def makeSpatialKernel(self, order):
basicGaussian1 = afwMath.GaussianFunction2D(2., 2., 0.)
basicKernel1 = afwMath.AnalyticKernel(self.ksize, self.ksize,
basicGaussian1)
basicGaussian2 = afwMath.GaussianFunction2D(5., 3., 0.5 * num.pi)
basicKernel2 = afwMath.AnalyticKernel(self.ksize, self.ksize,
basicGaussian2)
basisList = []
basisList.append(basicKernel1)
basisList.append(basicKernel2)
basisList = ipDiffim.renormalizeKernelList(basisList)
spatialKernelFunction = afwMath.PolynomialFunction2D(order)
spatialKernel = afwMath.LinearCombinationKernel(
basisList, spatialKernelFunction)
kCoeffs = [
[
0.0 for x in range(1,
spatialKernelFunction.getNParameters() + 1)
],
[
0.01 * x
for x in range(1,
spatialKernelFunction.getNParameters() + 1)
]
]
kCoeffs[0][
0] = 1.0 # it does not vary spatially; constant across image
spatialKernel.setSpatialParameters(kCoeffs)
return spatialKernel
def makeSpatialKernel(self, order):
basicGaussian1 = afwMath.GaussianFunction2D(2., 2., 0.)
basicKernel1 = afwMath.AnalyticKernel(self.ksize, self.ksize, basicGaussian1)
basicGaussian2 = afwMath.GaussianFunction2D(5., 3., 0.5 * num.pi)
basicKernel2 = afwMath.AnalyticKernel(self.ksize, self.ksize, basicGaussian2)
basisList = afwMath.KernelList()
basisList.append(basicKernel1)
basisList.append(basicKernel2)
basisList = afwMath.KernelList(ipDiffim.renormalizeKernelList(basisList))
spatialKernelFunction = afwMath.PolynomialFunction2D(order)
spatialKernel = afwMath.LinearCombinationKernel(basisList, spatialKernelFunction)
kCoeffs = [[0.0 for x in range(1,spatialKernelFunction.getNParameters()+1)],
[0.01 * x for x in range(1,spatialKernelFunction.getNParameters()+1)]]
kCoeffs[0][0] = 1.0 # it does not vary spatially; constant across image
spatialKernel.setSpatialParameters(kCoeffs)
return spatialKernel
def testRenormalize(self):
# inputs
gauss1 = afwMath.GaussianFunction2D(2, 2)
gauss2 = afwMath.GaussianFunction2D(3, 4)
gauss3 = afwMath.GaussianFunction2D(0.2, 0.25)
gaussKernel1 = afwMath.AnalyticKernel(self.kSize, self.kSize, gauss1)
gaussKernel2 = afwMath.AnalyticKernel(self.kSize, self.kSize, gauss2)
gaussKernel3 = afwMath.AnalyticKernel(self.kSize, self.kSize, gauss3)
kimage1 = afwImage.ImageD(gaussKernel1.getDimensions())
ksum1 = gaussKernel1.computeImage(kimage1, False)
kimage2 = afwImage.ImageD(gaussKernel2.getDimensions())
ksum2 = gaussKernel2.computeImage(kimage2, False)
kimage3 = afwImage.ImageD(gaussKernel3.getDimensions())
ksum3 = gaussKernel3.computeImage(kimage3, False)
self.assertNotEqual(ksum1, 1.)
self.assertNotEqual(ksum2, 1.)
self.assertNotEqual(ksum3, 1.)
# no constraints on first kernels norm
self.assertNotEqual(num.sum(num.ravel(kimage2.getArray())**2), 1.)
self.assertNotEqual(num.sum(num.ravel(kimage3.getArray())**2), 1.)
basisListIn = []
basisListIn.append(gaussKernel1)
basisListIn.append(gaussKernel2)
basisListIn.append(gaussKernel3)
# outputs
basisListOut = ipDiffim.renormalizeKernelList(basisListIn)
gaussKernel1 = basisListOut[0]
gaussKernel2 = basisListOut[1]
gaussKernel3 = basisListOut[2]
ksum1 = gaussKernel1.computeImage(kimage1, False)
ksum2 = gaussKernel2.computeImage(kimage2, False)
ksum3 = gaussKernel3.computeImage(kimage3, False)
self.assertAlmostEqual(ksum1, 1.)
self.assertAlmostEqual(ksum2, 0.)
self.assertAlmostEqual(ksum3, 0.)
# no constraints on first kernels norm
self.assertAlmostEqual(num.sum(num.ravel(kimage2.getArray())**2), 1.)
self.assertAlmostEqual(num.sum(num.ravel(kimage3.getArray())**2), 1.)
def testRenormalize(self):
# inputs
gauss1 = afwMath.GaussianFunction2D(2, 2)
gauss2 = afwMath.GaussianFunction2D(3, 4)
gauss3 = afwMath.GaussianFunction2D(0.2, 0.25)
gaussKernel1 = afwMath.AnalyticKernel(self.kSize, self.kSize, gauss1)
gaussKernel2 = afwMath.AnalyticKernel(self.kSize, self.kSize, gauss2)
gaussKernel3 = afwMath.AnalyticKernel(self.kSize, self.kSize, gauss3)
kimage1 = afwImage.ImageD(gaussKernel1.getDimensions())
ksum1 = gaussKernel1.computeImage(kimage1, False)
kimage2 = afwImage.ImageD(gaussKernel2.getDimensions())
ksum2 = gaussKernel2.computeImage(kimage2, False)
kimage3 = afwImage.ImageD(gaussKernel3.getDimensions())
ksum3 = gaussKernel3.computeImage(kimage3, False)
self.assertNotEqual(ksum1, 1.)
self.assertNotEqual(ksum2, 1.)
self.assertNotEqual(ksum3, 1.)
# no constraints on first kernels norm
self.assertNotEqual(num.sum(num.ravel(kimage2.getArray())**2), 1.)
self.assertNotEqual(num.sum(num.ravel(kimage3.getArray())**2), 1.)
basisListIn = []
basisListIn.append(gaussKernel1)
basisListIn.append(gaussKernel2)
basisListIn.append(gaussKernel3)
# outputs
basisListOut = ipDiffim.renormalizeKernelList(basisListIn)
gaussKernel1 = basisListOut[0]
gaussKernel2 = basisListOut[1]
gaussKernel3 = basisListOut[2]
ksum1 = gaussKernel1.computeImage(kimage1, False)
ksum2 = gaussKernel2.computeImage(kimage2, False)
ksum3 = gaussKernel3.computeImage(kimage3, False)
self.assertAlmostEqual(ksum1, 1.)
self.assertAlmostEqual(ksum2, 0.)
self.assertAlmostEqual(ksum3, 0.)
# no constraints on first kernels norm
self.assertAlmostEqual(num.sum(num.ravel(kimage2.getArray())**2), 1.)
self.assertAlmostEqual(num.sum(num.ravel(kimage3.getArray())**2), 1.)
