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DM-22814: Remove afw APIs deprecated in DM-17566 #168

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Nov 4, 2020
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DM-22814: Remove afw APIs deprecated in DM-17566 #168

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4 changes: 3 additions & 1 deletion examples/crossCorrelation.py
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Expand Up @@ -228,7 +228,9 @@ def testAutoCorrelation(orderMake, orderFit, inMi=None, display=False):
spatialKernel.setSpatialParameters(kCoeffs)

cMi = afwImage.MaskedImageF(inMi.getDimensions())
afwMath.convolve(cMi, inMi, spatialKernel, True)
convolutionControl = afwMath.ConvolutionControl()
convolutionControl.setDoNormalize(True)
afwMath.convolve(cMi, inMi, spatialKernel, convolutionControl)

if display:
afwDisplay.Display(frame=1).mtv(inMi.getImage())
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4 changes: 3 additions & 1 deletion examples/modelPsfMatchTask.py
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Expand Up @@ -48,7 +48,9 @@ def createImageAndKernel(sigma, psfSize, image):
psf = measAlg.KernelPsf(kernel)

cim = afwImage.ImageF(image.getDimensions())
afwMath.convolve(cim, image, kernel, True)
convolutionControl = afwMath.ConvolutionControl()
convolutionControl.setDoNormalize(True)
afwMath.convolve(cim, image, kernel, convolutionControl)

# Trim off the border pixels
bbox = kernel.shrinkBBox(cim.getBBox(afwImage.LOCAL))
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4 changes: 3 additions & 1 deletion examples/spatialKernel.cc
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Expand Up @@ -72,7 +72,9 @@ int main() {
std::shared_ptr<afwImage::MaskedImage<PixelT>> mimg2(
new afwImage::MaskedImage<PixelT>(mimg1->getDimensions())
);
afwMath::convolve(*mimg2, *mimg1, *spatialKernel, false);
afwMath::ConvolutionControl convolutionControl;
convolutionControl.setDoNormalize(false);
afwMath::convolve(*mimg2, *mimg1, *spatialKernel, convolutionControl);
mimg1->writeFits("mimg1");
mimg2->writeFits("mimg2");

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8 changes: 6 additions & 2 deletions python/lsst/ip/diffim/diffimTools.py
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Expand Up @@ -204,7 +204,9 @@ def makeFakeKernelSet(sizeCell=128, nCell=3,
gaussFunction = afwMath.GaussianFunction2D(tGaussianWidth, tGaussianWidth)
gaussKernel = afwMath.AnalyticKernel(gaussKernelWidth, gaussKernelWidth, gaussFunction)
cim = afwImage.ImageF(tim.getDimensions())
afwMath.convolve(cim, tim, gaussKernel, True)
convolutionControl = afwMath.ConvolutionControl()
convolutionControl.setDoNormalize(True)
afwMath.convolve(cim, tim, gaussKernel, convolutionControl)
tim = cim

# Trim off border pixels
Expand All @@ -221,7 +223,9 @@ def makeFakeKernelSet(sizeCell=128, nCell=3,
sKernel = afwMath.LinearCombinationKernel(basisList[:nToUse], polyFunc)
sKernel.setSpatialParameters(kCoeffs[:nToUse])
sim = afwImage.ImageF(tim.getDimensions())
afwMath.convolve(sim, tim, sKernel, True)
convolutionControl = afwMath.ConvolutionControl()
convolutionControl.setDoNormalize(True)
afwMath.convolve(sim, tim, sKernel, convolutionControl)

# Get the good subregion
bbox = sKernel.shrinkBBox(sim.getBBox(afwImage.LOCAL))
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5 changes: 3 additions & 2 deletions python/lsst/ip/diffim/imagePsfMatch.py
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Expand Up @@ -557,8 +557,9 @@ def matchMaskedImages(self, templateMaskedImage, scienceMaskedImage, candidateLi
spatialSolution, psfMatchingKernel, backgroundModel = self._solve(kernelCellSet, basisList)

psfMatchedMaskedImage = afwImage.MaskedImageF(templateMaskedImage.getBBox())
doNormalize = False
afwMath.convolve(psfMatchedMaskedImage, templateMaskedImage, psfMatchingKernel, doNormalize)
convolutionControl = afwMath.ConvolutionControl()
convolutionControl.setDoNormalize(False)
afwMath.convolve(psfMatchedMaskedImage, templateMaskedImage, psfMatchingKernel, convolutionControl)
return pipeBase.Struct(
matchedImage=psfMatchedMaskedImage,
psfMatchingKernel=psfMatchingKernel,
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5 changes: 3 additions & 2 deletions python/lsst/ip/diffim/modelPsfMatch.py
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Expand Up @@ -360,8 +360,9 @@ def run(self, exposure, referencePsfModel, kernelSum=1.0):

# Normalize the psf-matching kernel while convolving since its magnitude is meaningless
# when PSF-matching one model to another.
doNormalize = True
afwMath.convolve(psfMatchedMaskedImage, maskedImage, psfMatchingKernel, doNormalize)
convolutionControl = afwMath.ConvolutionControl()
convolutionControl.setDoNormalize(True)
afwMath.convolve(psfMatchedMaskedImage, maskedImage, psfMatchingKernel, convolutionControl)

self.log.info("done")
return pipeBase.Struct(psfMatchedExposure=psfMatchedExposure,
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2 changes: 1 addition & 1 deletion python/lsst/ip/diffim/utils.py
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Expand Up @@ -890,7 +890,7 @@ def plotWhisker(results, newWcs):
"""Plot whisker diagram of astromeric offsets between results.matches.
"""
refCoordKey = results.matches[0].first.getTable().getCoordKey()
inCentroidKey = results.matches[0].second.getTable().getCentroidKey()
inCentroidKey = results.matches[0].second.getTable().getCentroidSlot().getMeasKey()
positions = [m.first.get(refCoordKey) for m in results.matches]
residuals = [m.first.get(refCoordKey).getOffsetFrom(
newWcs.pixelToSky(m.second.get(inCentroidKey))) for
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18 changes: 13 additions & 5 deletions src/KernelSolution.cc
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Expand Up @@ -352,8 +352,10 @@ namespace diffim {
/* Iterators over convolved image list and basis list */
typename std::vector<Eigen::MatrixXd>::iterator eiter = convolvedEigenList.begin();
/* Create C_i in the formalism of Alard & Lupton */
afwMath::ConvolutionControl convolutionControl;
convolutionControl.setDoNormalize(false);
for (kiter = basisList.begin(); kiter != basisList.end(); ++kiter, ++eiter) {
afwMath::convolve(cimage, templateImage, **kiter, false); /* cimage stores convolved image */
afwMath::convolve(cimage, templateImage, **kiter, convolutionControl); /* cimage stores convolved image */

Eigen::MatrixXd cMat = imageToEigenMatrix(cimage).block(startRow,
startCol,
Expand Down Expand Up @@ -615,8 +617,10 @@ namespace diffim {
typename std::vector<Eigen::VectorXd>::iterator eiter = convolvedEigenList.begin();

/* Create C_i in the formalism of Alard & Lupton */
afwMath::ConvolutionControl convolutionControl;
convolutionControl.setDoNormalize(false);
for (kiter = basisList.begin(); kiter != basisList.end(); ++kiter, ++eiter) {
afwMath::convolve(cimage, templateImage, **kiter, false); /* cimage stores convolved image */
afwMath::convolve(cimage, templateImage, **kiter, convolutionControl); /* cimage stores convolved image */

ndarray::Array<InputT, 1, 1> arrayC =
ndarray::allocate(ndarray::makeVector(fullFp->getArea()));
Expand Down Expand Up @@ -771,8 +775,10 @@ namespace diffim {
/* Iterators over convolved image list and basis list */
typename std::vector<Eigen::MatrixXd>::iterator eiter = convolvedEigenList.begin();
/* Create C_i in the formalism of Alard & Lupton */
for (kiter = basisList.begin(); kiter != basisList.end(); ++kiter, ++eiter) {
afwMath::convolve(cimage, templateImage, **kiter, false); /* cimage stores convolved image */
afwMath::ConvolutionControl convolutionControl;
convolutionControl.setDoNormalize(false);
for (kiter = basisList.begin(); kiter != basisList.end(); ++kiter, ++eiter) {
afwMath::convolve(cimage, templateImage, **kiter, convolutionControl); /* cimage stores convolved image */

Eigen::MatrixXd cMat = imageToEigenMatrix(cimage).block(startRow,
startCol,
Expand Down Expand Up @@ -1003,8 +1009,10 @@ namespace diffim {
std::vector<Eigen::MatrixXd> convolvedEigenList(nKernelParameters);
typename std::vector<Eigen::MatrixXd>::iterator eiter = convolvedEigenList.begin();
/* Create C_i in the formalism of Alard & Lupton */
afwMath::ConvolutionControl convolutionControl;
convolutionControl.setDoNormalize(false);
for (kiter = basisList.begin(); kiter != basisList.end(); ++kiter, ++eiter) {
afwMath::convolve(cimage, templateImage, **kiter, false); /* cimage stores convolved image */
afwMath::convolve(cimage, templateImage, **kiter, convolutionControl); /* cimage stores convolved image */
Eigen::MatrixXd cMat(totalSize, 1);
cMat.setZero();

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8 changes: 6 additions & 2 deletions tests/test_assessSpatialKernelVisitor.py
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Expand Up @@ -56,7 +56,9 @@ def testGood(self):
ti[50, 50, afwImage.LOCAL] = (1., 0x0, 1.)
sKernel = self.makeSpatialKernel(2)
si = afwImage.MaskedImageF(ti.getDimensions())
afwMath.convolve(si, ti, sKernel, True)
convolutionControl = afwMath.ConvolutionControl()
convolutionControl.setDoNormalize(True)
afwMath.convolve(si, ti, sKernel, convolutionControl)

bbox = geom.Box2I(geom.Point2I(25, 25),
geom.Point2I(75, 75))
Expand Down Expand Up @@ -86,7 +88,9 @@ def testBad(self):
ti[50, 50, afwImage.LOCAL] = (1., 0x0, 1.)
sKernel = self.makeSpatialKernel(2)
si = afwImage.MaskedImageF(ti.getDimensions())
afwMath.convolve(si, ti, sKernel, True)
convolutionControl = afwMath.ConvolutionControl()
convolutionControl.setDoNormalize(True)
afwMath.convolve(si, ti, sKernel, convolutionControl)

bbox = geom.Box2I(geom.Point2I(25, 25),
geom.Point2I(75, 75))
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4 changes: 3 additions & 1 deletion tests/test_imageSubtract.py
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Expand Up @@ -63,7 +63,9 @@ def setUp(self):
self.templateImage = afwImage.MaskedImageF(defImagePath)
self.scienceImage = self.templateImage.Factory(self.templateImage.getDimensions())

afwMath.convolve(self.scienceImage, self.templateImage, self.gaussKernel, False)
convolutionControl = afwMath.ConvolutionControl()
convolutionControl.setDoNormalize(False)
afwMath.convolve(self.scienceImage, self.templateImage, self.gaussKernel, convolutionControl)

def tearDown(self):
del self.subconfig
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17 changes: 11 additions & 6 deletions tests/test_kernelCandidateAndSolution.py
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Expand Up @@ -191,8 +191,8 @@ def testConstructor(self):
def testSourceStats(self):
source = self.ss.addNew()
source.setId(1)
source.set(self.table.getCentroidKey().getX(), 276)
source.set(self.table.getCentroidKey().getY(), 717)
source.set(self.table.getCentroidSlot().getMeasKey().getX(), 276)
source.set(self.table.getCentroidSlot().getMeasKey().getY(), 717)
source.set("slot_PsfFlux_instFlux", 1.)

kc = ipDiffim.KernelCandidateF(source,
Expand All @@ -208,8 +208,8 @@ def testSourceStats(self):
def testSourceConstructor(self):
source = self.ss.addNew()
source.setId(1)
source.set(self.table.getCentroidKey().getX(), 276)
source.set(self.table.getCentroidKey().getY(), 717)
source.set(self.table.getCentroidSlot().getMeasKey().getX(), 276)
source.set(self.table.getCentroidSlot().getMeasKey().getY(), 717)
source.set("slot_PsfFlux_instFlux", 1.)

kc = ipDiffim.KernelCandidateF(source,
Expand Down Expand Up @@ -367,7 +367,10 @@ def testGaussianWithNoise(self):

imX, imY = self.templateExposure2.getMaskedImage().getDimensions()
smi = afwImage.MaskedImageF(geom.Extent2I(imX, imY))
afwMath.convolve(smi, self.templateExposure2.getMaskedImage(), gaussKernel, False)

convolutionControl = afwMath.ConvolutionControl()
convolutionControl.setDoNormalize(False)
afwMath.convolve(smi, self.templateExposure2.getMaskedImage(), gaussKernel, convolutionControl)

bbox = gaussKernel.shrinkBBox(smi.getBBox(afwImage.LOCAL))

Expand Down Expand Up @@ -436,7 +439,9 @@ def testGaussian(self, imsize=50):

# science image
smi = afwImage.MaskedImageF(tmi.getDimensions())
afwMath.convolve(smi, tmi, gaussKernel, False)
convolutionControl = afwMath.ConvolutionControl()
convolutionControl.setDoNormalize(False)
afwMath.convolve(smi, tmi, gaussKernel, convolutionControl)

# get the actual kernel sum (since the image is not infinite)
gscaling = afwMath.makeStatistics(smi, afwMath.SUM).getValue(afwMath.SUM)
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4 changes: 3 additions & 1 deletion tests/test_kernelCandidateDetection.py
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Expand Up @@ -40,7 +40,9 @@ def setUp(self):
self.templateImage = afwImage.MaskedImageF(defImagePath)
self.scienceImage = self.templateImage.Factory(self.templateImage.getDimensions())

afwMath.convolve(self.scienceImage, self.templateImage, self.gaussKernel, False)
convolutionControl = afwMath.ConvolutionControl()
convolutionControl.setDoNormalize(False)
afwMath.convolve(self.scienceImage, self.templateImage, self.gaussKernel, convolutionControl)

def tearDown(self):
del self.config
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