def testRegularizationSmallClamp(self):
"""Test that large variations between model planes are reduced.
This also tests that noise-like pixels are not regularized.
"""
clampFrequency = 2
regularizationWidth = 2
fluxRange = 10.
modelImages = self.makeTestImages(fluxRange=fluxRange)
dcrModels = DcrModel(modelImages=modelImages,
mask=self.mask,
effectiveWavelength=self.effectiveWavelength,
bandwidth=self.bandwidth)
newModels = [model.clone() for model in dcrModels]
templateImage = dcrModels.getReferenceImage(self.bbox)
statsCtrl = self.prepareStats()
dcrModels.regularizeModelFreq(newModels, self.bbox, statsCtrl,
clampFrequency, regularizationWidth)
for model, refModel in zip(newModels, dcrModels):
# Make sure the test parameters do reduce the outliers
self.assertGreater(np.max(refModel.array - templateImage),
np.max(model.array - templateImage))
highThreshold = templateImage * clampFrequency
highPix = model.array > highThreshold
highPix = ndimage.morphology.binary_opening(
highPix, iterations=regularizationWidth)
self.assertFalse(np.all(highPix))
lowThreshold = templateImage / clampFrequency
lowPix = model.array < lowThreshold
lowPix = ndimage.morphology.binary_opening(
lowPix, iterations=regularizationWidth)
self.assertFalse(np.all(lowPix))
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 testRegularizationSmallClamp(self):
"""Test that large variations between model planes are reduced.
This also tests that noise-like pixels are not regularized.
"""
clampFrequency = 2
regularizationWidth = 2
fluxRange = 10.
modelImages = self.makeTestImages(fluxRange=fluxRange)
dcrModels = DcrModel(modelImages=modelImages, mask=self.mask)
newModels = [model.clone() for model in dcrModels]
templateImage = dcrModels.getReferenceImage(self.bbox)
statsCtrl = self.prepareStats()
dcrModels.regularizeModelFreq(newModels, self.bbox, statsCtrl, clampFrequency, regularizationWidth)
for model, refModel in zip(newModels, dcrModels):
# Make sure the test parameters do reduce the outliers
self.assertGreater(np.max(refModel.array - templateImage),
np.max(model.array - templateImage))
highThreshold = templateImage*clampFrequency
highPix = model.array > highThreshold
highPix = ndimage.morphology.binary_opening(highPix, iterations=regularizationWidth)
self.assertFalse(np.all(highPix))
lowThreshold = templateImage/clampFrequency
lowPix = model.array < lowThreshold
lowPix = ndimage.morphology.binary_opening(lowPix, iterations=regularizationWidth)
self.assertFalse(np.all(lowPix))