def showDiaSources(sources, exposure, isFlagged, isDipole, frame=None):
"""Display Dia Sources
"""
#
# Show us the ccandidates
#
# Too many mask planes in diffims
for plane in ("BAD", "CR", "EDGE", "INTERPOlATED", "INTRP", "SAT",
"SATURATED"):
ds9.setMaskPlaneVisibility(plane, False)
mos = displayUtils.Mosaic()
for i in range(len(sources)):
source = sources[i]
badFlag = isFlagged[i]
dipoleFlag = isDipole[i]
bbox = source.getFootprint().getBBox()
stamp = exposure.Factory(exposure, bbox, True)
im = displayUtils.Mosaic(gutter=1, background=0, mode="x")
im.append(stamp.getMaskedImage())
lab = "%.1f,%.1f:" % (source.getX(), source.getY())
if badFlag:
ctype = ds9.RED
lab += "BAD"
if dipoleFlag:
ctype = ds9.YELLOW
lab += "DIPOLE"
if not badFlag and not dipoleFlag:
ctype = ds9.GREEN
lab += "OK"
mos.append(im.makeMosaic(), lab, ctype)
title = "Dia Sources"
mosaicImage = mos.makeMosaic(frame=frame, title=title)
return mosaicImage
def testPca(self):
"""Test calculating PCA"""
random.seed(0)
width, height = 200, 100
numBases = 3
numInputs = 3
bases = []
for i in range(numBases):
im = afwImage.ImageF(width, height)
array = im.getArray()
x, y = np.indices(array.shape)
period = 5 * (i + 1)
fx = np.sin(2 * math.pi / period * x + 2 * math.pi / numBases * i)
fy = np.sin(2 * math.pi / period * y + 2 * math.pi / numBases * i)
array[x, y] = fx + fy
bases.append(im)
if display:
mos = displayUtils.Mosaic(background=-10)
ds9.mtv(mos.makeMosaic(bases), title="Basis functions", frame=1)
inputs = []
for i in range(numInputs):
im = afwImage.ImageF(afwGeom.Extent2I(width, height))
im.set(0)
for b in bases:
im.scaledPlus(random.random(), b)
inputs.append(im)
self.ImageSet.addImage(im, 1.0)
if display:
mos = displayUtils.Mosaic(background=-10)
ds9.mtv(mos.makeMosaic(inputs), title="Inputs", frame=2)
self.ImageSet.analyze()
eImages = []
for img in self.ImageSet.getEigenImages():
eImages.append(img)
if display:
mos = displayUtils.Mosaic(background=-10)
ds9.mtv(mos.makeMosaic(eImages), title="Eigenimages", frame=3)
self.assertEqual(len(eImages), numInputs)
# Test for orthogonality
for i1, i2 in itertools.combinations(range(len(eImages)), 2):
inner = afwImage.innerProduct(eImages[i1], eImages[i2])
norm1 = eImages[i1].getArray().sum()
norm2 = eImages[i2].getArray().sum()
inner /= norm1 * norm2
self.assertAlmostEqual(inner, 0)
def testCandidateList(self):
self.assertFalse(self.cellSet.getCellList()[0].empty())
self.assertTrue(self.cellSet.getCellList()[1].empty())
self.assertFalse(self.cellSet.getCellList()[2].empty())
self.assertTrue(self.cellSet.getCellList()[3].empty())
stamps = []
stampInfo = []
for cell in self.cellSet.getCellList():
for cand in cell:
#
# Swig doesn't know that we inherited from SpatialCellMaskedImageCandidate; all
# it knows is that we have a SpatialCellCandidate, and SpatialCellCandidates
# don't know about getMaskedImage; so cast the pointer to SpatialCellMaskedImageCandidate<float>
# and all will be well
#
cand = afwMath.cast_SpatialCellMaskedImageCandidateF(cell[0])
width, height = 29, 25
cand.setWidth(width); cand.setHeight(height);
im = cand.getMaskedImage()
stamps.append(im)
self.assertEqual(im.getWidth(), width)
self.assertEqual(im.getHeight(), height)
if False and display:
mos = displayUtils.Mosaic()
mos.makeMosaic(stamps, frame=2)
def plotPsfCandidates(cellSet, showBadCandidates=False, frame=1):
import lsst.afw.display.utils as displayUtils
stamps = []
for cell in cellSet.getCellList():
for cand in cell.begin(not showBadCandidates): # maybe include bad candidates
try:
im = cand.getMaskedImage()
chi2 = cand.getChi2()
if chi2 < 1e100:
chi2 = "%.1f" % chi2
else:
chi2 = float("nan")
stamps.append((im, "%d%s" %
(maUtils.splitId(cand.getSource().getId(), True)["objId"], chi2),
cand.getStatus()))
except Exception:
continue
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)
if mos.images:
mos.makeMosaic(frame=frame, title="Psf Candidates")
def showPsf(psf, eigenValues=None, XY=None, normalize=True, display=None):
"""Display a PSF's eigen images
If normalize is True, set the largest absolute value of each eigenimage to 1.0 (n.b. sum == 0.0 for i > 0)
"""
if eigenValues:
coeffs = eigenValues
elif XY is not None:
coeffs = psf.getLocalKernel(lsst.geom.PointD(XY[0], XY[1])).getKernelParameters()
else:
coeffs = None
mos = displayUtils.Mosaic(gutter=2, background=-0.1)
for i, k in enumerate(psf.getKernel().getKernelList()):
im = afwImage.ImageD(k.getDimensions())
k.computeImage(im, False)
if normalize:
im /= numpy.max(numpy.abs(im.getArray()))
if coeffs:
mos.append(im, "%g" % (coeffs[i]/coeffs[0]))
else:
mos.append(im)
if not display:
display = afwDisplay.Display()
mos.makeMosaic(display=display, title="Kernel Basis Functions")
return mos
def testGetImage(self):
"""Test returning a realisation of the dgPsf"""
xcen = self.psf.getKernel().getWidth()//2
ycen = self.psf.getKernel().getHeight()//2
stamps = []
trueCenters = []
for x, y in ([10, 10], [9.4999, 10.4999], [10.5001, 10.5001]):
fx, fy = x - int(x), y - int(y)
if fx >= 0.5:
fx -= 1.0
if fy >= 0.5:
fy -= 1.0
im = self.psf.computeImage(afwGeom.Point2D(x, y)).convertF()
stamps.append(im.Factory(im, True))
trueCenters.append([xcen + fx, ycen + fy])
if display:
mos = displayUtils.Mosaic() # control mosaics
ds9.mtv(mos.makeMosaic(stamps))
for i in range(len(trueCenters)):
bbox = mos.getBBox(i)
ds9.dot("+",
bbox.getMinX() + xcen, bbox.getMinY() + ycen, ctype = ds9.RED, size = 1)
ds9.dot("+",
bbox.getMinX() + trueCenters[i][0], bbox.getMinY() + trueCenters[i][1])
ds9.dot("%.2f, %.2f" % (trueCenters[i][0], trueCenters[i][1]),
bbox.getMinX() + xcen, bbox.getMinY() + 2)
def testKernelPsf(self):
"""Test creating a Psf from a Kernel"""
x,y = 10.4999, 10.4999
ksize = 15
sigma1 = 1
#
# Make a PSF from that kernel
#
kPsf = measAlg.KernelPsf(afwMath.AnalyticKernel(ksize, ksize,
afwMath.GaussianFunction2D(sigma1, sigma1)))
kIm = kPsf.computeImage(afwGeom.Point2D(x, y))
#
# And now via the dgPsf model
#
dgPsf = measAlg.DoubleGaussianPsf(ksize, ksize, sigma1)
dgIm = dgPsf.computeImage(afwGeom.Point2D(x, y))
#
# Check that they're the same
#
diff = type(kIm)(kIm, True); diff -= dgIm
stats = afwMath.makeStatistics(diff, afwMath.MAX | afwMath.MIN)
self.assertAlmostEqual(stats.getValue(afwMath.MAX), 0.0, places=16)
self.assertAlmostEqual(stats.getValue(afwMath.MIN), 0.0, places=16)
if display:
mos = displayUtils.Mosaic()
mos.setBackground(-0.1)
ds9.mtv(mos.makeMosaic([kIm, dgIm, diff], mode="x"), frame=1)
def showPsf(psf, eigenValues=None, XY=None, normalize=True, frame=None):
"""Display a PSF's eigen images
If normalize is True, set the largest absolute value of each eigenimage to 1.0 (n.b. sum == 0.0 for i > 0)
"""
if eigenValues:
coeffs = eigenValues
elif XY is not None:
coeffs = psf.getLocalKernel(afwGeom.PointD(XY[0], XY[1])).getKernelParameters()
else:
coeffs = None
mos = displayUtils.Mosaic()
i = 0
for k in afwMath.cast_LinearCombinationKernel(psf.getKernel()).getKernelList():
im = afwImage.ImageD(k.getDimensions())
k.computeImage(im, False)
if normalize:
im /= numpy.max(numpy.abs(im.getArray()))
if coeffs:
mos.append(im, "%g" % (coeffs[i]/coeffs[0]))
i += 1
else:
mos.append(im)
mos.makeMosaic(frame=frame, title="Eigen Images")
return mos
def showKernelBasis(kernel, frame=None):
"""Display a Kernel's basis images
"""
mos = displayUtils.Mosaic()
for k in kernel.getKernelList():
im = afwImage.ImageD(k.getDimensions())
k.computeImage(im, False)
mos.append(im)
mos.makeMosaic(frame=frame, title="Kernel Basis Images")
return mos
def runBasicTest(self, kernel, convControl, refKernel=None, kernelDescr="", rtol=1.0e-05, atol=1e-08):
"""Assert that afwMath::convolve gives the same result as reference convolution for a given kernel.
Inputs:
- kernel: convolution kernel
- convControl: convolution control parameters (afwMath.ConvolutionControl)
- refKernel: kernel to use for refConvolve (if None then kernel is used)
- kernelDescr: description of kernel
- rtol: relative tolerance (see below)
- atol: absolute tolerance (see below)
rtol and atol are positive, typically very small numbers.
The relative difference (rtol * abs(b)) and the absolute difference "atol" are added together
to compare against the absolute difference between "a" and "b".
"""
if refKernel is None:
refKernel = kernel
# strip garbage characters (whitespace and punctuation) to make a short description for saving files
shortKernelDescr = self._removeGarbageChars(kernelDescr)
doNormalize = convControl.getDoNormalize()
doCopyEdge = convControl.getDoCopyEdge()
maxInterpDist = convControl.getMaxInterpolationDistance()
imMaskVar = self.maskedImage.getArrays()
xy0 = self.maskedImage.getXY0()
refCnvImMaskVarArr = refConvolve(imMaskVar, xy0, refKernel, doNormalize, doCopyEdge)
refMaskedImage = afwImage.makeMaskedImageFromArrays(*refCnvImMaskVarArr)
afwMath.convolve(self.cnvImage, self.maskedImage.getImage(), kernel, convControl)
self.assertEqual(self.cnvImage.getXY0(), self.xy0)
afwMath.convolve(self.cnvMaskedImage, self.maskedImage, kernel, convControl)
if display and False:
ds9.mtv(displayUtils.Mosaic().makeMosaic([
self.maskedImage, refMaskedImage, self.cnvMaskedImage]), frame=0)
if False:
for (x, y) in ((0, 0), (1, 0), (0, 1), (50, 50)):
print("Mask(%d,%d) 0x%x 0x%x" % (x, y, refMaskedImage.getMask().get(x, y),
self.cnvMaskedImage.getMask().get(x, y)))
self.assertImagesNearlyEqual(self.cnvImage, refMaskedImage.getImage(), atol=atol, rtol=rtol)
self.assertMaskedImagesNearlyEqual(self.cnvMaskedImage, refMaskedImage, atol=atol, rtol=rtol)
if not sameMaskPlaneDicts(self.cnvMaskedImage, self.maskedImage):
self.cnvMaskedImage.writeFits("act%s" % (shortKernelDescr,))
refMaskedImage.writeFits("des%s" % (shortKernelDescr,))
self.fail("convolve(MaskedImage, kernel=%s, doNormalize=%s, "
"doCopyEdge=%s, maxInterpDist=%s) failed:\n%s" %
(kernelDescr, doNormalize, doCopyEdge, maxInterpDist,
"convolved mask dictionary does not match input"))
def main(rootDir, visit, ccd, n=4):
# make a butler and specify your dataId
butler = dafPersist.Butler(rootDir)
dataId = {'visit': visit, 'ccd':ccd}
# get the souces and the exposure from the butler
sources = butler.get('src', dataId)
exposure = butler.get("calexp", dataId)
# make a list of the ones with the calib.psf.used flag set
psfSources = [s for s in sources if s.get("calib.psf.used")]
wx, wy = 25, 25 # the width to use for the bounding box
subImages = []
labels = []
for psfSrc in psfSources[0:n]:
# make a bounding box for this source
x, y = int(psfSrc.getX()), int(psfSrc.getY())
bbox = afwGeom.Box2I(afwGeom.Point2I(x-wx/2, y-wy/2), afwGeom.Extent2I(wx, wy))
# create a new image, and add it to our list
subimg = afwImage.ImageF(exposure.getMaskedImage().getImage(), bbox)
subImages.append(subimg)
# use the ID for a label
label = "ID="+str(psfSrc.getId())
labels.append(label)
# create a Mosaic object and set the specifications for your mosaic
m = dispUtils.Mosaic()
m.setGutter(2) # width of the space between each subimage
m.setBackground(0)
m.setMode("square")
# create the mosaic
mosaic = m.makeMosaic(subImages)
# display it with labels in ds9
ds9.mtv(mosaic)
m.drawLabels(labels)
# make a pyplot png (note: y-axis flipped for pyplot)
img = mosaic.getArray()[::-1,:]
vmin, vmax = zscale(img)
pyplot.imshow(img, interpolation="none", cmap="gray", vmin=vmin, vmax=vmax)
pyplot.gcf().savefig("mosaic.png")
def main(rootDir, visit, ccd, n=4):
# butler を開き読み込みたいデータの dataId を指定する
butler = dafPersist.Butler(rootDir)
dataId = {'visit': visit, 'ccd': ccd}
# butler から一次処理済データのカタログファイルをとデータを読み込む
sources = butler.get('src', dataId)
exposure = butler.get("calexp", dataId)
# calib.psf.used で flag がついている天体リストを作る
psfSources = [s for s in sources if s.get("calib.psf.used")]
wx, wy = 25, 25 # bounding box の大きさを指定
subImages = []
labels = []
for psfSrc in psfSources[0:n]:
# この天体の bounding box を作る
x, y = int(psfSrc.getX()), int(psfSrc.getY())
bbox = afwGeom.Box2I(afwGeom.Point2I(x - wx / 2, y - wy / 2),
afwGeom.Extent2I(wx, wy))
# 新しい画像を作り、リストに加える
subimg = afwImage.ImageF(exposure.getMaskedImage().getImage(), bbox)
subImages.append(subimg)
# ラベルつけのために ID を使う
label = "ID=" + str(psfSrc.getId())
labels.append(label)
# mosaic の天体を作り、仕様をセットする
m = dispUtils.Mosaic()
m.setGutter(2) # 各 subimage の幅を指定する
m.setBackground(0)
m.setMode("square")
# mosaic を作る
mosaic = m.makeMosaic(subImages)
# ds9 で表示し、その画像にラベルをつける
ds9.mtv(mosaic)
m.drawLabels(labels)
# pyplot で png 画像を作る（pyplot 用に y-軸 は判定居させている）
img = mosaic.getArray()[::-1, :]
vmin, vmax = zscale(img)
pyplot.imshow(img, interpolation="none", cmap="gray", vmin=vmin, vmax=vmax)
pyplot.gcf().savefig("mosaic.png")
def BuildMosaic(filename, gutter=0, background=0):
import lsst.afw.display.utils as Util
import lsst.afw.image as afwImg
m = Util.Mosaic()
m.setGutter(gutter)
m.setBackground(background)
images = []
for i in range(2, 18):
m.append(afwImg.ImageF(filename, i), str(i))
# mosaic = m.makeMosaic()
return m
def showPsf(da, distort=False, stampSize=19, nx=7, ny=None, gutterLevel=0.05):
mos = displayUtils.Mosaic(background=gutterLevel, gutter=1)
if ny is None:
ny = 2*nx
dataId = da.dataId.copy()
for ccd in (8, 9, 5, 4, 3, 6, 7, 2, 1, 0):
dataId.update(ccd=ccd)
calexp = da.getDataset("calexp", dataId)[0]
if False:
print calexp.getDetector().getId()
mos.append(maUtils.showPsfMosaic(calexp, stampSize=stampSize,
nx=nx, ny=ny, distort=distort).makeMosaic(mode=nx))
return mos.makeMosaic(mode=5) # Camera is 5x2
def testCandidateList(self):
if False and display:
ds9.mtv(self.mi)
for cell in self.cellSet.getCellList():
x0, y0, x1, y1 = (cell.getBBox().getX0(),
cell.getBBox().getY0(),
cell.getBBox().getX1(),
cell.getBBox().getY1())
print((x0, y0, " ", x1, y1))
x0 -= 0.5
y0 -= 0.5
x1 += 0.5
y1 += 0.5
ds9.line([(x0, y0), (x1, y0), (x1, y1), (x0, y1), (x0, y0)],
ctype=ds9.RED)
self.assertFalse(self.cellSet.getCellList()[0].empty())
self.assertTrue(self.cellSet.getCellList()[1].empty())
self.assertFalse(self.cellSet.getCellList()[2].empty())
stamps = []
for cell in self.cellSet.getCellList():
for cand in cell:
#
# Swig doesn't know that we PsfCandidate; all it knows is
# that we have a SpatialCellCandidate, and
# SpatialCellCandidates don't know about getMaskedImage; so
# cast the pointer to PsfCandidate<float> and all will be well
#
cand = algorithms.PsfCandidateF.cast(cell[0])
width, height = 15, 17
cand.setWidth(width)
cand.setHeight(height)
im = cand.getMaskedImage()
stamps.append(im)
self.assertEqual(im.getWidth(), width)
self.assertEqual(im.getHeight(), height)
if display:
mos = displayUtils.Mosaic()
ds9.mtv(mos.makeMosaic(stamps), frame=1)
def testCandidateList(self):
if False and display:
ds9.mtv(self.mi)
for cell in self.cellSet.getCellList():
x0, y0, x1, y1 = (cell.getBBox().getX0(),
cell.getBBox().getY0(),
cell.getBBox().getX1(),
cell.getBBox().getY1())
print((x0, y0, " ", x1, y1))
x0 -= 0.5
y0 -= 0.5
x1 += 0.5
y1 += 0.5
ds9.line([(x0, y0), (x1, y0), (x1, y1), (x0, y1), (x0, y0)],
ctype=ds9.RED)
self.assertFalse(self.cellSet.getCellList()[0].empty())
self.assertTrue(self.cellSet.getCellList()[1].empty())
self.assertFalse(self.cellSet.getCellList()[2].empty())
stamps = []
for cell in self.cellSet.getCellList():
for cand in cell:
cand = cell[0]
width, height = 15, 17
cand.setWidth(width)
cand.setHeight(height)
im = cand.getMaskedImage()
stamps.append(im)
self.assertEqual(im.getWidth(), width)
self.assertEqual(im.getHeight(), height)
if display:
mos = displayUtils.Mosaic()
ds9.mtv(mos.makeMosaic(stamps), frame=1)
def testKernelPsf(self):
"""Test creating a Psf from a Kernel."""
x, y = 10.4999, 10.4999
ksize = 15
sigma1 = 1
#
# Make a PSF from that kernel
#
kPsf = measAlg.KernelPsf(
afwMath.AnalyticKernel(ksize, ksize,
afwMath.GaussianFunction2D(sigma1, sigma1)))
kIm = kPsf.computeImage(afwGeom.Point2D(x, y))
#
# And now via the dgPsf model
#
dgPsf = measAlg.DoubleGaussianPsf(ksize, ksize, sigma1)
dgIm = dgPsf.computeImage(afwGeom.Point2D(x, y))
#
# Check that they're the same
#
diff = type(kIm)(kIm, True)
diff -= dgIm
stats = afwMath.makeStatistics(diff, afwMath.MAX | afwMath.MIN)
self.assertAlmostEqual(stats.getValue(afwMath.MAX), 0.0, places=16)
self.assertAlmostEqual(stats.getValue(afwMath.MIN), 0.0, places=16)
for pad in [-2, 4, 0]:
resizedKPsf = kPsf.resized(ksize + pad, ksize + pad)
self.assertEqual(resizedKPsf.computeBBox().getDimensions(),
afwGeom.Extent2I(ksize + pad, ksize + pad))
self.assertEqual(resizedKPsf.getKernel().getKernelParameters(),
kPsf.getKernel().getKernelParameters())
self._compareKernelImages(kPsf, resizedKPsf)
if display:
mos = displayUtils.Mosaic()
mos.setBackground(-0.1)
ds9.mtv(mos.makeMosaic([kIm, dgIm, diff], mode="x"), frame=1)
def testCandidateList(self):
self.assertFalse(self.cellSet.getCellList()[0].empty())
self.assertTrue(self.cellSet.getCellList()[1].empty())
self.assertFalse(self.cellSet.getCellList()[2].empty())
self.assertTrue(self.cellSet.getCellList()[3].empty())
stamps = []
for cell in self.cellSet.getCellList():
for cand in cell:
cand = cell[0]
width, height = 29, 25
cand.setWidth(width)
cand.setHeight(height)
im = cand.getMaskedImage()
stamps.append(im)
self.assertEqual(im.getWidth(), width)
self.assertEqual(im.getHeight(), height)
if False and display:
mos = displayUtils.Mosaic()
mos.makeMosaic(stamps, frame=2)
def testPcaNaN(self):
"""Test calculating PCA when the images can contain NaNs"""
width, height = 20, 10
values = (100, 200, 300)
for i, val in enumerate(values):
im = afwImage.ImageF(afwGeom.Extent2I(width, height))
im.set(val)
if i == 1:
im.set(width // 2, height // 2, np.nan)
self.ImageSet.addImage(im, 1.0)
self.ImageSet.analyze()
eImages = []
for img in self.ImageSet.getEigenImages():
eImages.append(img)
if display:
mos = displayUtils.Mosaic(background=-10)
ds9.mtv(mos.makeMosaic(eImages), frame=1)
def testGetPcaKernel(self):
"""Convert our cellSet to a LinearCombinationKernel"""
nEigenComponents = 2
spatialOrder = 1
kernelSize = 21
nStarPerCell = 2
nStarPerCellSpatialFit = 2
tolerance = 1e-5
if display:
ds9.mtv(self.mi, frame=0)
#
# Show the candidates we're using
#
for cell in self.cellSet.getCellList():
i = 0
for cand in cell:
i += 1
source = cand.getSource()
xc, yc = source.getXAstrom() - self.mi.getX0(
), source.getYAstrom() - self.mi.getY0()
if i <= nStarPerCell:
ds9.dot("o", xc, yc, ctype=ds9.GREEN)
else:
ds9.dot("o", xc, yc, ctype=ds9.YELLOW)
pair = algorithms.createKernelFromPsfCandidates(
self.cellSet, self.exposure.getDimensions(),
self.exposure.getXY0(), nEigenComponents, spatialOrder, kernelSize,
nStarPerCell)
kernel, eigenValues = pair[0], pair[1]
del pair
print("lambda", " ".join(["%g" % l for l in eigenValues]))
pair = algorithms.fitSpatialKernelFromPsfCandidates(
kernel, self.cellSet, nStarPerCellSpatialFit, tolerance)
status, chi2 = pair[0], pair[1]
del pair
print("Spatial fit: %s chi^2 = %.2g" % (status, chi2))
psf = roundTripPsf(5, algorithms.PcaPsf(kernel)) # Hurrah!
self.assertIsNotNone(psf.getKernel())
self.checkTablePersistence(psf)
if display:
# print psf.getKernel().toString()
eImages = []
for k in psf.getKernel().getKernelList():
im = afwImage.ImageD(k.getDimensions())
k.computeImage(im, False)
eImages.append(im)
mos = displayUtils.Mosaic()
frame = 3
ds9.mtv(mos.makeMosaic(eImages), frame=frame)
ds9.dot("Eigen Images", 0, 0, frame=frame)
#
# Make a mosaic of PSF candidates
#
stamps = []
stampInfo = []
for cell in self.cellSet.getCellList():
for cand in cell:
s = cand.getSource()
im = cand.getMaskedImage()
stamps.append(im)
stampInfo.append("[%d 0x%x]" %
(s.getId(), s.getFlagForDetection()))
mos = displayUtils.Mosaic()
frame = 1
ds9.mtv(mos.makeMosaic(stamps), frame=frame, lowOrderBits=True)
for i in range(len(stampInfo)):
ds9.dot(stampInfo[i],
mos.getBBox(i).getX0(),
mos.getBBox(i).getY0(),
frame=frame,
ctype=ds9.RED)
psfImages = []
labels = []
if False:
nx, ny = 3, 4
for iy in range(ny):
for ix in range(nx):
x = int((ix + 0.5) * self.mi.getWidth() / nx)
y = int((iy + 0.5) * self.mi.getHeight() / ny)
im = psf.getImage(x, y)
psfImages.append(im.Factory(im, True))
labels.append("PSF(%d,%d)" % (int(x), int(y)))
if True:
print((x, y, "PSF parameters:",
psf.getKernel().getKernelParameters()))
else:
nx, ny = 2, 2
for x, y in [(20, 20), (60, 20), (60, 210), (20, 210)]:
im = psf.computeImage(afwGeom.PointD(x, y))
psfImages.append(im.Factory(im, True))
labels.append("PSF(%d,%d)" % (int(x), int(y)))
if True:
print(x, y, "PSF parameters:",
psf.getKernel().getKernelParameters())
frame = 2
mos.makeMosaic(psfImages, frame=frame, mode=nx)
mos.drawLabels(labels, frame=frame)
if display:
ds9.mtv(self.mi, frame=0)
psfImages = []
labels = []
if False:
nx, ny = 3, 4
for iy in range(ny):
for ix in range(nx):
x = int((ix + 0.5) * self.mi.getWidth() / nx)
y = int((iy + 0.5) * self.mi.getHeight() / ny)
algorithms.subtractPsf(psf, self.mi, x, y)
else:
nx, ny = 2, 2
for x, y in [(20, 20), (60, 20), (60, 210), (20, 210)]:
if False: # Test subtraction with non-centered psfs
x += 0.5
y -= 0.5
#algorithms.subtractPsf(psf, self.mi, x, y)
ds9.mtv(self.mi, frame=1)
def showPsfMosaic(exposure, psf=None, nx=7, ny=None, showCenter=True, showEllipticity=False,
showFwhm=False, stampSize=0, display=None, title=None):
"""Show a mosaic of Psf images. exposure may be an Exposure (optionally with PSF),
or a tuple (width, height)
If stampSize is > 0, the psf images will be trimmed to stampSize*stampSize
"""
scale = 1.0
if showFwhm:
showEllipticity = True
scale = 2*math.log(2) # convert sigma^2 to HWHM^2 for a Gaussian
mos = displayUtils.Mosaic()
try: # maybe it's a real Exposure
width, height = exposure.getWidth(), exposure.getHeight()
x0, y0 = exposure.getXY0()
if not psf:
psf = exposure.getPsf()
except AttributeError:
try: # OK, maybe a list [width, height]
width, height = exposure[0], exposure[1]
x0, y0 = 0, 0
except TypeError: # I guess not
raise RuntimeError("Unable to extract width/height from object of type %s" % type(exposure))
if not ny:
ny = int(nx*float(height)/width + 0.5)
if not ny:
ny = 1
centroidName = "SdssCentroid"
shapeName = "base_SdssShape"
schema = afwTable.SourceTable.makeMinimalSchema()
schema.getAliasMap().set("slot_Centroid", centroidName)
schema.getAliasMap().set("slot_Centroid_flag", centroidName+"_flag")
control = measBase.SdssCentroidControl()
centroider = measBase.SdssCentroidAlgorithm(control, centroidName, schema)
sdssShape = measBase.SdssShapeControl()
shaper = measBase.SdssShapeAlgorithm(sdssShape, shapeName, schema)
table = afwTable.SourceTable.make(schema)
table.defineCentroid(centroidName)
table.defineShape(shapeName)
bbox = None
if stampSize > 0:
w, h = psf.computeImage(lsst.geom.PointD(0, 0)).getDimensions()
if stampSize <= w and stampSize <= h:
bbox = lsst.geom.BoxI(lsst.geom.PointI((w - stampSize)//2, (h - stampSize)//2),
lsst.geom.ExtentI(stampSize, stampSize))
centers = []
shapes = []
for iy in range(ny):
for ix in range(nx):
x = int(ix*(width-1)/(nx-1)) + x0
y = int(iy*(height-1)/(ny-1)) + y0
im = psf.computeImage(lsst.geom.PointD(x, y)).convertF()
imPeak = psf.computePeak(lsst.geom.PointD(x, y))
im /= imPeak
if bbox:
im = im.Factory(im, bbox)
lab = "PSF(%d,%d)" % (x, y) if False else ""
mos.append(im, lab)
exp = afwImage.makeExposure(afwImage.makeMaskedImage(im))
exp.setPsf(psf)
w, h = im.getWidth(), im.getHeight()
centerX = im.getX0() + w//2
centerY = im.getY0() + h//2
src = table.makeRecord()
spans = afwGeom.SpanSet(exp.getBBox())
foot = afwDet.Footprint(spans)
foot.addPeak(centerX, centerY, 1)
src.setFootprint(foot)
try:
centroider.measure(src, exp)
centers.append((src.getX() - im.getX0(), src.getY() - im.getY0()))
shaper.measure(src, exp)
shapes.append((src.getIxx(), src.getIxy(), src.getIyy()))
except Exception:
pass
if not display:
display = afwDisplay.Display()
mos.makeMosaic(display=display, title=title if title else "Model Psf", mode=nx)
if centers and display:
with display.Buffering():
for i, (cen, shape) in enumerate(zip(centers, shapes)):
bbox = mos.getBBox(i)
xc, yc = cen[0] + bbox.getMinX(), cen[1] + bbox.getMinY()
if showCenter:
display.dot("+", xc, yc, ctype=afwDisplay.BLUE)
if showEllipticity:
ixx, ixy, iyy = shape
ixx *= scale
ixy *= scale
iyy *= scale
display.dot("@:%g,%g,%g" % (ixx, ixy, iyy), xc, yc, ctype=afwDisplay.RED)
return mos
def showPsf(psf, set, ext=None, wcsData=None, trim=0, nspot=5,
diagnostics=False, outDir="", frame=None, title=None):
"""Show a PSF on ds9"""
if ext is not None:
psf = psf[ext]
if wcsData:
if ext is not None:
wcsData = wcsData[ext]
wcs, naxis1, naxis2 = wcsData
else:
wcs, naxis1, naxis2 = None, None, None
naxis = [naxis1, naxis2]
for i in range(2):
if naxis[i] is None:
# cmin, cmax are the range of input star positions
cmin, cmax = [set.getContextOffset(i) + d*set.getContextScale(i) for d in (-0.5, 0.5)]
naxis[i] = cmax + cmin # a decent guess
import lsst.afw.display.utils as ds9Utils
if naxis[0] > naxis[1]:
nx, ny = int(nspot*naxis[0]/float(naxis[1]) + 0.5), nspot
else:
nx, ny = nspot, int(nspot*naxis[1]/float(naxis[0]) + 0.5)
mos = ds9Utils.Mosaic(gutter=2, background=0.02)
xpos, ypos = np.linspace(0, naxis[0], nx), np.linspace(0, naxis[1], ny)
for y in ypos:
for x in xpos:
psf.build(x, y)
im = afwImage.ImageF(*psf.getLoc().shape)
im.getArray()[:] = psf.getLoc()
im /= float(im.getArray().max())
if trim:
if trim > im.getHeight()//2:
trim = im.getHeight()//2
im = im[trim:-trim, trim:-trim]
mos.append(im)
mosaic = mos.makeMosaic(mode=nx)
if frame is not None:
ds9.mtv(mosaic, frame=frame, title=title)
#
# Figure out the WCS for the mosaic
#
pos = []
if False:
for x, y, i in zip((xpos[0], xpos[-1]), (ypos[0], ypos[-1]), (0, mos.nImage - 1)):
bbox = mos.getBBox(i)
mosx = bbox.getMinX() + 0.5*(bbox.getWidth() - 1)
mosy = bbox.getMinY() + 0.5*(bbox.getHeight() - 1)
pos.append([afwGeom.PointD(mosx, mosy), wcs.pixelToSky(afwGeom.PointD(x, y))])
else:
pos.append([afwGeom.PointD(0, 0), wcs.pixelToSky(afwGeom.PointD(0, 0))])
pos.append([afwGeom.PointD(*mosaic.getDimensions()), wcs.pixelToSky(afwGeom.PointD(naxis1, naxis2))])
CD = []
for i in range(2):
delta = pos[1][1][i].asDegrees() - pos[0][1][i].asDegrees()
CD.append(delta/(pos[1][0][i] - pos[0][0][i]))
CD = np.array(CD)
CD.shape = (2, 2)
mosWcs = afwGeom.makeSkyWcs(crval=pos[0][0], crpix=pos[0][1], cdMatrix=CD)
if ext is not None:
title = "%s-%d" % (title, ext)
if frame is not None:
ds9.mtv(mosaic, frame=frame, title=title, wcs=mosWcs)
if diagnostics:
outFile = "%s-mod.fits" % title
if outDir:
outFile = os.path.join(outDir, outFile)
mosaic.writeFits(outFile, mosWcs.getFitsMetadata())
#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
mos = ds9Utils.Mosaic(gutter=4, background=0.002)
for i in range(set.getNsample()):
s = set.getSample(i)
if ext is not None and s.getExtindex() != ext:
continue
smos = ds9Utils.Mosaic(gutter=2, background=-0.003)
for func in [s.getVig, s.getVigResi]:
arr = func()
if func == s.getVig:
norm = float(arr.max()) if True else s.getNorm()
arr /= norm
im = afwImage.ImageF(*arr.shape)
im.getArray()[:] = arr
smos.append(im)
mos.append(smos.makeMosaic(mode="x"))
mosaic = mos.makeMosaic(title=title)
if frame is not None:
ds9.mtv(mosaic, title=title, frame=frame+1)
if diagnostics:
outFile = "%s-psfstars.fits" % title
if outDir:
outFile = os.path.join(outDir, outFile)
mosaic.writeFits(outFile)
def showPsfCandidates(exposure, psfCellSet, psf=None, display=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 not display:
display = afwDisplay.Display()
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
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 = lsst.geom.BoxI(lsst.geom.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 Exception:
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, afwImage.LOCAL] = 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:
subtractPsf(psf, im, xc, yc)
except Exception:
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 = psf.getKernel()
except Exception:
noSpatialKernel = None
if noSpatialKernel:
candCenter = lsst.geom.PointD(cand.getXCenter(), cand.getYCenter())
fit = 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 = afwDisplay.RED if cand.isBad() else afwDisplay.GREEN
else:
lab = "%d flux %8.3g" % (objId, cand.getSource().getPsfInstFlux())
ctype = afwDisplay.GREEN
mos.append(im, lab, ctype)
if False and numpy.isnan(rchi2):
display.mtv(cand.getMaskedImage().getImage(), title="showPsfCandidates: candidate")
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(display=display, title=title)
with display.Buffering():
for centers, color in ((candidateCenters, afwDisplay.GREEN), (candidateCentersBad, afwDisplay.RED)):
for cen in centers:
bbox = mos.getBBox(cen[0])
display.dot("+", cen[1] + bbox.getMinX(), cen[2] + bbox.getMinY(), ctype=color)
return mosaicImage
spatialBg = results.backgroundModel
kernelCellSet = results.kernelCellSet
except Exception as e:
print('FAIL')
sys.exit(1)
if False:
print(spatialKernel.getSpatialFunctionList()[0].toString())
print(spatialKernel.getKernelParameters())
print(spatialKernel.getSpatialParameters())
import pdb
pdb.set_trace()
# Lets see what we got
if display:
mos = displayUtils.Mosaic()
# Inputs
for cell in kernelCellSet.getCellList():
for cand in cell.begin(False): # False = include bad candidates
rchi2 = cand.getChi2()
# No kernels made
if cand.getStatus() == afwMath.SpatialCellCandidate.UNKNOWN:
continue
try:
im = cand.getKernelImage(ipDiffim.KernelCandidateF.RECENT)
except Exception:
continue
def makeDeblendFamilyMosaic(mi,
parent,
kids,
mapperInfo=None,
background=-10,
maskbit=False,
imBbox=None):
"""Create a mosaic of an object's children
"""
aa = {}
if maskbit:
aa.update(mask=True)
parent_im = footprintToImage(parent.getFootprint(), mi, **aa)
bbox = afwGeom.BoxD(parent.getFootprint().getBBox())
pext = (bbox.getMinX(), bbox.getMaxX(), bbox.getMinY(), bbox.getMaxY())
pks = parent.getFootprint().getPeaks()
pix = [pk.getIx() for pk in pks]
piy = [pk.getIy() for pk in pks]
pfx = [pk.getFx() for pk in pks]
pfy = [pk.getFy() for pk in pks]
N = 1 + len(kids)
S = np.ceil(np.sqrt(N))
C = S
R = np.ceil(float(N) / C)
Rx, Ry = [], []
tts = []
stys = []
xys = []
#
# Find how large an image we need to display the parent and all the children
#
kidImages, kim = {}, None
for kid in kids:
kim = footprintToImage(kid.getFootprint(), mi, **aa)
kidImages[kid] = kim
if not kim:
kim = parent_im.clone()
if not imBbox:
imBbox = parent_im.getBBox(afwImage.PARENT)
for kid in kids:
imBbox.include(kidImages[kid].getBBox(afwImage.PARENT))
mos = displayUtils.Mosaic(background=background)
bbox = afwGeom.Box2I(
afwGeom.Point2I(kim.getX0() - imBbox.getMinX(),
kim.getY0() - imBbox.getMinY()), kim.getDimensions())
kidImages[parent] = parent_im # not strictly a kid
for kid in [parent] + kids:
kim = kidImages[kid]
#
# Put the child into the correct place in the parent image. We have to do this for
# the parent too if some of the children extended outside its BBox
#
bbox = afwGeom.Box2I(
afwGeom.Point2I(kim.getX0() - imBbox.getMinX(),
kim.getY0() - imBbox.getMinY()),
kim.getDimensions())
_kim = parent_im.Factory(imBbox)
if kid.getFootprint().getBBox().isEmpty():
_kim[:] = 0
pass
else:
_kim[bbox] = kim
mos.append(
_kim,
'%d%s' % (mapperInfo.getId(kid) if mapperInfo else
(kid.getId() & 0xfff), "P" if kid == parent else "C"))
del _kim
return mos
def showKernelMosaic(bbox,
kernel,
nx=7,
ny=None,
frame=None,
title=None,
showCenter=True,
showEllipticity=True):
"""Show a mosaic of Kernel images.
"""
mos = displayUtils.Mosaic()
x0 = bbox.getBeginX()
y0 = bbox.getBeginY()
width = bbox.getWidth()
height = bbox.getHeight()
if not ny:
ny = int(nx * float(height) / width + 0.5)
if not ny:
ny = 1
schema = afwTable.SourceTable.makeMinimalSchema()
centroidName = "base_SdssCentroid"
shapeName = "base_SdssShape"
control = measBase.SdssCentroidControl()
schema.getAliasMap().set("slot_Centroid", centroidName)
schema.getAliasMap().set("slot_Centroid_flag", centroidName + "_flag")
centroider = measBase.SdssCentroidAlgorithm(control, centroidName, schema)
sdssShape = measBase.SdssShapeControl()
shaper = measBase.SdssShapeAlgorithm(sdssShape, shapeName, schema)
table = afwTable.SourceTable.make(schema)
table.defineCentroid(centroidName)
table.defineShape(shapeName)
centers = []
shapes = []
for iy in range(ny):
for ix in range(nx):
x = int(ix * (width - 1) / (nx - 1)) + x0
y = int(iy * (height - 1) / (ny - 1)) + y0
im = afwImage.ImageD(kernel.getDimensions())
ksum = kernel.computeImage(im, False, x, y)
lab = "Kernel(%d,%d)=%.2f" % (x, y, ksum) if False else ""
mos.append(im, lab)
# SdssCentroidAlgorithm.measure requires an exposure of floats
exp = afwImage.makeExposure(afwImage.makeMaskedImage(
im.convertF()))
w, h = im.getWidth(), im.getHeight()
centerX = im.getX0() + w // 2
centerY = im.getY0() + h // 2
src = table.makeRecord()
foot = afwDet.Footprint(exp.getBBox())
foot.addPeak(centerX, centerY, 1)
src.setFootprint(foot)
centroider.measure(src, exp)
centers.append((src.getX(), src.getY()))
shaper.measure(src, exp)
shapes.append((src.getIxx(), src.getIxy(), src.getIyy()))
mos.makeMosaic(frame=frame,
title=title if title else "Model Kernel",
mode=nx)
if centers and frame is not None:
i = 0
with ds9.Buffering():
for cen, shape in zip(centers, shapes):
bbox = mos.getBBox(i)
i += 1
xc, yc = cen[0] + bbox.getMinX(), cen[1] + bbox.getMinY()
if showCenter:
ds9.dot("+", xc, yc, ctype=ds9.BLUE, frame=frame)
if showEllipticity:
ixx, ixy, iyy = shape
ds9.dot("@:%g,%g,%g" % (ixx, ixy, iyy),
xc,
yc,
frame=frame,
ctype=ds9.RED)
return mos
def showPsfMosaic(exposure, psf=None, nx=7, ny=None,
showCenter=True, showEllipticity=False, showFwhm=False,
stampSize=0, frame=None, title=None):
"""Show a mosaic of Psf images. exposure may be an Exposure (optionally with PSF), or a tuple (width, height)
If stampSize is > 0, the psf images will be trimmed to stampSize*stampSize
"""
scale = 1.0
if showFwhm:
showEllipticity = True
scale = 2*math.log(2) # convert sigma^2 to HWHM^2 for a Gaussian
mos = displayUtils.Mosaic()
try: # maybe it's a real Exposure
width, height = exposure.getWidth(), exposure.getHeight()
x0, y0 = exposure.getXY0()
if not psf:
psf = exposure.getPsf()
except AttributeError:
try: # OK, maybe a list [width, height]
width, height = exposure[0], exposure[1]
x0, y0 = 0, 0
except TypeError: # I guess not
raise RuntimeError, ("Unable to extract width/height from object of type %s" % type(exposure))
if not ny:
ny = int(nx*float(height)/width + 0.5)
if not ny:
ny = 1
schema = afwTable.SourceTable.makeMinimalSchema()
control = algorithmsLib.GaussianCentroidControl()
centroider = algorithmsLib.MeasureSourcesBuilder().addAlgorithm(control).build(schema)
sdssShape = algorithmsLib.SdssShapeControl()
shaper = algorithmsLib.MeasureSourcesBuilder().addAlgorithm(sdssShape).build(schema)
table = afwTable.SourceTable.make(schema)
table.defineCentroid(control.name)
table.defineShape(sdssShape.name)
bbox = None
if stampSize > 0:
w, h = psf.computeImage(afwGeom.PointD(0, 0)).getDimensions()
if stampSize <= w and stampSize <= h:
bbox = afwGeom.BoxI(afwGeom.PointI((w - stampSize)//2, (h - stampSize)//2),
afwGeom.ExtentI(stampSize, stampSize))
centers = []
shapes = []
for iy in range(ny):
for ix in range(nx):
x = int(ix*(width-1)/(nx-1)) + x0
y = int(iy*(height-1)/(ny-1)) + y0
im = psf.computeImage(afwGeom.PointD(x, y)).convertF()
imPeak = psf.computePeak(afwGeom.PointD(x, y))
im /= imPeak
if bbox:
im = im.Factory(im, bbox)
lab = "PSF(%d,%d)" % (x, y) if False else ""
mos.append(im, lab)
exp = afwImage.makeExposure(afwImage.makeMaskedImage(im))
w, h = im.getWidth(), im.getHeight()
cen = afwGeom.PointD(im.getX0() + w//2, im.getY0() + h//2)
src = table.makeRecord()
foot = afwDet.Footprint(exp.getBBox())
src.setFootprint(foot)
centroider.apply(src, exp, cen)
centers.append((src.getX() - im.getX0(), src.getY() - im.getY0()))
shaper.apply(src, exp, cen)
shapes.append((src.getIxx(), src.getIxy(), src.getIyy()))
mos.makeMosaic(frame=frame, title=title if title else "Model Psf", mode=nx)
if centers and frame is not None:
i = 0
with ds9.Buffering():
for cen, shape in zip(centers, shapes):
bbox = mos.getBBox(i); i += 1
xc, yc = cen[0] + bbox.getMinX(), cen[1] + bbox.getMinY()
if showCenter:
ds9.dot("+", xc, yc, ctype=ds9.BLUE, frame=frame)
if showEllipticity:
ixx, ixy, iyy = shape
ixx *= scale; ixy *= scale; iyy *= scale
ds9.dot("@:%g,%g,%g" % (ixx, ixy, iyy), xc, yc, frame=frame, ctype=ds9.RED)
return mos
class PcaPsfDeterminer(object):
"""!
A measurePsfTask psf estimator
"""
ConfigClass = PcaPsfDeterminerConfig
def __init__(self, config):
"""!Construct a PCA PSF Fitter
\param[in] config instance of PcaPsfDeterminerConfig
"""
self.config = config
# N.b. name of component is meas.algorithms.psfDeterminer so you can turn on psf debugging
# independent of which determiner is active
self.debugLog = pexLog.Debug("meas.algorithms.psfDeterminer")
self.warnLog = pexLog.Log(pexLog.getDefaultLog(),
"meas.algorithms.psfDeterminer")
def _fitPsf(self, exposure, psfCellSet, kernelSize, nEigenComponents):
algorithmsLib.PsfCandidateF.setPixelThreshold(
self.config.pixelThreshold)
algorithmsLib.PsfCandidateF.setMaskBlends(self.config.doMaskBlends)
#
# Loop trying to use nEigenComponents, but allowing smaller numbers if necessary
#
for nEigen in range(nEigenComponents, 0, -1):
# Determine KL components
try:
kernel, eigenValues = algorithmsLib.createKernelFromPsfCandidates(
psfCellSet, exposure.getDimensions(), exposure.getXY0(),
nEigen, self.config.spatialOrder, kernelSize,
self.config.nStarPerCell, bool(self.config.constantWeight))
break # OK, we can get nEigen components
except pexExceptions.LengthError as e:
if nEigen == 1: # can't go any lower
raise IndexError("No viable PSF candidates survive")
self.warnLog.log(
pexLog.Log.WARN,
"%s: reducing number of eigen components" % e.what())
#
# We got our eigen decomposition so let's use it
#
# Express eigenValues in units of reduced chi^2 per star
size = kernelSize + 2 * self.config.borderWidth
nu = size * size - 1 # number of degrees of freedom/star for chi^2
eigenValues = [
l / float(
algorithmsLib.countPsfCandidates(
psfCellSet, self.config.nStarPerCell) * nu)
for l in eigenValues
]
# Fit spatial model
status, chi2 = algorithmsLib.fitSpatialKernelFromPsfCandidates(
kernel, psfCellSet, bool(self.config.nonLinearSpatialFit),
self.config.nStarPerCellSpatialFit, self.config.tolerance,
self.config.lam)
psf = algorithmsLib.PcaPsf(kernel)
return psf, eigenValues, nEigen, chi2
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())
if len(sizes) == 0:
raise RuntimeError("No usable PSF candidates supplied")
nEigenComponents = self.config.nEigenComponents # initial version
if self.config.kernelSize >= 15:
self.debugLog.debug(1, \
"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.debugLog.debug(3, "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 iter 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
cand = algorithmsLib.cast_PsfCandidateF(cand)
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, 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 = algorithmsLib.cast_PsfCandidateF(cand)
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.debugLog.debug(
2, "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
cand = algorithmsLib.cast_PsfCandidateF(cand)
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 = int(
len(badCandidates) * (iter + 1) / self.config.nIterForPsf +
0.5)
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 = afwMath.cast_LinearCombinationKernel(
psf.getKernel())
for cell in psfCellSet.getCellList():
for cand in cell.begin(False):
cand = algorithmsLib.cast_PsfCandidateF(cand)
candCenter = afwGeom.PointD(cand.getXCenter(),
cand.getYCenter())
try:
im = cand.getMaskedImage(kernel.getWidth(),
kernel.getHeight())
except Exception, e:
continue
fit = algorithmsLib.fitKernelParamsToImage(
noSpatialKernel, im, candCenter)
params = fit[0]
kernels = fit[1]
amp = 0.0
for p, k in zip(params, kernels):
amp += p * afwMath.cast_FixedKernel(k).getSum()
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)
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 showKernelCandidates(kernelCellSet,
kernel,
background,
frame=None,
showBadCandidates=True,
resids=False,
kernels=False):
"""Display the Kernel candidates.
If kernel is provided include spatial model and residuals;
If chi is True, generate a plot of residuals/sqrt(variance), i.e. chi
"""
#
# Show us the ccandidates
#
if kernels:
mos = displayUtils.Mosaic(gutter=5, background=0)
else:
mos = displayUtils.Mosaic(gutter=5, background=-1)
#
candidateCenters = []
candidateCentersBad = []
candidateIndex = 0
for cell in kernelCellSet.getCellList():
for cand in cell.begin(False): # include bad candidates
# Original difference image; if does not exist, skip candidate
try:
resid = cand.getDifferenceImage(
diffimLib.KernelCandidateF.ORIG)
except Exception:
continue
rchi2 = cand.getChi2()
if rchi2 > 1e100:
rchi2 = np.nan
if not showBadCandidates and cand.isBad():
continue
im_resid = displayUtils.Mosaic(gutter=1, background=-0.5, mode="x")
try:
im = cand.getScienceMaskedImage()
im = im.Factory(im, True)
im.setXY0(cand.getScienceMaskedImage().getXY0())
except Exception:
continue
if (not resids and not kernels):
im_resid.append(im.Factory(im, True))
try:
im = cand.getTemplateMaskedImage()
im = im.Factory(im, True)
im.setXY0(cand.getTemplateMaskedImage().getXY0())
except Exception:
continue
if (not resids and not kernels):
im_resid.append(im.Factory(im, True))
# Difference image with original basis
if resids:
var = resid.getVariance()
var = var.Factory(var, True)
np.sqrt(var.getArray(), var.getArray()) # inplace sqrt
resid = resid.getImage()
resid /= var
bbox = kernel.shrinkBBox(resid.getBBox())
resid = resid.Factory(resid, bbox, True)
elif kernels:
kim = cand.getKernelImage(
diffimLib.KernelCandidateF.ORIG).convertF()
resid = kim.Factory(kim, True)
im_resid.append(resid)
# residuals using spatial model
ski = afwImage.ImageD(kernel.getDimensions())
kernel.computeImage(ski, False, int(cand.getXCenter()),
int(cand.getYCenter()))
sk = afwMath.FixedKernel(ski)
sbg = 0.0
if background:
sbg = background(int(cand.getXCenter()),
int(cand.getYCenter()))
sresid = cand.getDifferenceImage(sk, sbg)
resid = sresid
if resids:
resid = sresid.getImage()
resid /= var
bbox = kernel.shrinkBBox(resid.getBBox())
resid = resid.Factory(resid, bbox, True)
elif kernels:
kim = ski.convertF()
resid = kim.Factory(kim, True)
im_resid.append(resid)
im = im_resid.makeMosaic()
lab = "%d chi^2 %.1f" % (cand.getId(), rchi2)
ctype = ds9.RED if cand.isBad() else ds9.GREEN
mos.append(im, lab, ctype)
if False and np.isnan(rchi2):
ds9.mtv(cand.getScienceMaskedImage.getImage(),
title="candidate",
frame=1)
print("rating", cand.getCandidateRating())
im = cand.getScienceMaskedImage()
center = (candidateIndex, cand.getXCenter() - im.getX0(),
cand.getYCenter() - im.getY0())
candidateIndex += 1
if cand.isBad():
candidateCentersBad.append(center)
else:
candidateCenters.append(center)
if resids:
title = "chi Diffim"
elif kernels:
title = "Kernels"
else:
title = "Candidates & residuals"
mosaicImage = mos.makeMosaic(frame=frame, title=title)
return mosaicImage
