def testRegularizationSidelobes(self):
"""Test that artificial chromatic sidelobes are suppressed.
"""
clampFrequency = 2.
regularizationWidth = 2
noiseLevel = 0.1
sourceAmplitude = 100.
modelImages = self.makeTestImages(seed=5, nSrc=5, psfSize=3., noiseLevel=noiseLevel,
detectionSigma=5., sourceSigma=sourceAmplitude, fluxRange=2.)
templateImage = np.mean([model.array for model in modelImages], axis=0)
sidelobeImages = self.makeTestImages(seed=5, nSrc=5, psfSize=1.5, noiseLevel=noiseLevel/10.,
detectionSigma=5., sourceSigma=sourceAmplitude*5., fluxRange=2.)
statsCtrl = self.prepareStats()
signList = [-1., 0., 1.]
sidelobeShift = (0., 4.)
for model, sidelobe, sign in zip(modelImages, sidelobeImages, signList):
sidelobe.array *= sign
model.array += applyDcr(sidelobe.array, sidelobeShift, useInverse=False)
model.array += applyDcr(sidelobe.array, sidelobeShift, useInverse=True)
dcrModels = DcrModel(modelImages=modelImages, mask=self.mask)
refModels = [dcrModels[subfilter].clone() for subfilter in range(self.dcrNumSubfilters)]
dcrModels.regularizeModelFreq(modelImages, self.bbox, statsCtrl, clampFrequency,
regularizationWidth=regularizationWidth)
for model, refModel, sign in zip(modelImages, refModels, signList):
# Make sure the test parameters do reduce the outliers
self.assertGreater(np.sum(np.abs(refModel.array - templateImage)),
np.sum(np.abs(model.array - templateImage)))
def testRegularizationSidelobes(self):
"""Test that artificial chromatic sidelobes are suppressed.
"""
clampFrequency = 2.
regularizationWidth = 2
noiseLevel = 0.1
sourceAmplitude = 100.
modelImages = self.makeTestImages(seed=5, nSrc=5, psfSize=3., noiseLevel=noiseLevel,
detectionSigma=5., sourceSigma=sourceAmplitude, fluxRange=2.)
templateImage = np.mean([model.array for model in modelImages], axis=0)
sidelobeImages = self.makeTestImages(seed=5, nSrc=5, psfSize=1.5, noiseLevel=noiseLevel/10.,
detectionSigma=5., sourceSigma=sourceAmplitude*5., fluxRange=2.)
statsCtrl = self.prepareStats()
signList = [-1., 0., 1.]
sidelobeShift = (0., 4.)
for model, sidelobe, sign in zip(modelImages, sidelobeImages, signList):
sidelobe.array *= sign
model.array += applyDcr(sidelobe.array, sidelobeShift, useInverse=False)
model.array += applyDcr(sidelobe.array, sidelobeShift, useInverse=True)
dcrModels = DcrModel(modelImages=modelImages, mask=self.mask)
refModels = [dcrModels[subfilter].clone() for subfilter in range(self.dcrNumSubfilters)]
dcrModels.regularizeModelFreq(modelImages, self.bbox, statsCtrl, clampFrequency,
regularizationWidth=regularizationWidth)
for model, refModel, sign in zip(modelImages, refModels, signList):
# Make sure the test parameters do reduce the outliers
self.assertGreater(np.sum(np.abs(refModel.array - templateImage)),
np.sum(np.abs(model.array - templateImage)))
def testApplyDcr(self):
"""Test that the image transformation reduces to a simple shift.
"""
dxVals = [-2, 1, 0, 1, 2]
dyVals = [-2, 1, 0, 1, 2]
x0 = 13
y0 = 27
inputImage = afwImage.MaskedImageF(self.bbox)
image = inputImage.image.array
image[y0, x0] = 1.
for dx in dxVals:
for dy in dyVals:
shift = (dy, dx)
shiftedImage = applyDcr(image, shift, useInverse=False)
# Create a blank reference image, and add the fake point source at the shifted location.
refImage = afwImage.MaskedImageF(self.bbox)
refImage.image.array[y0 + dy, x0 + dx] = 1.
self.assertFloatsAlmostEqual(shiftedImage, refImage.image.array, rtol=1e-12, atol=1e-12)
def testApplyDcr(self):
"""Test that the image transformation reduces to a simple shift.
"""
dxVals = [-2, 1, 0, 1, 2]
dyVals = [-2, 1, 0, 1, 2]
x0 = 13
y0 = 27
inputImage = afwImage.MaskedImageF(self.bbox)
image = inputImage.image.array
image[y0, x0] = 1.
for dx in dxVals:
for dy in dyVals:
shift = (dy, dx)
shiftedImage = applyDcr(image, shift, useInverse=False)
# Create a blank reference image, and add the fake point source at the shifted location.
refImage = afwImage.MaskedImageF(self.bbox)
refImage.image.array[y0 + dy, x0 + dx] = 1.
self.assertFloatsAlmostEqual(shiftedImage, refImage.image.array, rtol=1e-12, atol=1e-12)