), source.getIyy(), source.getIxy())
rmsSize = quad.getTraceRadius()
sizes[i] = rmsSize
if self.config.kernelSize >= 15:
self.log.warn("NOT scaling kernelSize by stellar quadrupole moment, but using absolute value")
actualKernelSize = int(self.config.kernelSize)
else:
actualKernelSize = 2 * int(self.config.kernelSize * np.sqrt(np.median(sizes)) + 0.5) + 1
if actualKernelSize < self.config.kernelSizeMin:
actualKernelSize = self.config.kernelSizeMin
if actualKernelSize > self.config.kernelSizeMax:
actualKernelSize = self.config.kernelSizeMax
if display:
rms = np.median(sizes)
print("Median PSF RMS size=%.2f pixels (\"FWHM\"=%.2f)" % (rms, 2*np.sqrt(2*np.log(2))*rms))
# If we manually set the resolution then we need the size in pixel
# units
pixKernelSize = actualKernelSize
if self.config.samplingSize > 0:
pixKernelSize = int(actualKernelSize*self.config.samplingSize)
if pixKernelSize % 2 == 0:
pixKernelSize += 1
self.log.trace("Psfex Kernel size=%.2f, Image Kernel Size=%.2f", actualKernelSize, pixKernelSize)
psfCandidateList[0].setHeight(pixKernelSize)
psfCandidateList[0].setWidth(pixKernelSize)
# -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=- BEGIN PSFEX
#
# Insert the good candidates into the set
#
defaultsFile = os.path.join(os.environ["MEAS_EXTENSIONS_PSFEX_DIR"], "config", "default-lsst.psfex")
args_md = dafBase.PropertySet()
args_md.set("BASIS_TYPE", str(self.config.psfexBasis))
args_md.set("PSFVAR_DEGREES", str(self.config.spatialOrder))
args_md.set("PSF_SIZE", str(actualKernelSize))
args_md.set("PSF_SAMPLING", str(self.config.samplingSize))
prefs = psfex.Prefs(defaultsFile, args_md)
prefs.setCommandLine([])
prefs.addCatalog("psfexPsfDeterminer")
prefs.use()
principalComponentExclusionFlag = bool(bool(psfex.Context.REMOVEHIDDEN)
if False else psfex.Context.KEEPHIDDEN)
context = psfex.Context(prefs.getContextName(), prefs.getContextGroup(),
prefs.getGroupDeg(), principalComponentExclusionFlag)
set = psfex.Set(context)
set.setVigSize(pixKernelSize, pixKernelSize)
set.setFwhm(2*np.sqrt(2*np.log(2))*np.median(sizes))
set.setRecentroid(self.config.recentroid)
catindex, ext = 0, 0
backnoise2 = afwMath.makeStatistics(mi.getImage(), afwMath.VARIANCECLIP).getValue()
ccd = exposure.getDetector()
if ccd:
gain = np.mean(np.array([a.getGain() for a in ccd]))
else:
gain = 1.0
self.log.warn("Setting gain to %g" % (gain,))
pc = 0
contextvalp = []
for i, key in enumerate(context.getName()):
if context.getPcflag(i):
raise RuntimeError("Principal Components can not be accessed")
contextvalp.append(pcval[pc]) # noqa: F821
pc += 1
elif key[0] == ':':
try:
contextvalp.append(exposure.getMetadata().getScalar(key[1:]))
except KeyError:
raise RuntimeError("*Error*: %s parameter not found in the header of %s" %
(key[1:], prefs.getContextName()))
else:
try:
contextvalp.append(np.array([psfCandidateList[_].getSource().get(key)
for _ in range(nCand)]))
except KeyError:
raise RuntimeError("*Error*: %s parameter not found" % (key,))
set.setContextname(i, key)
if display:
frame = 0
if displayExposure:
disp = afwDisplay.Display(frame=frame)
disp.mtv(exposure, title="psf determination")
badBits = mi.getMask().getPlaneBitMask(self.config.badMaskBits)
fluxName = prefs.getPhotfluxRkey()
fluxFlagName = "base_" + fluxName + "_flag"
xpos, ypos = [], []
for i, psfCandidate in enumerate(psfCandidateList):
source = psfCandidate.getSource()
xc, yc = source.getX(), source.getY()
try:
int(xc), int(yc)
except ValueError:
continue
try:
pstamp = psfCandidate.getMaskedImage().clone()
except Exception:
continue
if fluxFlagName in source.schema and source.get(fluxFlagName):
continue
flux = source.get(fluxName)
if flux < 0 or np.isnan(flux):
continue
# From this point, we're configuring the "sample" (PSFEx's version
# of a PSF candidate).
# Having created the sample, we must proceed to configure it, and
# then fini (finalize), or it will be malformed.
try:
sample = set.newSample()
sample.setCatindex(catindex)
sample.setExtindex(ext)
sample.setObjindex(i)
imArray = pstamp.getImage().getArray()
imArray[np.where(np.bitwise_and(pstamp.getMask().getArray(), badBits))] = \
-2*psfex.BIG
sample.setVig(imArray)
sample.setNorm(flux)
sample.setBacknoise2(backnoise2)
sample.setGain(gain)
sample.setX(xc)
sample.setY(yc)
sample.setFluxrad(sizes[i])
for j in range(set.getNcontext()):
sample.setContext(j, float(contextvalp[j][i]))
except Exception as e:
self.log.debug("Exception when processing sample at (%f,%f): %s", xc, yc, e)
continue
else:
set.finiSample(sample)
xpos.append(xc) # for QA
ypos.append(yc)
if displayExposure:
with disp.Buffering():
disp.dot("o", xc, yc, ctype=afwDisplay.CYAN, size=4)
if set.getNsample() == 0:
raise RuntimeError("No good PSF candidates to pass to PSFEx")
# ---- Update min and max and then the scaling
for i in range(set.getNcontext()):
cmin = contextvalp[i].min()
cmax = contextvalp[i].max()
set.setContextScale(i, cmax - cmin)
set.setContextOffset(i, (cmin + cmax)/2.0)
# Don't waste memory!
set.trimMemory()
# =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=- END PSFEX
#
# Do a PSFEX decomposition of those PSF candidates
#
fields = []
field = psfex.Field("Unknown")
field.addExt(exposure.getWcs(), exposure.getWidth(), exposure.getHeight(), set.getNsample())
field.finalize()
fields.append(field)
sets = []
sets.append(set)
psfex.makeit(fields, sets)
psfs = field.getPsfs()
# Flag which objects were actually used in psfex by
good_indices = []
for i in range(sets[0].getNsample()):
index = sets[0].getSample(i).getObjindex()
if index > -1:
good_indices.append(index)
if flagKey is not None:
for i, psfCandidate in enumerate(psfCandidateList):
source = psfCandidate.getSource()
if i in good_indices:
source.set(flagKey, True)
xpos = np.array(xpos)
ypos = np.array(ypos)
numGoodStars = len(good_indices)
avgX, avgY = np.mean(xpos), np.mean(ypos)
psf = psfex.PsfexPsf(psfs[0], geom.Point2D(avgX, avgY))
if False and (displayResiduals or displayPsfMosaic):
ext = 0
frame = 1
diagnostics = True
catDir = "."
title = "psfexPsfDeterminer"
psfex.psfex.showPsf(psfs, set, ext,
[(exposure.getWcs(), exposure.getWidth(), exposure.getHeight())],
nspot=3, trim=5, frame=frame, diagnostics=diagnostics, outDir=catDir,
title=title)
#
# Display code for debugging
#
if display:
assert psfCellSet is not None
if displayExposure:
maUtils.showPsfSpatialCells(exposure, psfCellSet, showChi2=True,
symb="o", ctype=afwDisplay.YELLOW, ctypeBad=afwDisplay.RED,
size=8, display=disp)
if displayResiduals:
disp4 = afwDisplay.Display(frame=4)
maUtils.showPsfCandidates(exposure, psfCellSet, psf=psf, display=disp4,
normalize=normalizeResiduals,
showBadCandidates=showBadCandidates)
if displayPsfComponents:
disp6 = afwDisplay.Display(frame=6)
maUtils.showPsf(psf, display=disp6)
if displayPsfMosaic:
disp7 = afwDisplay.Display(frame=7)
maUtils.showPsfMosaic(exposure, psf, display=disp7, showFwhm=True)
disp.scale('linear', 0, 1)
#
# Generate some QA information
#
# Count PSF stars
#
if metadata is not None:
metadata.set("spatialFitChi2", np.nan)
metadata.set("numAvailStars", nCand)
metadata.set("numGoodStars", numGoodStars)
metadata.set("avgX", avgX)
metadata.set("avgY", avgY)
return psf, psfCellSet
def testShape(self):
"""Tests computation of shape means.
"""
sources = afwTable.SourceCatalog(self.sourceTable)
clusters = apCluster.SourceClusterCatalog(self.clusterTable)
centroidKey = self.sourceTable.getCentroidKey()
shapeKey = self.sourceTable.getShapeKey()
shapeErrKey = self.sourceTable.getShapeErrKey()
badshapeKey = self.sourceTable.getSchema().find("flags.badshape").key
for i in xrange(6):
s = sources.addNew()
s["exposure.id"] = 0
s["exposure.filter.id"] = 0
s.setRa(0.0 * afwGeom.radians)
s.setDec(0.0 * afwGeom.radians)
s.set(centroidKey, afwGeom.Point2D(1799.5, 1799.5))
s.set(shapeKey, afwGeomEllipses.Quadrupole(i, 2.0*i, 0.5*i))
s.set(shapeErrKey, numpy.identity(3, dtype=numpy.float32))
if i < 1 or i > 3:
s.set(badshapeKey, True)
cluster = clusters.addNew()
seVec = apCluster.computeBasicAttributes(
cluster, sources, self.exposures, self.spConfig.exposurePrefix)
fkv = afwTable.FlagKeyVector()
fkv.push_back(badshapeKey)
apCluster.computeShapeMean(cluster, seVec, "shape", fkv)
scale2 = math.radians(1.0/3600.0)**2
self.assertEqual(cluster.getNumSources(), 6)
self.assertEqual(cluster.getNumSources("u"), 6)
self.assertEqual(cluster.getShapeCount("u"), 3)
shape = cluster.getShape("u")
self.assertAlmostEqual(shape.getIxx(), scale2 * 2.0)
self.assertAlmostEqual(shape.getIxx(), scale2 * 4.0)
self.assertAlmostEqual(shape.getIxx(), scale2 * 1.0)
shapeErr = cluster.getShapeErr("u")
self.assertAlmostEqual(shapeErr[0,1], 0.0)
self.assertAlmostEqual(shapeErr[0,2], 0.0)
self.assertAlmostEqual(shapeErr[1,2], 0.0)
self.assertEqual(shapeErr[0,1], shapeErr[1,0])
self.assertEqual(shapeErr[0,2], shapeErr[2,0])
self.assertEqual(shapeErr[1,2], shapeErr[2,1])
self.assertAlmostEqual(shapeErr[0,0], scale2**2/3)
self.assertAlmostEqual(shapeErr[1,1], scale2**2/3)
self.assertAlmostEqual(shapeErr[2,2], scale2**2/3)
# Test with a single source
s = sources[2]
sources = afwTable.SourceCatalog(self.sourceTable)
sources.append(s)
cluster = clusters.addNew()
seVec = apCluster.computeBasicAttributes(
cluster, sources, self.exposures, self.spConfig.exposurePrefix)
apCluster.computeShapeMean(cluster, seVec, "shape", fkv)
self.assertEqual(cluster.getNumSources(), 1)
self.assertEqual(cluster.getNumSources("u"), 1)
self.assertEqual(cluster.getShapeCount("u"), 1)
shape = cluster.getShape("u")
self.assertAlmostEqual(shape.getIxx(), scale2 * 2.0)
self.assertAlmostEqual(shape.getIxx(), scale2 * 4.0)
self.assertAlmostEqual(shape.getIxx(), scale2 * 1.0)
shapeErr = cluster.getShapeErr("u")
self.assertAlmostEqual(shapeErr[0,1], 0.0)
self.assertAlmostEqual(shapeErr[0,2], 0.0)
self.assertAlmostEqual(shapeErr[1,2], 0.0)
self.assertEqual(shapeErr[0,1], shapeErr[1,0])
self.assertEqual(shapeErr[0,2], shapeErr[2,0])
self.assertEqual(shapeErr[1,2], shapeErr[2,1])
self.assertAlmostEqual(shapeErr[0,0], scale2**2)
self.assertAlmostEqual(shapeErr[1,1], scale2**2)
self.assertAlmostEqual(shapeErr[2,2], scale2**2)
def testHsmPsfMomentsDebiasedEdge(self, width, useSourceCentroidOffset,
center):
# As we reduce the flux, our deviation from the expected value
# increases, so decrease tolerance.
var = 1.2
for flux, decimals in [
(1e6, 3),
(1e4, 2),
(1e3, 1),
]:
psf = PyGaussianPsf(35, 35, width)
exposure = afwImage.ExposureF(45, 56)
exposure.getMaskedImage().set(1.0, 0, 2 * var + 1.1)
exposure.setPsf(psf)
# perform the shape measurement
control = lsst.meas.extensions.shapeHSM.HsmPsfMomentsDebiasedControl(
)
control.useSourceCentroidOffset = useSourceCentroidOffset
self.assertEqual(control.noiseSource, "variance")
plugin, cat = makePluginAndCat(
lsst.meas.extensions.shapeHSM.HsmPsfMomentsDebiasedAlgorithm,
"ext_shapeHSM_HsmPsfMomentsDebiased",
centroid="centroid",
psfflux="base_PsfFlux",
control=control)
source = cat.addNew()
source.set("centroid_x", center[0])
source.set("centroid_y", center[1])
offset = geom.Point2I(*center)
source.set("base_PsfFlux_instFlux", flux)
tmpSpans = afwGeom.SpanSet.fromShape(int(width), offset=offset)
source.setFootprint(afwDetection.Footprint(tmpSpans))
# Edge fails when setting noise from var plane
with self.assertRaises(base.MeasurementError):
plugin.measure(source, exposure)
# Succeeds when noise is from meta
exposure.getMetadata().set("BGMEAN", var)
control.noiseSource = "meta"
plugin, cat = makePluginAndCat(
lsst.meas.extensions.shapeHSM.HsmPsfMomentsDebiasedAlgorithm,
"ext_shapeHSM_HsmPsfMomentsDebiased",
centroid="centroid",
psfflux="base_PsfFlux",
control=control)
source = cat.addNew()
source.set("centroid_x", center[0])
source.set("centroid_y", center[1])
offset = geom.Point2I(*center)
source.set("base_PsfFlux_instFlux", flux)
tmpSpans = afwGeom.SpanSet.fromShape(int(width), offset=offset)
source.setFootprint(afwDetection.Footprint(tmpSpans))
plugin.measure(source, exposure)
x = source.get("ext_shapeHSM_HsmPsfMomentsDebiased_x")
y = source.get("ext_shapeHSM_HsmPsfMomentsDebiased_y")
xx = source.get("ext_shapeHSM_HsmPsfMomentsDebiased_xx")
yy = source.get("ext_shapeHSM_HsmPsfMomentsDebiased_yy")
xy = source.get("ext_shapeHSM_HsmPsfMomentsDebiased_xy")
self.assertFalse(
source.get("ext_shapeHSM_HsmPsfMomentsDebiased_flag"))
self.assertFalse(
source.get(
"ext_shapeHSM_HsmPsfMomentsDebiased_flag_no_pixels"))
self.assertFalse(
source.get(
"ext_shapeHSM_HsmPsfMomentsDebiased_flag_not_contained"))
self.assertFalse(
source.get(
"ext_shapeHSM_HsmPsfMomentsDebiased_flag_parent_source"))
# but _does_ set EDGE flag in this case
self.assertTrue(
source.get("ext_shapeHSM_HsmPsfMomentsDebiased_flag_edge"))
expected = afwEll.Quadrupole(afwEll.Axes(width, width, 0.0))
self.assertAlmostEqual(x, 0.0, decimals)
self.assertAlmostEqual(y, 0.0, decimals)
T = expected.getIxx() + expected.getIyy()
self.assertAlmostEqual((xx - expected.getIxx()) / T, 0.0, decimals)
self.assertAlmostEqual((xy - expected.getIxy()) / T, 0.0, decimals)
self.assertAlmostEqual((yy - expected.getIyy()) / T, 0.0, decimals)
# But fails hard if meta doesn't contain BGMEAN
exposure.getMetadata().remove("BGMEAN")
plugin, cat = makePluginAndCat(
lsst.meas.extensions.shapeHSM.HsmPsfMomentsDebiasedAlgorithm,
"ext_shapeHSM_HsmPsfMomentsDebiased",
centroid="centroid",
psfflux="base_PsfFlux",
control=control)
source = cat.addNew()
source.set("centroid_x", center[0])
source.set("centroid_y", center[1])
offset = geom.Point2I(*center)
source.set("base_PsfFlux_instFlux", flux)
tmpSpans = afwGeom.SpanSet.fromShape(int(width), offset=offset)
source.setFootprint(afwDetection.Footprint(tmpSpans))
with self.assertRaises(base.FatalAlgorithmError):
plugin.measure(source, exposure)
def check(self, psfFwhm=0.5, flux=1000.0, forced=False):
"""Check that we can measure convolved fluxes
We create an image with a Gaussian PSF and a single point source.
Measurements of the point source should match expectations for a
Gaussian of the known sigma and known aperture radius.
Parameters
----------
psfFwhm : `float`
PSF FWHM (arcsec)
flux : `float`
Source flux (ADU)
forced : `bool`
Forced measurement?
"""
bbox = afwGeom.Box2I(afwGeom.Point2I(12345, 6789),
afwGeom.Extent2I(200, 300))
# We'll only achieve the target accuracy if the pixel scale is rather smaller than Gaussians
# involved. Otherwise it's important to consider the convolution with the pixel grid, and we're
# not doing that here.
scale = 0.1 * afwGeom.arcseconds
TaskClass = measBase.ForcedMeasurementTask if forced else measBase.SingleFrameMeasurementTask
exposure, center = makeExposure(bbox, scale, psfFwhm, flux)
measConfig = TaskClass.ConfigClass()
algName = "ext_convolved_ConvolvedFlux"
measConfig.plugins.names.add(algName)
if not forced:
measConfig.plugins.names.add("ext_photometryKron_KronFlux")
else:
measConfig.copyColumns = {
"id": "objectId",
"parent": "parentObjectId"
}
values = [ii / scale.asArcseconds() for ii in (0.6, 0.8, 1.0, 1.2)]
algConfig = measConfig.plugins[algName]
algConfig.seeing = values
algConfig.aperture.radii = values
algConfig.aperture.maxSincRadius = max(
values) + 1 # Get as exact as we can
if forced:
offset = lsst.afw.geom.Extent2D(-12.3, 45.6)
kronRadiusName = "my_Kron_Radius"
kronRadius = 12.345
refWcs = exposure.getWcs().clone()
refWcs.shiftReferencePixel(offset)
measConfig.plugins[algName].kronRadiusName = kronRadiusName
refSchema = afwTable.SourceTable.makeMinimalSchema()
centroidKey = afwTable.Point2DKey.addFields(refSchema,
"my_centroid",
doc="centroid",
unit="pixel")
shapeKey = afwTable.QuadrupoleKey.addFields(
refSchema, "my_shape", "shape")
refSchema.getAliasMap().set("slot_Centroid", "my_centroid")
refSchema.getAliasMap().set("slot_Shape", "my_shape")
refSchema.addField("my_centroid_flag",
type="Flag",
doc="centroid flag")
refSchema.addField("my_shape_flag", type="Flag", doc="shape flag")
refSchema.addField(kronRadiusName,
type=float,
doc="my custom kron radius",
units="pixel")
refCat = afwTable.SourceCatalog(refSchema)
refSource = refCat.addNew()
refSource.set(centroidKey, center + offset)
refSource.set(
shapeKey,
afwEll.Quadrupole(afwEll.Axes(kronRadius, kronRadius, 0)))
refSource.set(kronRadiusName, kronRadius)
refSource.setCoord(refWcs.pixelToSky(refSource.get(centroidKey)))
taskInitArgs = (refSchema, )
taskRunArgs = (refCat, refWcs)
else:
taskInitArgs = (afwTable.SourceTable.makeMinimalSchema(), )
taskRunArgs = ()
algMetadata = dafBase.PropertyList()
task = TaskClass(*taskInitArgs,
config=measConfig,
algMetadata=algMetadata)
schema = task.schema
measCat = afwTable.SourceCatalog(schema)
source = measCat.addNew()
source.getTable().setMetadata(algMetadata)
ss = afwDetection.FootprintSet(exposure.getMaskedImage(),
afwDetection.Threshold(0.1))
fp = ss.getFootprints()[0]
source.setFootprint(fp)
task.run(measCat, exposure, *taskRunArgs)
disp = afwDisplay.Display(frame)
disp.mtv(exposure)
disp.dot("x",
*center,
origin=afwImage.PARENT,
title="psfFwhm=%f" % (psfFwhm, ))
self.checkSchema(schema, algConfig.getAllApertureResultNames())
self.checkSchema(schema, algConfig.getAllKronResultNames())
self.checkSchema(schema, algConfig.getAllResultNames())
if not forced:
kronRadius = source.get("ext_photometryKron_KronFlux_radius")
self.assertFalse(source.get(algName + "_flag")) # algorithm succeeded
originalSeeing = psfFwhm / scale.asArcseconds()
for ii, targetSeeing in enumerate(algConfig.seeing):
deconvolve = targetSeeing < originalSeeing
self.assertEqual(source.get(algName + "_%d_deconv" % ii),
deconvolve)
seeing = originalSeeing if deconvolve else targetSeeing
def expected(radius, sigma=seeing / SIGMA_TO_FWHM):
"""Return expected flux for 2D Gaussian with nominated sigma"""
return flux * (1.0 - math.exp(-0.5 * (radius / sigma)**2))
# Kron succeeded and match expectation
if not forced:
kronName = algConfig.getKronResultName(targetSeeing)
kronApRadius = algConfig.kronRadiusForFlux * kronRadius
self.assertFloatsAlmostEqual(source.get(kronName + "_flux"),
expected(kronApRadius),
rtol=1.0e-3)
self.assertGreater(source.get(kronName + "_fluxSigma"), 0)
self.assertFalse(source.get(kronName + "_flag"))
# Aperture measurements suceeded and match expectation
for jj, radius in enumerate(
measConfig.algorithms[algName].aperture.radii):
name = algConfig.getApertureResultName(targetSeeing, radius)
self.assertFloatsAlmostEqual(source.get(name + "_flux"),
expected(radius),
rtol=1.0e-3)
self.assertFalse(source.get(name + "_flag"))
self.assertGreater(source.get(name + "_fluxSigma"), 0)
def selectStars(self, exposure, catalog, matches=None):
"""Return a list of PSF candidates that represent likely stars
A list of PSF candidates may be used by a PSF fitter to construct a PSF.
@param[in] exposure: the exposure containing the sources
@param[in] catalog: a SourceCatalog containing sources that may be stars
@param[in] matches: astrometric matches; ignored by this star selector
@return psfCandidateList: a list of PSF candidates.
"""
import lsstDebug
display = lsstDebug.Info(__name__).display
displayExposure = lsstDebug.Info(
__name__).displayExposure # display the Exposure + spatialCells
isGoodSource = CheckSource(catalog.getTable(), self.config.badFlags,
self.config.fluxLim, self.config.fluxMax)
detector = exposure.getDetector()
distorter = None
xy0 = afwGeom.Point2D(0, 0)
if not detector is None:
cPix = detector.getCenterPixel()
detSize = detector.getSize()
xy0.setX(cPix.getX() - int(0.5 * detSize.getMm()[0]))
xy0.setY(cPix.getY() - int(0.5 * detSize.getMm()[1]))
distorter = detector.getDistortion()
mi = exposure.getMaskedImage()
#
# Create an Image of Ixx v. Iyy, i.e. a 2-D histogram
#
# Use stats on our Ixx/yy values to determine the xMax/yMax range for clump image
iqqList = []
for s in catalog:
ixx, iyy = s.getIxx(), s.getIyy()
# ignore NaN and unrealistically large values
if (ixx == ixx and ixx < self.config.histMomentMax and iyy == iyy
and iyy < self.config.histMomentMax and isGoodSource(s)):
iqqList.append(s.getIxx())
iqqList.append(s.getIyy())
stat = afwMath.makeStatistics(
iqqList, afwMath.MEANCLIP | afwMath.STDEVCLIP | afwMath.MAX)
iqqMean = stat.getValue(afwMath.MEANCLIP)
iqqStd = stat.getValue(afwMath.STDEVCLIP)
iqqMax = stat.getValue(afwMath.MAX)
iqqLimit = max(iqqMean + self.config.histMomentClip * iqqStd,
self.config.histMomentMaxMultiplier * iqqMean)
# if the max value is smaller than our range, use max as the limit, but don't go below N*mean
if iqqLimit > iqqMax:
iqqLimit = max(self.config.histMomentMinMultiplier * iqqMean,
iqqMax)
psfHist = _PsfShapeHistogram(detector=detector,
xSize=self.config.histSize,
ySize=self.config.histSize,
ixxMax=iqqLimit,
iyyMax=iqqLimit,
xy0=xy0)
if display and displayExposure:
frame = 0
ds9.mtv(mi, frame=frame, title="PSF candidates")
with ds9.Buffering():
for source in catalog:
if isGoodSource(source):
if psfHist.insert(
source
): # n.b. this call has the side effect of inserting
ctype = ds9.GREEN # good
else:
ctype = ds9.MAGENTA # rejected
else:
ctype = ds9.RED # bad
if display and displayExposure:
ds9.dot("o",
source.getX() - mi.getX0(),
source.getY() - mi.getY0(),
frame=frame,
ctype=ctype)
clumps = psfHist.getClumps(display=display)
#
# Go through and find all the PSF-like objects
#
# We'll split the image into a number of cells, each of which contributes only
# one PSF candidate star
#
psfCandidateList = []
# psf candidate shapes must lie within this many RMS of the average shape
# N.b. if Ixx == Iyy, Ixy = 0 the criterion is
# dx^2 + dy^2 < self.config.clumpNSigma*(Ixx + Iyy) == 2*self.config.clumpNSigma*Ixx
for source in catalog:
Ixx, Ixy, Iyy = source.getIxx(), source.getIxy(), source.getIyy()
if distorter:
xpix, ypix = source.getX() + xy0.getX(), source.getY(
) + xy0.getY()
p = afwGeom.Point2D(xpix, ypix)
m = distorter.undistort(p, geomEllip.Quadrupole(Ixx, Iyy, Ixy),
detector)
Ixx, Iyy, Ixy = m.getIxx(), m.getIyy(), m.getIxy()
x, y = psfHist.momentsToPixel(Ixx, Iyy)
for clump in clumps:
dx, dy = (x - clump.x), (y - clump.y)
if math.sqrt(clump.a * dx * dx + 2 * clump.b * dx * dy +
clump.c * dy * dy) < 2 * self.config.clumpNSigma:
# A test for > would be confused by NaN
if not isGoodSource(source):
continue
try:
psfCandidate = algorithmsLib.makePsfCandidate(
source, exposure)
# The setXXX methods are class static, but it's convenient to call them on
# an instance as we don't know Exposure's pixel type
# (and hence psfCandidate's exact type)
if psfCandidate.getWidth() == 0:
psfCandidate.setBorderWidth(
self.config.borderWidth)
psfCandidate.setWidth(self.config.kernelSize +
2 * self.config.borderWidth)
psfCandidate.setHeight(self.config.kernelSize +
2 * self.config.borderWidth)
im = psfCandidate.getMaskedImage().getImage()
if not numpy.isfinite(
afwMath.makeStatistics(
im, afwMath.MAX).getValue()):
continue
psfCandidateList.append(psfCandidate)
if display and displayExposure:
ds9.dot("o",
source.getX() - mi.getX0(),
source.getY() - mi.getY0(),
size=4,
frame=frame,
ctype=ds9.CYAN)
except Exception as err:
pass # FIXME: should log this!
break
return psfCandidateList
def testHsmPsfMomentsDebiased(self, width, useSourceCentroidOffset,
varyBBox, wrongBBox, center):
# As a note, it's really hard to actually unit test whether we've
# succesfully "debiased" these measurements. That would require a
# many-object comparison of moments with and without noise. So we just
# test similar to the biased moments above.
var = 1.2
# As we reduce the flux, our deviation from the expected value
# increases, so decrease tolerance.
for flux, decimals in [
(1e6, 3),
(1e4, 1),
(1e3, 0),
]:
psf = PyGaussianPsf(35,
35,
width,
varyBBox=varyBBox,
wrongBBox=wrongBBox)
exposure = afwImage.ExposureF(45, 56)
exposure.getMaskedImage().set(1.0, 0, var)
exposure.setPsf(psf)
# perform the shape measurement
control = lsst.meas.extensions.shapeHSM.HsmPsfMomentsDebiasedControl(
)
self.assertTrue(control.useSourceCentroidOffset)
self.assertEqual(control.noiseSource, "variance")
control.useSourceCentroidOffset = useSourceCentroidOffset
plugin, cat = makePluginAndCat(
lsst.meas.extensions.shapeHSM.HsmPsfMomentsDebiasedAlgorithm,
"ext_shapeHSM_HsmPsfMomentsDebiased",
centroid="centroid",
psfflux="base_PsfFlux",
control=control)
source = cat.addNew()
source.set("centroid_x", center[0])
source.set("centroid_y", center[1])
offset = geom.Point2I(*center)
source.set("base_PsfFlux_instFlux", flux)
tmpSpans = afwGeom.SpanSet.fromShape(int(width), offset=offset)
source.setFootprint(afwDetection.Footprint(tmpSpans))
plugin.measure(source, exposure)
x = source.get("ext_shapeHSM_HsmPsfMomentsDebiased_x")
y = source.get("ext_shapeHSM_HsmPsfMomentsDebiased_y")
xx = source.get("ext_shapeHSM_HsmPsfMomentsDebiased_xx")
yy = source.get("ext_shapeHSM_HsmPsfMomentsDebiased_yy")
xy = source.get("ext_shapeHSM_HsmPsfMomentsDebiased_xy")
for flag in [
"ext_shapeHSM_HsmPsfMomentsDebiased_flag",
"ext_shapeHSM_HsmPsfMomentsDebiased_flag_no_pixels",
"ext_shapeHSM_HsmPsfMomentsDebiased_flag_not_contained",
"ext_shapeHSM_HsmPsfMomentsDebiased_flag_parent_source",
"ext_shapeHSM_HsmPsfMomentsDebiased_flag_edge"
]:
self.assertFalse(source.get(flag))
expected = afwEll.Quadrupole(afwEll.Axes(width, width, 0.0))
self.assertAlmostEqual(x, 0.0, decimals)
self.assertAlmostEqual(y, 0.0, decimals)
T = expected.getIxx() + expected.getIyy()
self.assertAlmostEqual((xx - expected.getIxx()) / T, 0.0, decimals)
self.assertAlmostEqual((xy - expected.getIxy()) / T, 0.0, decimals)
self.assertAlmostEqual((yy - expected.getIyy()) / T, 0.0, decimals)
# Repeat using noiseSource='meta'. Should get nearly the same
# results if BGMEAN is set to `var` above.
exposure2 = afwImage.ExposureF(45, 56)
# set the variance plane to something else to ensure we're
# ignoring it
exposure2.getMaskedImage().set(1.0, 0, 2 * var + 1.1)
exposure2.setPsf(psf)
exposure2.getMetadata().set("BGMEAN", var)
control2 = lsst.meas.extensions.shapeHSM.HsmPsfMomentsDebiasedControl(
)
control2.noiseSource = "meta"
control2.useSourceCentroidOffset = useSourceCentroidOffset
plugin2, cat2 = makePluginAndCat(
lsst.meas.extensions.shapeHSM.HsmPsfMomentsDebiasedAlgorithm,
"ext_shapeHSM_HsmPsfMomentsDebiased",
centroid="centroid",
psfflux="base_PsfFlux",
control=control2)
source2 = cat2.addNew()
source2.set("centroid_x", center[0])
source2.set("centroid_y", center[1])
offset2 = geom.Point2I(*center)
source2.set("base_PsfFlux_instFlux", flux)
tmpSpans2 = afwGeom.SpanSet.fromShape(int(width), offset=offset2)
source2.setFootprint(afwDetection.Footprint(tmpSpans2))
plugin2.measure(source2, exposure2)
x2 = source2.get("ext_shapeHSM_HsmPsfMomentsDebiased_x")
y2 = source2.get("ext_shapeHSM_HsmPsfMomentsDebiased_y")
xx2 = source2.get("ext_shapeHSM_HsmPsfMomentsDebiased_xx")
yy2 = source2.get("ext_shapeHSM_HsmPsfMomentsDebiased_yy")
xy2 = source2.get("ext_shapeHSM_HsmPsfMomentsDebiased_xy")
for flag in [
"ext_shapeHSM_HsmPsfMomentsDebiased_flag",
"ext_shapeHSM_HsmPsfMomentsDebiased_flag_no_pixels",
"ext_shapeHSM_HsmPsfMomentsDebiased_flag_not_contained",
"ext_shapeHSM_HsmPsfMomentsDebiased_flag_parent_source",
"ext_shapeHSM_HsmPsfMomentsDebiased_flag_edge"
]:
self.assertFalse(source.get(flag))
# Would be identically equal, but variance input via "BGMEAN" is
# consumed in c++ as a double, where variance from the variance
# plane is a c++ float.
self.assertAlmostEqual(x, x2, 8)
self.assertAlmostEqual(y, y2, 8)
self.assertAlmostEqual(xx, xx2, 5)
self.assertAlmostEqual(xy, xy2, 5)
self.assertAlmostEqual(yy, yy2, 5)
def plantSources(x0, y0, nx, ny, sky, nObj, wid, detector, useRandom=False):
tanSys = detector.makeCameraSys(cameraGeom.TAN_PIXELS)
pixToTanXYTransform = detector.getTransformMap()[tanSys]
img0 = afwImage.ImageF(afwGeom.ExtentI(nx, ny))
img = afwImage.ImageF(afwGeom.ExtentI(nx, ny))
ixx0, iyy0, ixy0 = wid*wid, wid*wid, 0.0
edgeBuffer = 40.0*wid
flux = 1.0e4
nkx, nky = int(10*wid) + 1, int(10*wid) + 1
xhwid,yhwid = nkx/2, nky/2
nRow = int(math.sqrt(nObj))
xstep = (nx - 1 - 0.0*edgeBuffer)/(nRow+1)
ystep = (ny - 1 - 0.0*edgeBuffer)/(nRow+1)
if useRandom:
nObj = nRow*nRow
goodAdded0 = []
goodAdded = []
for i in range(nObj):
# get our position
if useRandom:
xcen0, ycen0 = numpy.random.uniform(nx), numpy.random.uniform(ny)
else:
xcen0, ycen0 = xstep*((i%nRow) + 1), ystep*(int(i/nRow) + 1)
ixcen0, iycen0 = int(xcen0), int(ycen0)
# distort position and shape
pTan = afwGeom.Point2D(xcen0, ycen0)
linTransform = pixToTanXYTransform.linearizeReverseTransform(pTan).getLinear()
m = geomEllip.Quadrupole(ixx0, iyy0, ixy0)
m.transform(linTransform)
p = pixToTanXYTransform.reverseTransform(pTan)
xcen, ycen = xcen0, ycen0 #p.getX(), p.getY()
if (xcen < 1.0*edgeBuffer or (nx - xcen) < 1.0*edgeBuffer or
ycen < 1.0*edgeBuffer or (ny - ycen) < 1.0*edgeBuffer):
continue
ixcen, iycen = int(xcen), int(ycen)
ixx, iyy, ixy = m.getIxx(), m.getIyy(), m.getIxy()
# plant the object
tmp = 0.25*(ixx-iyy)**2 + ixy**2
a2 = 0.5*(ixx+iyy) + numpy.sqrt(tmp)
b2 = 0.5*(ixx+iyy) - numpy.sqrt(tmp)
#ellip = 1.0 - numpy.sqrt(b2/a2)
theta = 0.5*numpy.arctan2(2.0*ixy, ixx-iyy)
a = numpy.sqrt(a2)
b = numpy.sqrt(b2)
c, s = math.cos(theta), math.sin(theta)
good0, good = True, True
for y in range(nky):
iy = iycen + y - yhwid
iy0 = iycen0 + y - yhwid
for x in range(nkx):
ix = ixcen + x - xhwid
ix0 = ixcen0 + x - xhwid
if ix >= 0 and ix < nx and iy >= 0 and iy < ny:
dx, dy = ix - xcen, iy - ycen
u = c*dx + s*dy
v = -s*dx + c*dy
I0 = flux/(2*math.pi*a*b)
val = I0*math.exp(-0.5*((u/a)**2 + (v/b)**2))
if val < 0:
val = 0
prevVal = img.get(ix, iy)
img.set(ix, iy, val+prevVal)
else:
good = False
if ix0 >=0 and ix0 < nx and iy0 >= 0 and iy0 < ny:
dx, dy = ix - xcen, iy - ycen
I0 = flux/(2*math.pi*wid*wid)
val = I0*math.exp(-0.5*((dx/wid)**2 + (dy/wid)**2))
if val < 0:
val = 0
prevVal = img0.get(ix0, iy0)
img0.set(ix0, iy0, val+prevVal)
else:
good0 = False
if good0:
goodAdded0.append([xcen,ycen])
if good:
goodAdded.append([xcen,ycen])
# add sky and noise
img += sky
img0 += sky
noise = afwImage.ImageF(afwGeom.ExtentI(nx, ny))
noise0 = afwImage.ImageF(afwGeom.ExtentI(nx, ny))
for i in range(nx):
for j in range(ny):
noise.set(i, j, numpy.random.poisson(img.get(i,j) ))
noise0.set(i, j, numpy.random.poisson(img0.get(i,j) ))
edgeWidth = int(0.5*edgeBuffer)
mask = afwImage.MaskU(afwGeom.ExtentI(nx, ny))
left = afwGeom.Box2I(afwGeom.Point2I(0,0), afwGeom.ExtentI(edgeWidth, ny))
right = afwGeom.Box2I(afwGeom.Point2I(nx - edgeWidth,0), afwGeom.ExtentI(edgeWidth, ny))
top = afwGeom.Box2I(afwGeom.Point2I(0,ny - edgeWidth), afwGeom.ExtentI(nx, edgeWidth))
bottom = afwGeom.Box2I(afwGeom.Point2I(0,0), afwGeom.ExtentI(nx, edgeWidth))
for pos in [left, right, top, bottom]:
msk = afwImage.MaskU(mask, pos, False)
msk.set(msk.getPlaneBitMask('EDGE'))
expos = afwImage.makeExposure(afwImage.makeMaskedImage(noise, mask, afwImage.ImageF(noise, True)))
expos0 = afwImage.makeExposure(afwImage.makeMaskedImage(noise0, mask, afwImage.ImageF(noise0, True)))
im = expos.getMaskedImage().getImage()
im0 = expos0.getMaskedImage().getImage()
im -= sky
im0 -= sky
return expos, goodAdded, expos0, goodAdded0
def roundTrip(self, dist, *args):
if len(args) == 2:
x, y = args
p = afwGeom.Point2D(x, y)
pDist = dist.distort(p, self.det)
pp = dist.undistort(pDist, self.det)
if self.prynt:
print "p: %.12f %.12f" % (p.getX(), p.getY())
print "pDist: %.12f %.12f" % (pDist.getX(), pDist.getY())
print "pp: %.12f %.12f" % (pp.getX(), pp.getY())
self.assertAlmostEqual(pp.getX(), p.getX())
self.assertAlmostEqual(pp.getY(), p.getY())
if len(args) == 3:
x, y, m = args
ixx, iyy, ixy = m
p = afwGeom.Point2D(x, y)
pDist = dist.distort(p, self.det)
m = geomEllip.Quadrupole(ixx, iyy, ixy)
mDist = dist.distort(p, m, self.det)
mm = dist.undistort(pDist, mDist, self.det)
r0 = math.sqrt(x * x + y * y)
theta = math.atan2(y, x)
#dx = 0.001*math.cos(theta)
#dy = 0.001*math.sin(theta)
#dr = math.sqrt(dx*dx+dy*dy)
scale = 1.0000001
p2 = afwGeom.Point2D(scale * x, scale * y)
p2Dist = dist.distort(p2, self.det)
r1 = math.sqrt(pDist.getX() * pDist.getX() +
pDist.getY() * pDist.getY())
r2 = math.sqrt(p2Dist.getX() * p2Dist.getX() +
p2Dist.getY() * p2Dist.getY())
if r0 > 0:
drTest = (r2 - r1) / ((scale - 1.0) * r0)
else:
drTest = 0.0
if self.prynt:
print "m: %.12f %.12f %.12f" % (m.getIxx(), m.getIyy(),
m.getIxy())
print "mDist: %.12f %.12f %.12f" % (
mDist.getIxx(), mDist.getIyy(), mDist.getIxy())
print "mp: %.12f %.12f %.12f" % (mm.getIxx(), mm.getIyy(),
mm.getIxy())
ixyTmp = m.getIxy() if m.getIxy() != 0.0 else 1.0
print "err: %.12f %.12f %.12f" % (
(m.getIxx() - mm.getIxx()) / m.getIxx(),
(m.getIyy() - mm.getIyy()) / m.getIyy(),
(m.getIxy() - mm.getIxy()) / ixyTmp,
)
self.assertAlmostEqual(mm.getIxx(), m.getIxx())
self.assertAlmostEqual(mm.getIyy(), m.getIyy())
self.assertAlmostEqual(mm.getIxy(), m.getIxy())
def determinePsf(self,
exposure,
psfCandidateList,
metadata=None,
flagKey=None):
"""!Determine a PCA PSF model for an exposure given a list of PSF candidates
\param[in] exposure exposure containing the psf candidates (lsst.afw.image.Exposure)
\param[in] psfCandidateList a sequence of PSF candidates (each an lsst.meas.algorithms.PsfCandidate);
typically obtained by detecting sources and then running them through a star selector
\param[in,out] metadata a home for interesting tidbits of information
\param[in] flagKey schema key used to mark sources actually used in PSF determination
\return a list of
- psf: the measured PSF, an lsst.meas.algorithms.PcaPsf
- cellSet: an lsst.afw.math.SpatialCellSet containing the PSF candidates
"""
import lsstDebug
display = lsstDebug.Info(__name__).display
displayExposure = lsstDebug.Info(
__name__).displayExposure # display the Exposure + spatialCells
displayPsfCandidates = lsstDebug.Info(
__name__).displayPsfCandidates # show the viable candidates
displayIterations = lsstDebug.Info(
__name__).displayIterations # display on each PSF iteration
displayPsfComponents = lsstDebug.Info(
__name__).displayPsfComponents # show the PCA components
displayResiduals = lsstDebug.Info(
__name__).displayResiduals # show residuals
displayPsfMosaic = lsstDebug.Info(
__name__).displayPsfMosaic # show mosaic of reconstructed PSF(x,y)
# match Kernel amplitudes for spatial plots
matchKernelAmplitudes = lsstDebug.Info(__name__).matchKernelAmplitudes
# Keep matplotlib alive post mortem
keepMatplotlibPlots = lsstDebug.Info(__name__).keepMatplotlibPlots
displayPsfSpatialModel = lsstDebug.Info(
__name__).displayPsfSpatialModel # Plot spatial model?
showBadCandidates = lsstDebug.Info(
__name__).showBadCandidates # Include bad candidates
# Normalize residuals by object amplitude
normalizeResiduals = lsstDebug.Info(__name__).normalizeResiduals
pause = lsstDebug.Info(
__name__).pause # Prompt user after each iteration?
if display > 1:
pause = True
mi = exposure.getMaskedImage()
if len(psfCandidateList) == 0:
raise RuntimeError("No PSF candidates supplied.")
# construct and populate a spatial cell set
bbox = mi.getBBox()
psfCellSet = afwMath.SpatialCellSet(bbox, self.config.sizeCellX,
self.config.sizeCellY)
sizes = []
for i, psfCandidate in enumerate(psfCandidateList):
if psfCandidate.getSource().getPsfFluxFlag(): # bad measurement
continue
try:
psfCellSet.insertCandidate(psfCandidate)
except Exception as e:
self.log.debug("Skipping PSF candidate %d of %d: %s", i,
len(psfCandidateList), e)
continue
source = psfCandidate.getSource()
quad = afwEll.Quadrupole(source.getIxx(), source.getIyy(),
source.getIxy())
axes = afwEll.Axes(quad)
sizes.append(axes.getA())
if len(sizes) == 0:
raise RuntimeError("No usable PSF candidates supplied")
nEigenComponents = self.config.nEigenComponents # initial version
if self.config.kernelSize >= 15:
self.log.warn(
"WARNING: NOT scaling kernelSize by stellar quadrupole moment "
+
"because config.kernelSize=%s >= 15; using config.kernelSize as as the width, instead",
self.config.kernelSize)
actualKernelSize = int(self.config.kernelSize)
else:
medSize = numpy.median(sizes)
actualKernelSize = 2 * int(self.config.kernelSize *
math.sqrt(medSize) + 0.5) + 1
if actualKernelSize < self.config.kernelSizeMin:
actualKernelSize = self.config.kernelSizeMin
if actualKernelSize > self.config.kernelSizeMax:
actualKernelSize = self.config.kernelSizeMax
if display:
print("Median size=%s" % (medSize, ))
self.log.trace("Kernel size=%s", actualKernelSize)
# Set size of image returned around candidate
psfCandidateList[0].setHeight(actualKernelSize)
psfCandidateList[0].setWidth(actualKernelSize)
if self.config.doRejectBlends:
# Remove blended candidates completely
blendedCandidates = [
] # Candidates to remove; can't do it while iterating
for cell, cand in candidatesIter(psfCellSet, False):
if len(cand.getSource().getFootprint().getPeaks()) > 1:
blendedCandidates.append((cell, cand))
continue
if display:
print("Removing %d blended Psf candidates" %
len(blendedCandidates))
for cell, cand in blendedCandidates:
cell.removeCandidate(cand)
if sum(1 for cand in candidatesIter(psfCellSet, False)) == 0:
raise RuntimeError("All PSF candidates removed as blends")
if display:
frame = 0
if displayExposure:
ds9.mtv(exposure, frame=frame, title="psf determination")
maUtils.showPsfSpatialCells(exposure,
psfCellSet,
self.config.nStarPerCell,
symb="o",
ctype=ds9.CYAN,
ctypeUnused=ds9.YELLOW,
size=4,
frame=frame)
#
# Do a PCA decomposition of those PSF candidates
#
reply = "y" # used in interactive mode
for iterNum in range(self.config.nIterForPsf):
if display and displayPsfCandidates: # Show a mosaic of usable PSF candidates
#
import lsst.afw.display.utils as displayUtils
stamps = []
for cell in psfCellSet.getCellList():
for cand in cell.begin(not showBadCandidates
): # maybe include bad candidates
try:
im = cand.getMaskedImage()
chi2 = cand.getChi2()
if chi2 > 1e100:
chi2 = numpy.nan
stamps.append(
(im, "%d%s" %
(maUtils.splitId(cand.getSource().getId(),
True)["objId"], chi2),
cand.getStatus()))
except Exception as e:
continue
if len(stamps) == 0:
print(
"WARNING: No PSF candidates to show; try setting showBadCandidates=True"
)
else:
mos = displayUtils.Mosaic()
for im, label, status in stamps:
im = type(im)(im, True)
try:
im /= afwMath.makeStatistics(
im, afwMath.MAX).getValue()
except NotImplementedError:
pass
mos.append(
im, label, ds9.GREEN
if status == afwMath.SpatialCellCandidate.GOOD else
ds9.YELLOW if status ==
afwMath.SpatialCellCandidate.UNKNOWN else ds9.RED)
mos.makeMosaic(frame=8, title="Psf Candidates")
# Re-fit until we don't have any candidates with naughty chi^2 values influencing the fit
cleanChi2 = False # Any naughty (negative/NAN) chi^2 values?
while not cleanChi2:
cleanChi2 = True
#
# First, estimate the PSF
#
psf, eigenValues, nEigenComponents, fitChi2 = \
self._fitPsf(exposure, psfCellSet, actualKernelSize, nEigenComponents)
#
# In clipping, allow all candidates to be innocent until proven guilty on this iteration.
# Throw out any prima facie guilty candidates (naughty chi^2 values)
#
for cell in psfCellSet.getCellList():
awfulCandidates = []
for cand in cell.begin(False): # include bad candidates
cand.setStatus(afwMath.SpatialCellCandidate.UNKNOWN
) # until proven guilty
rchi2 = cand.getChi2()
if not numpy.isfinite(rchi2) or rchi2 <= 0:
# Guilty prima facie
awfulCandidates.append(cand)
cleanChi2 = False
self.log.debug("chi^2=%s; id=%s", cand.getChi2(),
cand.getSource().getId())
for cand in awfulCandidates:
if display:
print("Removing bad candidate: id=%d, chi^2=%f" % \
(cand.getSource().getId(), cand.getChi2()))
cell.removeCandidate(cand)
#
# Clip out bad fits based on reduced chi^2
#
badCandidates = list()
for cell in psfCellSet.getCellList():
for cand in cell.begin(False): # include bad candidates
rchi2 = cand.getChi2(
) # reduced chi^2 when fitting PSF to candidate
assert rchi2 > 0
if rchi2 > self.config.reducedChi2ForPsfCandidates:
badCandidates.append(cand)
badCandidates.sort(key=lambda x: x.getChi2(), reverse=True)
numBad = numCandidatesToReject(len(badCandidates), iterNum,
self.config.nIterForPsf)
for i, c in zip(range(numBad), badCandidates):
if display:
chi2 = c.getChi2()
if chi2 > 1e100:
chi2 = numpy.nan
print("Chi^2 clipping %-4d %.2g" %
(c.getSource().getId(), chi2))
c.setStatus(afwMath.SpatialCellCandidate.BAD)
#
# Clip out bad fits based on spatial fitting.
#
# This appears to be better at getting rid of sources that have a single dominant kernel component
# (other than the zeroth; e.g., a nearby contaminant) because the surrounding sources (which help
# set the spatial model) don't contain that kernel component, and so the spatial modeling
# downweights the component.
#
residuals = list()
candidates = list()
kernel = psf.getKernel()
noSpatialKernel = psf.getKernel()
for cell in psfCellSet.getCellList():
for cand in cell.begin(False):
candCenter = afwGeom.PointD(cand.getXCenter(),
cand.getYCenter())
try:
im = cand.getMaskedImage(kernel.getWidth(),
kernel.getHeight())
except Exception as e:
continue
fit = fitKernelParamsToImage(noSpatialKernel, im,
candCenter)
params = fit[0]
kernels = fit[1]
amp = 0.0
for p, k in zip(params, kernels):
amp += p * k.getSum()
predict = [
kernel.getSpatialFunction(k)(candCenter.getX(),
candCenter.getY())
for k in range(kernel.getNKernelParameters())
]
#print cand.getSource().getId(), [a / amp for a in params], predict
residuals.append(
[a / amp - p for a, p in zip(params, predict)])
candidates.append(cand)
residuals = numpy.array(residuals)
for k in range(kernel.getNKernelParameters()):
if False:
# Straight standard deviation
mean = residuals[:, k].mean()
rms = residuals[:, k].std()
elif False:
# Using interquartile range
sr = numpy.sort(residuals[:, k])
mean = sr[int(0.5*len(sr))] if len(sr) % 2 else \
0.5 * (sr[int(0.5*len(sr))] + sr[int(0.5*len(sr))+1])
rms = 0.74 * (sr[int(0.75 * len(sr))] -
sr[int(0.25 * len(sr))])
else:
stats = afwMath.makeStatistics(
residuals[:, k], afwMath.MEANCLIP | afwMath.STDEVCLIP)
mean = stats.getValue(afwMath.MEANCLIP)
rms = stats.getValue(afwMath.STDEVCLIP)
rms = max(
1.0e-4,
rms) # Don't trust RMS below this due to numerical issues
if display:
print("Mean for component %d is %f" % (k, mean))
print("RMS for component %d is %f" % (k, rms))
badCandidates = list()
for i, cand in enumerate(candidates):
if numpy.fabs(residuals[i, k] -
mean) > self.config.spatialReject * rms:
badCandidates.append(i)
badCandidates.sort(
key=lambda x: numpy.fabs(residuals[x, k] - mean),
reverse=True)
numBad = numCandidatesToReject(len(badCandidates), iterNum,
self.config.nIterForPsf)
for i, c in zip(range(min(len(badCandidates), numBad)),
badCandidates):
cand = candidates[c]
if display:
print("Spatial clipping %d (%f,%f) based on %d: %f vs %f" % \
(cand.getSource().getId(), cand.getXCenter(), cand.getYCenter(), k,
residuals[badCandidates[i], k], self.config.spatialReject * rms))
cand.setStatus(afwMath.SpatialCellCandidate.BAD)
#
# Display results
#
if display and displayIterations:
if displayExposure:
if iterNum > 0:
ds9.erase(frame=frame)
maUtils.showPsfSpatialCells(exposure,
psfCellSet,
self.config.nStarPerCell,
showChi2=True,
symb="o",
size=8,
frame=frame,
ctype=ds9.YELLOW,
ctypeBad=ds9.RED,
ctypeUnused=ds9.MAGENTA)
if self.config.nStarPerCellSpatialFit != self.config.nStarPerCell:
maUtils.showPsfSpatialCells(
exposure,
psfCellSet,
self.config.nStarPerCellSpatialFit,
symb="o",
size=10,
frame=frame,
ctype=ds9.YELLOW,
ctypeBad=ds9.RED)
if displayResiduals:
while True:
try:
maUtils.showPsfCandidates(
exposure,
psfCellSet,
psf=psf,
frame=4,
normalize=normalizeResiduals,
showBadCandidates=showBadCandidates)
maUtils.showPsfCandidates(
exposure,
psfCellSet,
psf=psf,
frame=5,
normalize=normalizeResiduals,
showBadCandidates=showBadCandidates,
variance=True)
except:
if not showBadCandidates:
showBadCandidates = True
continue
break
if displayPsfComponents:
maUtils.showPsf(psf, eigenValues, frame=6)
if displayPsfMosaic:
maUtils.showPsfMosaic(exposure,
psf,
frame=7,
showFwhm=True)
ds9.scale('linear', 0, 1, frame=7)
if displayPsfSpatialModel:
maUtils.plotPsfSpatialModel(
exposure,
psf,
psfCellSet,
showBadCandidates=True,
matchKernelAmplitudes=matchKernelAmplitudes,
keepPlots=keepMatplotlibPlots)
if pause:
while True:
try:
reply = input(
"Next iteration? [ynchpqQs] ").strip()
except EOFError:
reply = "n"
reply = reply.split()
if reply:
reply, args = reply[0], reply[1:]
else:
reply = ""
if reply in ("", "c", "h", "n", "p", "q", "Q", "s",
"y"):
if reply == "c":
pause = False
elif reply == "h":
print("c[ontinue without prompting] h[elp] n[o] p[db] q[uit displaying] " \
"s[ave fileName] y[es]")
continue
elif reply == "p":
import pdb
pdb.set_trace()
elif reply == "q":
display = False
elif reply == "Q":
sys.exit(1)
elif reply == "s":
fileName = args.pop(0)
if not fileName:
print("Please provide a filename")
continue
print("Saving to %s" % fileName)
maUtils.saveSpatialCellSet(psfCellSet,
fileName=fileName)
continue
break
else:
print("Unrecognised response: %s" % reply,
file=sys.stderr)
if reply == "n":
break
# One last time, to take advantage of the last iteration
psf, eigenValues, nEigenComponents, fitChi2 = \
self._fitPsf(exposure, psfCellSet, actualKernelSize, nEigenComponents)
#
# Display code for debugging
#
if display and reply != "n":
if displayExposure:
maUtils.showPsfSpatialCells(exposure,
psfCellSet,
self.config.nStarPerCell,
showChi2=True,
symb="o",
ctype=ds9.YELLOW,
ctypeBad=ds9.RED,
size=8,
frame=frame)
if self.config.nStarPerCellSpatialFit != self.config.nStarPerCell:
maUtils.showPsfSpatialCells(
exposure,
psfCellSet,
self.config.nStarPerCellSpatialFit,
symb="o",
ctype=ds9.YELLOW,
ctypeBad=ds9.RED,
size=10,
frame=frame)
if displayResiduals:
maUtils.showPsfCandidates(
exposure,
psfCellSet,
psf=psf,
frame=4,
normalize=normalizeResiduals,
showBadCandidates=showBadCandidates)
if displayPsfComponents:
maUtils.showPsf(psf, eigenValues, frame=6)
if displayPsfMosaic:
maUtils.showPsfMosaic(exposure, psf, frame=7, showFwhm=True)
ds9.scale("linear", 0, 1, frame=7)
if displayPsfSpatialModel:
maUtils.plotPsfSpatialModel(
exposure,
psf,
psfCellSet,
showBadCandidates=True,
matchKernelAmplitudes=matchKernelAmplitudes,
keepPlots=keepMatplotlibPlots)
#
# Generate some QA information
#
# Count PSF stars
#
numGoodStars = 0
numAvailStars = 0
avgX = 0.0
avgY = 0.0
for cell in psfCellSet.getCellList():
for cand in cell.begin(False): # don't ignore BAD stars
numAvailStars += 1
for cand in cell.begin(True): # do ignore BAD stars
src = cand.getSource()
if flagKey is not None:
src.set(flagKey, True)
avgX += src.getX()
avgY += src.getY()
numGoodStars += 1
avgX /= numGoodStars
avgY /= numGoodStars
if metadata is not None:
metadata.set("spatialFitChi2", fitChi2)
metadata.set("numGoodStars", numGoodStars)
metadata.set("numAvailStars", numAvailStars)
metadata.set("avgX", avgX)
metadata.set("avgY", avgY)
psf = PcaPsf(psf.getKernel(), afwGeom.Point2D(avgX, avgY))
return psf, psfCellSet
def determinePsf(self,
exposure,
psfCandidateList,
metadata=None,
flagKey=None):
"""!Determine a PCA PSF model for an exposure given a list of PSF candidates
\param[in] exposure exposure containing the psf candidates (lsst.afw.image.Exposure)
\param[in] psfCandidateList a sequence of PSF candidates (each an lsst.meas.algorithms.PsfCandidate);
typically obtained by detecting sources and then running them through a star selector
\param[in,out] metadata a home for interesting tidbits of information
\param[in] flagKey schema key used to mark sources actually used in PSF determination
\return a list of
- psf: the measured PSF, an lsst.meas.algorithms.PcaPsf
- cellSet: an lsst.afw.math.SpatialCellSet containing the PSF candidates
"""
import lsstDebug
display = lsstDebug.Info(__name__).display
displayExposure = lsstDebug.Info(
__name__).displayExposure # display the Exposure + spatialCells
displayPsfCandidates = lsstDebug.Info(
__name__).displayPsfCandidates # show the viable candidates
displayIterations = lsstDebug.Info(
__name__).displayIterations # display on each PSF iteration
displayPsfComponents = lsstDebug.Info(
__name__).displayPsfComponents # show the PCA components
displayResiduals = lsstDebug.Info(
__name__).displayResiduals # show residuals
displayPsfMosaic = lsstDebug.Info(
__name__).displayPsfMosaic # show mosaic of reconstructed PSF(x,y)
matchKernelAmplitudes = lsstDebug.Info(
__name__).matchKernelAmplitudes # match Kernel amplitudes
# for spatial plots
keepMatplotlibPlots = lsstDebug.Info(
__name__).keepMatplotlibPlots # Keep matplotlib alive
# post mortem
displayPsfSpatialModel = lsstDebug.Info(
__name__).displayPsfSpatialModel # Plot spatial model?
showBadCandidates = lsstDebug.Info(
__name__).showBadCandidates # Include bad candidates
normalizeResiduals = lsstDebug.Info(
__name__).normalizeResiduals # Normalise residuals by
# object amplitude
pause = lsstDebug.Info(
__name__).pause # Prompt user after each iteration?
if display > 1:
pause = True
mi = exposure.getMaskedImage()
if len(psfCandidateList) == 0:
raise RuntimeError("No PSF candidates supplied.")
# construct and populate a spatial cell set
bbox = mi.getBBox()
psfCellSet = afwMath.SpatialCellSet(bbox, self.config.sizeCellX,
self.config.sizeCellY)
sizes = []
for i, psfCandidate in enumerate(psfCandidateList):
if psfCandidate.getSource().getPsfFluxFlag(): # bad measurement
continue
try:
psfCellSet.insertCandidate(psfCandidate)
except Exception, e:
self.debugLog.debug(
2, "Skipping PSF candidate %d of %d: %s" %
(i, len(psfCandidateList), e))
continue
source = psfCandidate.getSource()
quad = afwEll.Quadrupole(source.getIxx(), source.getIyy(),
source.getIxy())
axes = afwEll.Axes(quad)
sizes.append(axes.getA())
