def testMakeBadKernels(self):
"""Attempt to make various invalid kernels; make sure the constructor shows an exception
"""
kWidth = 4
kHeight = 3
gaussFunc1 = afwMath.GaussianFunction1D(1.0)
gaussFunc2 = afwMath.GaussianFunction2D(1.0, 1.0, 0.0)
spFunc = afwMath.PolynomialFunction2D(1)
kernelList = []
kernelList.append(
afwMath.FixedKernel(
afwImage.ImageD(lsst.geom.Extent2I(kWidth, kHeight), 0.1)))
kernelList.append(
afwMath.FixedKernel(
afwImage.ImageD(lsst.geom.Extent2I(kWidth, kHeight), 0.2)))
for numKernelParams in (2, 4):
spFuncList = []
for ii in range(numKernelParams):
spFuncList.append(spFunc.clone())
try:
afwMath.AnalyticKernel(kWidth, kHeight, gaussFunc2, spFuncList)
self.fail(
"Should have failed with wrong # of spatial functions")
except pexExcept.Exception:
pass
for numKernelParams in (1, 3):
spFuncList = []
for ii in range(numKernelParams):
spFuncList.append(spFunc.clone())
try:
afwMath.LinearCombinationKernel(kernelList, spFuncList)
self.fail(
"Should have failed with wrong # of spatial functions")
except pexExcept.Exception:
pass
kParamList = [0.2] * numKernelParams
try:
afwMath.LinearCombinationKernel(kernelList, kParamList)
self.fail(
"Should have failed with wrong # of kernel parameters")
except pexExcept.Exception:
pass
try:
afwMath.SeparableKernel(kWidth, kHeight, gaussFunc1,
gaussFunc1, spFuncList)
self.fail(
"Should have failed with wrong # of spatial functions")
except pexExcept.Exception:
pass
for pointX in range(-1, kWidth + 2):
for pointY in range(-1, kHeight + 2):
if (0 <= pointX < kWidth) and (0 <= pointY < kHeight):
continue
try:
afwMath.DeltaFunctionKernel(
kWidth, kHeight, lsst.geom.Point2I(pointX, pointY))
self.fail("Should have failed with point not on kernel")
except pexExcept.Exception:
pass
def convolveImage(self, maskedImage, psf, doSmooth=True):
"""Convolve the image with the PSF
We convolve the image with a Gaussian approximation to the PSF,
because this is separable and therefore fast. It's technically a
correlation rather than a convolution, but since we use a symmetric
Gaussian there's no difference.
The convolution can be disabled with ``doSmooth=False``. If we do
convolve, we mask the edges as ``EDGE`` and return the convolved image
with the edges removed. This is because we can't convolve the edges
because the kernel would extend off the image.
Parameters
----------
maskedImage : `lsst.afw.image.MaskedImage`
Image to convolve.
psf : `lsst.afw.detection.Psf`
PSF to convolve with (actually with a Gaussian approximation
to it).
doSmooth : `bool`
Actually do the convolution? Set to False when running on
e.g. a pre-convolved image, or a mask plane.
Return Struct contents
----------------------
middle : `lsst.afw.image.MaskedImage`
Convolved image, without the edges.
sigma : `float`
Gaussian sigma used for the convolution.
"""
self.metadata.set("doSmooth", doSmooth)
sigma = psf.computeShape().getDeterminantRadius()
self.metadata.set("sigma", sigma)
if not doSmooth:
middle = maskedImage.Factory(maskedImage)
return pipeBase.Struct(middle=middle, sigma=sigma)
# Smooth using a Gaussian (which is separable, hence fast) of width sigma
# Make a SingleGaussian (separable) kernel with the 'sigma'
kWidth = self.calculateKernelSize(sigma)
self.metadata.set("smoothingKernelWidth", kWidth)
gaussFunc = afwMath.GaussianFunction1D(sigma)
gaussKernel = afwMath.SeparableKernel(kWidth, kWidth, gaussFunc,
gaussFunc)
convolvedImage = maskedImage.Factory(maskedImage.getBBox())
afwMath.convolve(convolvedImage, maskedImage, gaussKernel,
afwMath.ConvolutionControl())
#
# Only search psf-smoothed part of frame
#
goodBBox = gaussKernel.shrinkBBox(convolvedImage.getBBox())
middle = convolvedImage.Factory(convolvedImage, goodBBox,
afwImage.PARENT, False)
#
# Mark the parts of the image outside goodBBox as EDGE
#
self.setEdgeBits(maskedImage, goodBBox,
maskedImage.getMask().getPlaneBitMask("EDGE"))
return pipeBase.Struct(middle=middle, sigma=sigma)