def testFourNoVariation(self):
self.policy.set('constantVarianceWeighting', True)
self.policy.set('spatialKernelOrder', 0)
self.policy.set('spatialBgOrder', 0)
# Place candidate footprints within the spatial grid
for fp in self.footprints:
bbox = fp.getBBox()
# Grab the centers in the parent's coordinate system
xC = int(0.5 * ( bbox.getMinX() + bbox.getMaxX() ))
yC = int(0.5 * ( bbox.getMinY() + bbox.getMaxY() ))
bbox = afwGeom.Box2I(afwGeom.Point2I(int(xC)-24, int(yC)-24), afwGeom.Extent2I(49, 49))
tsmi = afwImage.MaskedImageF(self.tmi, bbox, afwImage.LOCAL)
ssmi = afwImage.MaskedImageF(self.smi, bbox, afwImage.LOCAL)
# Hotpants centroids go from -1 to 1
if xC > 90 and yC > 90:
cand = ipDiffim.makeKernelCandidate( (xC - 0.5 * self.smi.getWidth()) /
(0.5 * self.smi.getWidth()),
(yC - 0.5 * self.smi.getHeight()) /
(0.5 * self.smi.getHeight()),
tsmi, ssmi, self.policy)
self.kernelCellSet.insertCandidate(cand)
# Visitors
bbox = self.kernelCellSet.getBBox()
bsikv = ipDiffim.BuildSingleKernelVisitorF(self.basisList, self.policy)
bspkv = ipDiffim.BuildSpatialKernelVisitorF(self.basisList, bbox, self.policy)
for cell in self.kernelCellSet.getCellList():
for cand in cell.begin(False): # False = include bad candidates
cand = ipDiffim.cast_KernelCandidateF(cand)
bsikv.processCandidate(cand)
bspkv.processCandidate(cand)
HPspatialSolution = [ 0.969559,
-0.000223,
-0.198374,
0.000012,
-0.000010,
0.000036,
-0.000004,
-0.206751,
0.000012,
0.000004,
0.452304 ]
bspkv.solveLinearEquation()
sk, sb = bspkv.getSolutionPair()
spatialSolution = sk.getKernelParameters()
for i in range(len(spatialSolution)):
self.assertAlmostEqual(HPspatialSolution[i] * self.parity[i], spatialSolution[i], 5)
self.assertAlmostEqual(sb.getParameters()[0], HPspatialSolution[-1], 5)
def makeCandidate(self, kSum, x, y, size=51):
mi1 = afwImage.MaskedImageF(geom.Extent2I(size, size))
mi1.getVariance().set(1.0) # avoid NaNs
mi1[size // 2, size // 2, afwImage.LOCAL] = (1, 0x0, 1)
mi2 = afwImage.MaskedImageF(geom.Extent2I(size, size))
mi2.getVariance().set(1.0) # avoid NaNs
mi2[size // 2, size // 2, afwImage.LOCAL] = (kSum, 0x0, kSum)
kc = ipDiffim.makeKernelCandidate(x, y, mi1, mi2, self.ps)
return kc
def makeCandidate(self, kSum, x, y):
mi1 = afwImage.MaskedImageF(afwGeom.Extent2I(self.size, self.size))
mi1.getVariance().set(1.0) # avoid NaNs
mi1.set(self.size//2, self.size//2, (1, 0x0, 1))
mi2 = afwImage.MaskedImageF(afwGeom.Extent2I(self.size, self.size))
mi2.getVariance().set(1.0) # avoid NaNs
mi2.set(self.size//2, self.size//2, (kSum, 0x0, kSum))
kc = ipDiffim.makeKernelCandidate(x, y, mi1, mi2, self.policy)
return kc
def testFourNoVariation(self):
self.ps['constantVarianceWeighting'] = True
self.ps['spatialKernelOrder'] = 0
self.ps['spatialBgOrder'] = 0
# Place candidate footprints within the spatial grid
for fp in self.footprints:
bbox = fp.getBBox()
# Grab the centers in the parent's coordinate system
xC = int(0.5 * (bbox.getMinX() + bbox.getMaxX()))
yC = int(0.5 * (bbox.getMinY() + bbox.getMaxY()))
bbox = geom.Box2I(geom.Point2I(int(xC) - 24,
int(yC) - 24),
geom.Extent2I(49, 49))
tsmi = afwImage.MaskedImageF(self.tmi, bbox, origin=afwImage.LOCAL)
ssmi = afwImage.MaskedImageF(self.smi, bbox, origin=afwImage.LOCAL)
# Hotpants centroids go from -1 to 1
if xC > 90 and yC > 90:
cand = ipDiffim.makeKernelCandidate(
(xC - 0.5 * self.smi.getWidth()) /
(0.5 * self.smi.getWidth()),
(yC - 0.5 * self.smi.getHeight()) /
(0.5 * self.smi.getHeight()), tsmi, ssmi, self.ps)
self.kernelCellSet.insertCandidate(cand)
# Visitors
bbox = self.kernelCellSet.getBBox()
bsikv = ipDiffim.BuildSingleKernelVisitorF(self.basisList, self.ps)
bspkv = ipDiffim.BuildSpatialKernelVisitorF(self.basisList, bbox,
self.ps)
for cell in self.kernelCellSet.getCellList():
for cand in cell.begin(False): # False = include bad candidates
bsikv.processCandidate(cand)
bspkv.processCandidate(cand)
HPspatialSolution = [
0.969559, -0.000223, -0.198374, 0.000012, -0.000010, 0.000036,
-0.000004, -0.206751, 0.000012, 0.000004, 0.452304
]
bspkv.solveLinearEquation()
sk, sb = bspkv.getSolutionPair()
spatialSolution = sk.getKernelParameters()
for i in range(len(spatialSolution)):
self.assertAlmostEqual(HPspatialSolution[i] * self.parity[i],
spatialSolution[i], 5)
self.assertAlmostEqual(sb.getParameters()[0], HPspatialSolution[-1], 5)
def testRejection(self):
# we need to construct a candidate whose shape does not
# match the underlying basis
#
# so lets just make a kernel list with all the power in
# the center, but the candidate requires some off center
# power
kc1 = self.makeCandidate(1, 0.0, 0.0)
kc2 = self.makeCandidate(2, 0.0, 0.0)
kc3 = self.makeCandidate(3, 0.0, 0.0)
bskv1 = ipDiffim.BuildSingleKernelVisitorF(self.kList, self.ps)
bskv1.processCandidate(kc1)
bskv1.processCandidate(kc2)
bskv1.processCandidate(kc3)
imagePca = ipDiffim.KernelPcaD()
kpv = ipDiffim.KernelPcaVisitorF(imagePca)
kpv.processCandidate(kc1)
kpv.processCandidate(kc2)
kpv.processCandidate(kc3)
kpv.subtractMean()
imagePca.analyze()
eigenKernels = []
eigenKernels.append(kpv.getEigenKernels()[0])
self.assertEqual(len(eigenKernels), 1)
# bogus candidate
mi1 = afwImage.MaskedImageF(geom.Extent2I(self.size, self.size))
mi1.getVariance().set(0.1)
mi1[self.size // 2, self.size // 2, afwImage.LOCAL] = (1, 0x0, 1)
mi2 = afwImage.MaskedImageF(geom.Extent2I(self.size, self.size))
mi2.getVariance().set(0.1)
# make it high enough to make the mean resids large
mi2[self.size // 3, self.size // 3,
afwImage.LOCAL] = (self.size**2, 0x0, 1)
kc4 = ipDiffim.makeKernelCandidate(0, 0, mi1, mi2, self.ps)
self.assertEqual(kc4.getStatus(), afwMath.SpatialCellCandidate.UNKNOWN)
# process with eigenKernels
bskv2 = ipDiffim.BuildSingleKernelVisitorF(eigenKernels, self.ps)
bskv2.setSkipBuilt(False)
bskv2.processCandidate(kc1)
bskv2.processCandidate(kc2)
bskv2.processCandidate(kc3)
bskv2.processCandidate(kc4)
self.assertEqual(bskv2.getNProcessed(), 4)
self.assertEqual(bskv2.getNRejected(), 1)
self.assertEqual(kc1.getStatus(), afwMath.SpatialCellCandidate.GOOD)
self.assertEqual(kc2.getStatus(), afwMath.SpatialCellCandidate.GOOD)
self.assertEqual(kc3.getStatus(), afwMath.SpatialCellCandidate.GOOD)
self.assertEqual(kc4.getStatus(), afwMath.SpatialCellCandidate.BAD)
def testRejection(self):
# we need to construct a candidate whose shape does not
# match the underlying basis
#
# so lets just make a kernel list with all the power in
# the center, but the candidate requires some off center
# power
kc1 = self.makeCandidate(1, 0.0, 0.0)
kc2 = self.makeCandidate(2, 0.0, 0.0)
kc3 = self.makeCandidate(3, 0.0, 0.0)
bskv1 = ipDiffim.BuildSingleKernelVisitorF(self.kList, self.policy)
bskv1.processCandidate(kc1)
bskv1.processCandidate(kc2)
bskv1.processCandidate(kc3)
imagePca = ipDiffim.KernelPcaD()
kpv = ipDiffim.KernelPcaVisitorF(imagePca)
kpv.processCandidate(kc1)
kpv.processCandidate(kc2)
kpv.processCandidate(kc3)
kpv.subtractMean()
imagePca.analyze()
eigenKernels = []
eigenKernels.append(kpv.getEigenKernels()[0])
self.assertEqual(len(eigenKernels), 1)
# bogus candidate
mi1 = afwImage.MaskedImageF(afwGeom.Extent2I(self.size, self.size))
mi1.getVariance().set(0.1)
mi1[self.size//2, self.size//2, afwImage.LOCAL] = (1, 0x0, 1)
mi2 = afwImage.MaskedImageF(afwGeom.Extent2I(self.size, self.size))
mi2.getVariance().set(0.1)
# make it high enough to make the mean resids large
mi2[self.size//3, self.size//3, afwImage.LOCAL] = (self.size**2, 0x0, 1)
kc4 = ipDiffim.makeKernelCandidate(0, 0, mi1, mi2, self.policy)
self.assertEqual(kc4.getStatus(), afwMath.SpatialCellCandidate.UNKNOWN)
# process with eigenKernels
bskv2 = ipDiffim.BuildSingleKernelVisitorF(eigenKernels, self.policy)
bskv2.setSkipBuilt(False)
bskv2.processCandidate(kc1)
bskv2.processCandidate(kc2)
bskv2.processCandidate(kc3)
bskv2.processCandidate(kc4)
self.assertEqual(bskv2.getNProcessed(), 4)
self.assertEqual(bskv2.getNRejected(), 1)
self.assertEqual(kc1.getStatus(), afwMath.SpatialCellCandidate.GOOD)
self.assertEqual(kc2.getStatus(), afwMath.SpatialCellCandidate.GOOD)
self.assertEqual(kc3.getStatus(), afwMath.SpatialCellCandidate.GOOD)
self.assertEqual(kc4.getStatus(), afwMath.SpatialCellCandidate.BAD)
def testInsert(self):
mi = afwImage.MaskedImageF(geom.Extent2I(10, 10))
kc = ipDiffim.makeKernelCandidate(0., 0., mi, mi, self.ps)
kc.setStatus(afwMath.SpatialCellCandidate.GOOD)
sizeCellX = self.ps["sizeCellX"]
sizeCellY = self.ps["sizeCellY"]
kernelCellSet = afwMath.SpatialCellSet(geom.Box2I(geom.Point2I(0, 0), geom.Extent2I(1, 1)),
sizeCellX, sizeCellY)
kernelCellSet.insertCandidate(kc)
nSeen = 0
for cell in kernelCellSet.getCellList():
for cand in cell.begin(True):
self.assertEqual(cand.getStatus(), afwMath.SpatialCellCandidate.GOOD)
nSeen += 1
self.assertEqual(nSeen, 1)
def testInsert(self):
mi = afwImage.MaskedImageF(afwGeom.Extent2I(10, 10))
kc = ipDiffim.makeKernelCandidate(0., 0., mi, mi, self.policy)
kc.setStatus(afwMath.SpatialCellCandidate.GOOD)
sizeCellX = self.policy.get("sizeCellX")
sizeCellY = self.policy.get("sizeCellY")
kernelCellSet = afwMath.SpatialCellSet(afwGeom.Box2I(afwGeom.Point2I(0, 0), afwGeom.Extent2I(1, 1)),
sizeCellX, sizeCellY)
kernelCellSet.insertCandidate(kc)
nSeen = 0
for cell in kernelCellSet.getCellList():
for cand in cell.begin(True):
self.assertEqual(cand.getStatus(), afwMath.SpatialCellCandidate.GOOD)
nSeen += 1
self.assertEqual(nSeen, 1)
def apply(self, policy, visitor, xloc, yloc, tmi, smi):
kc = ipDiffim.makeKernelCandidate(xloc, yloc, tmi, smi, policy)
visitor.processCandidate(kc)
kim = kc.getKernelImage(ipDiffim.KernelCandidateF.RECENT)
diffIm = kc.getDifferenceImage(ipDiffim.KernelCandidateF.RECENT)
kSum = kc.getKsum(ipDiffim.KernelCandidateF.RECENT)
bg = kc.getBackground(ipDiffim.KernelCandidateF.RECENT)
bbox = kc.getKernel(ipDiffim.KernelCandidateF.RECENT).shrinkBBox(diffIm.getBBox(afwImage.LOCAL))
diffIm = afwImage.MaskedImageF(diffIm, bbox, origin=afwImage.LOCAL)
self.dStats.apply(diffIm)
dmean = afwMath.makeStatistics(diffIm.getImage(), afwMath.MEAN).getValue()
dstd = afwMath.makeStatistics(diffIm.getImage(), afwMath.STDEV).getValue()
vmean = afwMath.makeStatistics(diffIm.getVariance(), afwMath.MEAN).getValue()
return kSum, bg, dmean, dstd, vmean, kim, diffIm, kc
def apply(self, policy, visitor, xloc, yloc, tmi, smi):
kc = ipDiffim.makeKernelCandidate(xloc, yloc, tmi, smi, policy)
visitor.processCandidate(kc)
kim = kc.getKernelImage(ipDiffim.KernelCandidateF.RECENT)
diffIm = kc.getDifferenceImage(ipDiffim.KernelCandidateF.RECENT)
kSum = kc.getKsum(ipDiffim.KernelCandidateF.RECENT)
bg = kc.getBackground(ipDiffim.KernelCandidateF.RECENT)
bbox = kc.getKernel(ipDiffim.KernelCandidateF.RECENT).shrinkBBox(diffIm.getBBox(afwImage.LOCAL))
diffIm = afwImage.MaskedImageF(diffIm, bbox, afwImage.LOCAL)
self.dStats.apply(diffIm)
dmean = afwMath.makeStatistics(diffIm.getImage(), afwMath.MEAN).getValue()
dstd = afwMath.makeStatistics(diffIm.getImage(), afwMath.STDEV).getValue()
vmean = afwMath.makeStatistics(diffIm.getVariance(), afwMath.MEAN).getValue()
return kSum, bg, dmean, dstd, vmean, kim, diffIm, kc
def stamps(self, exp1, exp2):
"""Find suitable stamps
@param[in] exp1 First exposure of interest
@param[in] exp2 Second exposure of interest
@output Cell set
"""
policy = self.config['diff'].getPolicy()
# XXX The following was cut from lsst.ip.diffim.createPsfMatchingKernel, since that does the stamp
# identification and kernel solution within the same function, while one might imagine overriding one
# of these with some other method.
# Object to store the KernelCandidates for spatial modeling
kernelCellSet = afwMath.SpatialCellSet(exp1.getBBox(afwImage.PARENT),
policy.getInt("sizeCellX"),
policy.getInt("sizeCellY"))
# Candidate source footprints to use for Psf matching
footprints = diffim.getCollectionOfFootprintsForPsfMatching(
exp2, exp1, policy)
# Place candidate footprints within the spatial grid
for fp in footprints:
bbox = fp.getBBox()
# Grab the centers in the parent's coordinate system
xC = 0.5 * (bbox.getMinX() + bbox.getMaxX())
yC = 0.5 * (bbox.getMinY() + bbox.getMaxY())
# Since the footprint is in the parent's coordinate system,
# while the BBox uses the child's coordinate system.
bbox.shift(-afwGeom.Extent2I(exp2.getXY0()))
tmi = afwImage.MaskedImageF(exp2, bbox)
smi = afwImage.MaskedImageF(exp1, bbox)
cand = diffim.makeKernelCandidate(xC, yC, tmi, smi)
kernelCellSet.insertCandidate(cand)
return kernelCellSet
def testAllBgVariation2(self):
# OK, I ran HP on all the things in this image. Enough for
# second order spatial variation
self.ps['constantVarianceWeighting'] = True
self.ps['spatialKernelOrder'] = 0
self.ps['spatialBgOrder'] = 2
# Ignore the whole kernelCellSet thing
cands = []
for fp in self.footprints:
bbox = fp.getBBox()
# Grab the centers in the parent's coordinate system
xC = int(0.5 * (bbox.getMinX() + bbox.getMaxX()))
yC = int(0.5 * (bbox.getMinY() + bbox.getMaxY()))
bbox = geom.Box2I(geom.Point2I(int(xC) - 24,
int(yC) - 24),
geom.Extent2I(49, 49))
tsmi = afwImage.MaskedImageF(self.tmi, bbox, origin=afwImage.LOCAL)
ssmi = afwImage.MaskedImageF(self.smi, bbox, origin=afwImage.LOCAL)
# Hotpants centroids go from -1 to 1
cand = ipDiffim.makeKernelCandidate(
(xC - 0.5 * self.smi.getWidth()) / (0.5 * self.smi.getWidth()),
(yC - 0.5 * self.smi.getHeight()) /
(0.5 * self.smi.getHeight()), tsmi, ssmi, self.ps)
cands.append(cand)
# Visitors
bbox = self.kernelCellSet.getBBox()
bsikv = ipDiffim.BuildSingleKernelVisitorF(self.basisList, self.ps)
bspkv = ipDiffim.BuildSpatialKernelVisitorF(self.basisList, bbox,
self.ps)
for cand in cands:
bsikv.processCandidate(cand)
bspkv.processCandidate(cand)
HPspatialSolution = [
0.968505, -0.000053, -0.206505, 0.000005, 0.000062, 0.000028,
-0.000004, -0.206135, 0.000005, 0.000001,
[0.812488, 0.096456, -1.140900, 0.132670, -0.571923, -0.284670]
]
bspkv.solveLinearEquation()
sk, sb = bspkv.getSolutionPair()
spatialSolution = sk.getKernelParameters()
# Kernel
for i in range(len(spatialSolution)):
self.assertAlmostEqual(HPspatialSolution[i] * self.parity[i],
spatialSolution[i], 5)
# Bg
# Ordering of second order terms is just messy
spReorder = [0, 3, 1, 5, 4, 2]
spatialSolution = sb.getParameters()
for i in range(len(spatialSolution)):
self.assertAlmostEqual(HPspatialSolution[-1][spReorder[i]],
spatialSolution[i], 5)
elif switch == 'B':
# AL with ~320 bases
config = subconfigAL
config.alardDegGauss = (15, 10, 5)
elif switch == 'C':
config = subconfigDF
config.useRegularization = False
kList = ipDiffim.makeKernelBasisList(config)
ps = pexConfig.makePropertySet(config)
bskv = ipDiffim.BuildSingleKernelVisitorF(kList, ps)
# TEST 1
tmi, smi = makeTest1(doAddNoise)
kc = ipDiffim.makeKernelCandidate(0.0, 0.0, tmi, smi, ps)
bskv.processCandidate(kc)
kernel = kc.getKernel(ipDiffim.KernelCandidateF.ORIG)
kimage = afwImage.ImageD(kernel.getDimensions())
kernel.computeImage(kimage, False)
diffim = kc.getDifferenceImage(ipDiffim.KernelCandidateF.ORIG)
afwDisplay.Display(frame=fnum).mtv(tmi)
fnum += 1
afwDisplay.Display(frame=fnum).mtv(smi)
fnum += 1
afwDisplay.Display(frame=fnum).mtv(kimage)
fnum += 1
afwDisplay.Display(frame=fnum).mtv(diffim)
fnum += 1
def testAllVariation2(self):
# OK, I ran HP on all the things in this image. Enough for
# second order spatial variation
self.policy.set('constantVarianceWeighting', True)
self.policy.set('spatialKernelOrder', 2)
self.policy.set('spatialBgOrder', 2)
# Ignore the whole kernelCellSet thing
cands = []
for fp in self.footprints:
bbox = fp.getBBox()
# Grab the centers in the parent's coordinate system
xC = int(0.5 * (bbox.getMinX() + bbox.getMaxX()))
yC = int(0.5 * (bbox.getMinY() + bbox.getMaxY()))
bbox = afwGeom.Box2I(afwGeom.Point2I(int(xC)-24, int(yC)-24), afwGeom.Extent2I(49, 49))
tsmi = afwImage.MaskedImageF(self.tmi, bbox, origin=afwImage.LOCAL)
ssmi = afwImage.MaskedImageF(self.smi, bbox, origin=afwImage.LOCAL)
# Hotpants centroids go from -1 to 1
cand = ipDiffim.makeKernelCandidate((xC - 0.5 * self.smi.getWidth()) /
(0.5 * self.smi.getWidth()),
(yC - 0.5 * self.smi.getHeight()) /
(0.5 * self.smi.getHeight()),
tsmi, ssmi, self.policy)
cands.append(cand)
# Visitors
bbox = self.kernelCellSet.getBBox()
bsikv = ipDiffim.BuildSingleKernelVisitorF(self.basisList, self.policy)
bspkv = ipDiffim.BuildSpatialKernelVisitorF(self.basisList, bbox, self.policy)
for cand in cands:
bsikv.processCandidate(cand)
bspkv.processCandidate(cand)
HPspatialSolution = [[0.968505,
0.,
0.,
0.,
0.,
0.],
[-0.000094,
-0.000206,
-0.000027,
0.000025,
-0.000705,
0.000162],
[-0.188375,
0.001801,
-0.027534,
0.008718,
0.016346,
-0.033879],
[0.000004,
0.000012,
0.000023,
0.000004,
0.000017,
-0.000020],
[0.000128,
-0.000218,
0.000304,
-0.000038,
-0.000151,
-0.000531],
[-0.000011,
-0.000013,
0.000038,
-0.000017,
0.000133,
0.000093],
[-0.000003,
0.000003,
0.000006,
0.000008,
0.000002,
-0.000010],
[-0.212235,
-0.000856,
0.012246,
-0.010893,
0.049302,
0.008249],
[0.000014,
-0.000002,
-0.000050,
-0.000001,
0.000030,
0.000020],
[-0.000001,
0.000010,
-0.000012,
-0.000007,
0.000015,
0.000019],
[0.812488,
0.096456,
-1.140900,
0.132670,
-0.571923,
-0.284670]]
bspkv.solveLinearEquation()
sk, sb = bspkv.getSolutionPair()
spatialSolution = sk.getSpatialParameters()
# Kernel
spReorder = [0, 3, 1, 5, 4, 2]
for i in range(len(spatialSolution)):
for j in range(len(spReorder)):
self.assertAlmostEqual(HPspatialSolution[i][spReorder[j]] * self.parity[i],
spatialSolution[i][j], 5)
# Bg
spatialSolution = sb.getParameters()
for i in range(len(spatialSolution)):
self.assertAlmostEqual(HPspatialSolution[-1][spReorder[i]], spatialSolution[i], 5)
def testAllBgVariation2(self):
# OK, I ran HP on all the things in this image. Enough for
# second order spatial variation
self.policy.set('constantVarianceWeighting', True)
self.policy.set('spatialKernelOrder', 0)
self.policy.set('spatialBgOrder', 2)
# Ignore the whole kernelCellSet thing
cands = []
for fp in self.footprints:
bbox = fp.getBBox()
# Grab the centers in the parent's coordinate system
xC = int(0.5 * (bbox.getMinX() + bbox.getMaxX()))
yC = int(0.5 * (bbox.getMinY() + bbox.getMaxY()))
bbox = afwGeom.Box2I(afwGeom.Point2I(int(xC)-24, int(yC)-24), afwGeom.Extent2I(49, 49))
tsmi = afwImage.MaskedImageF(self.tmi, bbox, origin=afwImage.LOCAL)
ssmi = afwImage.MaskedImageF(self.smi, bbox, origin=afwImage.LOCAL)
# Hotpants centroids go from -1 to 1
cand = ipDiffim.makeKernelCandidate((xC - 0.5 * self.smi.getWidth()) /
(0.5 * self.smi.getWidth()),
(yC - 0.5 * self.smi.getHeight()) /
(0.5 * self.smi.getHeight()),
tsmi, ssmi, self.policy)
cands.append(cand)
# Visitors
bbox = self.kernelCellSet.getBBox()
bsikv = ipDiffim.BuildSingleKernelVisitorF(self.basisList, self.policy)
bspkv = ipDiffim.BuildSpatialKernelVisitorF(self.basisList, bbox, self.policy)
for cand in cands:
bsikv.processCandidate(cand)
bspkv.processCandidate(cand)
HPspatialSolution = [0.968505,
-0.000053,
-0.206505,
0.000005,
0.000062,
0.000028,
-0.000004,
-0.206135,
0.000005,
0.000001,
[0.812488,
0.096456,
-1.140900,
0.132670,
-0.571923,
-0.284670]]
bspkv.solveLinearEquation()
sk, sb = bspkv.getSolutionPair()
spatialSolution = sk.getKernelParameters()
# Kernel
for i in range(len(spatialSolution)):
self.assertAlmostEqual(HPspatialSolution[i] * self.parity[i], spatialSolution[i], 5)
# Bg
# Ordering of second order terms is just messy
spReorder = [0, 3, 1, 5, 4, 2]
spatialSolution = sb.getParameters()
for i in range(len(spatialSolution)):
self.assertAlmostEqual(HPspatialSolution[-1][spReorder[i]], spatialSolution[i], 5)
def testFourVariation(self):
self.policy.set('constantVarianceWeighting', True)
self.policy.set('spatialKernelOrder', 1)
self.policy.set('spatialBgOrder', 1)
# Place candidate footprints within the spatial grid
for fp in self.footprints:
bbox = fp.getBBox()
# Grab the centers in the parent's coordinate system
xC = int(0.5 * (bbox.getMinX() + bbox.getMaxX()))
yC = int(0.5 * (bbox.getMinY() + bbox.getMaxY()))
bbox = afwGeom.Box2I(afwGeom.Point2I(int(xC)-24, int(yC)-24), afwGeom.Extent2I(49, 49))
tsmi = afwImage.MaskedImageF(self.tmi, bbox, origin=afwImage.LOCAL)
ssmi = afwImage.MaskedImageF(self.smi, bbox, origin=afwImage.LOCAL)
# Hotpants centroids go from -1 to 1
if xC > 90 and yC > 90:
cand = ipDiffim.makeKernelCandidate((xC - 0.5 * self.smi.getWidth()) /
(0.5 * self.smi.getWidth()),
(yC - 0.5 * self.smi.getHeight()) /
(0.5 * self.smi.getHeight()),
tsmi, ssmi, self.policy)
self.kernelCellSet.insertCandidate(cand)
# Visitors
bbox = self.kernelCellSet.getBBox()
bsikv = ipDiffim.BuildSingleKernelVisitorF(self.basisList, self.policy)
bspkv = ipDiffim.BuildSpatialKernelVisitorF(self.basisList, bbox, self.policy)
for cell in self.kernelCellSet.getCellList():
for cand in cell.begin(False): # False = include bad candidates
bsikv.processCandidate(cand)
bspkv.processCandidate(cand)
HPspatialSolution = [[0.969559,
0.,
0.],
[-0.000082,
-0.000620,
0.000185],
[-0.197749,
0.001418,
-0.003321],
[0.000002,
0.000049,
-0.000016],
[0.000211,
-0.000283,
-0.000397],
[0.000034,
0.000002,
0.000006],
[-0.000013,
0.000041,
-0.000010],
[-0.220238,
0.028395,
0.013148],
[0.000019,
-0.000025,
0.000003],
[0.000003,
0.000000,
0.000005],
[0.782113,
-0.910963,
-0.106636]]
bspkv.solveLinearEquation()
sk, sb = bspkv.getSolutionPair()
spatialSolution = sk.getSpatialParameters()
for i in range(len(spatialSolution)):
# HP and LSST switch the order x<->y
self.assertAlmostEqual(HPspatialSolution[i][0] * self.parity[i], spatialSolution[i][0], 5)
self.assertAlmostEqual(HPspatialSolution[i][1] * self.parity[i], spatialSolution[i][2], 5)
self.assertAlmostEqual(HPspatialSolution[i][2] * self.parity[i], spatialSolution[i][1], 5)
self.assertAlmostEqual(sb.getParameters()[0], HPspatialSolution[-1][0], 5)
self.assertAlmostEqual(sb.getParameters()[1], HPspatialSolution[-1][2], 5) # x<->y
self.assertAlmostEqual(sb.getParameters()[2], HPspatialSolution[-1][1], 5) # x<->y
def testFourBgVariation(self):
# OK, so these can end up a bit different due to how HP and
# LSST represent the background in the matrix math. HP has
# each pixel have its own coordinate (which goes from -1 to 1
# across the entire image, by the way), whereas we give all
# the LSST pixels within a stamp the same coordinate.
# To make this comparison, I go ahead and edit the Hotpants
# code to give each pixel the same weight. For reference this
# is in fillStamp() and I replace:
#
# //xf = (i - rPixX2) / rPixX2;
# xf = (xi - rPixX2) / rPixX2;
#
# //yf = (j - rPixY2) / rPixY2;
# yf = (yi - rPixY2) / rPixY2;
self.policy.set('constantVarianceWeighting', True)
self.policy.set('spatialKernelOrder', 0)
self.policy.set('spatialBgOrder', 1)
# Place candidate footprints within the spatial grid
for fp in self.footprints:
bbox = fp.getBBox()
# Grab the centers in the parent's coordinate system
xC = int(0.5 * (bbox.getMinX() + bbox.getMaxX()))
yC = int(0.5 * (bbox.getMinY() + bbox.getMaxY()))
bbox = afwGeom.Box2I(afwGeom.Point2I(int(xC)-24, int(yC)-24), afwGeom.Extent2I(49, 49))
tsmi = afwImage.MaskedImageF(self.tmi, bbox, origin=afwImage.LOCAL)
ssmi = afwImage.MaskedImageF(self.smi, bbox, origin=afwImage.LOCAL)
# Hotpants centroids go from -1 to 1
if xC > 90 and yC > 90:
cand = ipDiffim.makeKernelCandidate((xC - 0.5 * self.smi.getWidth()) /
(0.5 * self.smi.getWidth()),
(yC - 0.5 * self.smi.getHeight()) /
(0.5 * self.smi.getHeight()),
tsmi, ssmi, self.policy)
# print 'OBJECT', cand.getId(), 'AT', xC, yC, cand.getXCenter(), cand.getYCenter()
self.kernelCellSet.insertCandidate(cand)
# Visitors
bbox = self.kernelCellSet.getBBox()
bsikv = ipDiffim.BuildSingleKernelVisitorF(self.basisList, self.policy)
bspkv = ipDiffim.BuildSpatialKernelVisitorF(self.basisList, bbox, self.policy)
for cell in self.kernelCellSet.getCellList():
for cand in cell.begin(False): # False = include bad candidates
bsikv.processCandidate(cand)
bspkv.processCandidate(cand)
HPspatialSolution = [0.969559,
-0.000223,
-0.198374,
0.000012,
-0.000010,
0.000036,
-0.000004,
-0.206751,
0.000012,
0.000004,
[0.782113,
-0.910963,
-0.106636]]
bspkv.solveLinearEquation()
sk, sb = bspkv.getSolutionPair()
spatialSolution = sk.getKernelParameters()
for i in range(len(spatialSolution)):
self.assertAlmostEqual(HPspatialSolution[i] * self.parity[i], spatialSolution[i], 5)
self.assertAlmostEqual(sb.getParameters()[0], HPspatialSolution[-1][0], 5)
self.assertAlmostEqual(sb.getParameters()[1], HPspatialSolution[-1][2], 5) # x<->y
self.assertAlmostEqual(sb.getParameters()[2], HPspatialSolution[-1][1], 5) # x<->y
def testSingleNoVariation(self):
self.policy.set('constantVarianceWeighting', True)
self.policy.set('spatialKernelOrder', 0)
self.policy.set('spatialBgOrder', 0)
# Place candidate footprints within the spatial grid
for fp in self.footprints:
bbox = fp.getBBox()
# Grab the centers in the parent's coordinate system
xC = int(0.5 * (bbox.getMinX() + bbox.getMaxX()))
yC = int(0.5 * (bbox.getMinY() + bbox.getMaxY()))
bbox = afwGeom.Box2I(afwGeom.Point2I(int(xC) - 24,
int(yC) - 24),
afwGeom.Extent2I(49, 49))
tsmi = afwImage.MaskedImageF(self.tmi, bbox, origin=afwImage.LOCAL)
ssmi = afwImage.MaskedImageF(self.smi, bbox, origin=afwImage.LOCAL)
# Hotpants centroids go from -1 to 1
# Only one passes
if xC > 150 and yC > 150:
cand = ipDiffim.makeKernelCandidate(
(xC - 0.5 * self.smi.getWidth()) /
(0.5 * self.smi.getWidth()),
(yC - 0.5 * self.smi.getHeight()) /
(0.5 * self.smi.getHeight()), tsmi, ssmi, self.policy)
self.kernelCellSet.insertCandidate(cand)
# Visitors
bbox = self.kernelCellSet.getBBox()
bsikv = ipDiffim.BuildSingleKernelVisitorF(self.basisList, self.policy)
bspkv = ipDiffim.BuildSpatialKernelVisitorF(self.basisList, bbox,
self.policy)
for cell in self.kernelCellSet.getCellList():
for cand in cell.begin(False): # False = include bad candidates
bsikv.processCandidate(cand)
bspkv.processCandidate(cand)
HPsingleSolution = [
0.959086, -0.000344, -0.197758, 0.000019, -0.000172, 0.000053,
0.000018, -0.192776, 0.000000, 0.000001, 0.602642
]
HPspatialSolution = HPsingleSolution
singleSolution = cand.getKernel(
ipDiffim.KernelCandidateF.RECENT).getKernelParameters()
for i in range(len(singleSolution)):
self.assertAlmostEqual(HPsingleSolution[i] * self.parity[i],
singleSolution[i], 5)
bspkv.solveLinearEquation()
sk, sb = bspkv.getSolutionPair()
spatialSolution = sk.getKernelParameters()
for i in range(len(spatialSolution)):
self.assertAlmostEqual(HPspatialSolution[i] * self.parity[i],
spatialSolution[i], 6)
self.assertAlmostEqual(sb.getParameters()[0], HPspatialSolution[-1], 5)
def testFourBgVariation(self):
# OK, so these can end up a bit different due to how HP and
# LSST represent the background in the matrix math. HP has
# each pixel have its own coordinate (which goes from -1 to 1
# across the entire image, by the way), whereas we give all
# the LSST pixels within a stamp the same coordinate.
# To make this comparison, I go ahead and edit the Hotpants
# code to give each pixel the same weight. For reference this
# is in fillStamp() and I replace:
#
# //xf = (i - rPixX2) / rPixX2;
# xf = (xi - rPixX2) / rPixX2;
#
# //yf = (j - rPixY2) / rPixY2;
# yf = (yi - rPixY2) / rPixY2;
self.ps['constantVarianceWeighting'] = True
self.ps['spatialKernelOrder'] = 0
self.ps['spatialBgOrder'] = 1
# Place candidate footprints within the spatial grid
for fp in self.footprints:
bbox = fp.getBBox()
# Grab the centers in the parent's coordinate system
xC = int(0.5 * (bbox.getMinX() + bbox.getMaxX()))
yC = int(0.5 * (bbox.getMinY() + bbox.getMaxY()))
bbox = geom.Box2I(geom.Point2I(int(xC) - 24,
int(yC) - 24),
geom.Extent2I(49, 49))
tsmi = afwImage.MaskedImageF(self.tmi, bbox, origin=afwImage.LOCAL)
ssmi = afwImage.MaskedImageF(self.smi, bbox, origin=afwImage.LOCAL)
# Hotpants centroids go from -1 to 1
if xC > 90 and yC > 90:
cand = ipDiffim.makeKernelCandidate(
(xC - 0.5 * self.smi.getWidth()) /
(0.5 * self.smi.getWidth()),
(yC - 0.5 * self.smi.getHeight()) /
(0.5 * self.smi.getHeight()), tsmi, ssmi, self.ps)
# print 'OBJECT', cand.getId(), 'AT', xC, yC, cand.getXCenter(), cand.getYCenter()
self.kernelCellSet.insertCandidate(cand)
# Visitors
bbox = self.kernelCellSet.getBBox()
bsikv = ipDiffim.BuildSingleKernelVisitorF(self.basisList, self.ps)
bspkv = ipDiffim.BuildSpatialKernelVisitorF(self.basisList, bbox,
self.ps)
for cell in self.kernelCellSet.getCellList():
for cand in cell.begin(False): # False = include bad candidates
bsikv.processCandidate(cand)
bspkv.processCandidate(cand)
HPspatialSolution = [
0.969559, -0.000223, -0.198374, 0.000012, -0.000010, 0.000036,
-0.000004, -0.206751, 0.000012, 0.000004,
[0.782113, -0.910963, -0.106636]
]
bspkv.solveLinearEquation()
sk, sb = bspkv.getSolutionPair()
spatialSolution = sk.getKernelParameters()
for i in range(len(spatialSolution)):
self.assertAlmostEqual(HPspatialSolution[i] * self.parity[i],
spatialSolution[i], 5)
self.assertAlmostEqual(sb.getParameters()[0], HPspatialSolution[-1][0],
5)
self.assertAlmostEqual(sb.getParameters()[1], HPspatialSolution[-1][2],
5) # x<->y
self.assertAlmostEqual(sb.getParameters()[2], HPspatialSolution[-1][1],
5) # x<->y
elif switch == 'B':
# AL with ~320 bases
config = subconfigAL
config.alardDegGauss = (15, 10, 5)
elif switch == 'C':
config = subconfigDF
config.useRegularization = False
kList = ipDiffim.makeKernelBasisList(config)
policy = pexConfig.makePolicy(config)
bskv = ipDiffim.BuildSingleKernelVisitorF(kList, policy)
# TEST 1
tmi, smi = makeTest1(doAddNoise)
kc = ipDiffim.makeKernelCandidate(0.0, 0.0, tmi, smi, policy)
bskv.processCandidate(kc)
kernel = kc.getKernel(ipDiffim.KernelCandidateF.ORIG)
kimage = afwImage.ImageD(kernel.getDimensions())
kernel.computeImage(kimage, False)
diffim = kc.getDifferenceImage(ipDiffim.KernelCandidateF.ORIG)
afwDisplay.Display(frame=fnum).mtv(tmi)
fnum += 1
afwDisplay.Display(frame=fnum).mtv(smi)
fnum += 1
afwDisplay.Display(frame=fnum).mtv(kimage)
fnum += 1
afwDisplay.Display(frame=fnum).mtv(diffim)
fnum += 1
def testFourVariation(self):
self.ps['constantVarianceWeighting'] = True
self.ps['spatialKernelOrder'] = 1
self.ps['spatialBgOrder'] = 1
# Place candidate footprints within the spatial grid
for fp in self.footprints:
bbox = fp.getBBox()
# Grab the centers in the parent's coordinate system
xC = int(0.5 * (bbox.getMinX() + bbox.getMaxX()))
yC = int(0.5 * (bbox.getMinY() + bbox.getMaxY()))
bbox = geom.Box2I(geom.Point2I(int(xC) - 24,
int(yC) - 24),
geom.Extent2I(49, 49))
tsmi = afwImage.MaskedImageF(self.tmi, bbox, origin=afwImage.LOCAL)
ssmi = afwImage.MaskedImageF(self.smi, bbox, origin=afwImage.LOCAL)
# Hotpants centroids go from -1 to 1
if xC > 90 and yC > 90:
cand = ipDiffim.makeKernelCandidate(
(xC - 0.5 * self.smi.getWidth()) /
(0.5 * self.smi.getWidth()),
(yC - 0.5 * self.smi.getHeight()) /
(0.5 * self.smi.getHeight()), tsmi, ssmi, self.ps)
self.kernelCellSet.insertCandidate(cand)
# Visitors
bbox = self.kernelCellSet.getBBox()
bsikv = ipDiffim.BuildSingleKernelVisitorF(self.basisList, self.ps)
bspkv = ipDiffim.BuildSpatialKernelVisitorF(self.basisList, bbox,
self.ps)
for cell in self.kernelCellSet.getCellList():
for cand in cell.begin(False): # False = include bad candidates
bsikv.processCandidate(cand)
bspkv.processCandidate(cand)
HPspatialSolution = [[0.969559, 0., 0.],
[-0.000082, -0.000620, 0.000185],
[-0.197749, 0.001418, -0.003321],
[0.000002, 0.000049, -0.000016],
[0.000211, -0.000283, -0.000397],
[0.000034, 0.000002, 0.000006],
[-0.000013, 0.000041, -0.000010],
[-0.220238, 0.028395, 0.013148],
[0.000019, -0.000025, 0.000003],
[0.000003, 0.000000, 0.000005],
[0.782113, -0.910963, -0.106636]]
bspkv.solveLinearEquation()
sk, sb = bspkv.getSolutionPair()
spatialSolution = sk.getSpatialParameters()
for i in range(len(spatialSolution)):
# HP and LSST switch the order x<->y
self.assertAlmostEqual(HPspatialSolution[i][0] * self.parity[i],
spatialSolution[i][0], 5)
self.assertAlmostEqual(HPspatialSolution[i][1] * self.parity[i],
spatialSolution[i][2], 5)
self.assertAlmostEqual(HPspatialSolution[i][2] * self.parity[i],
spatialSolution[i][1], 5)
self.assertAlmostEqual(sb.getParameters()[0], HPspatialSolution[-1][0],
5)
self.assertAlmostEqual(sb.getParameters()[1], HPspatialSolution[-1][2],
5) # x<->y
self.assertAlmostEqual(sb.getParameters()[2], HPspatialSolution[-1][1],
5) # x<->y
# Add more AL bases (typically 4 3 2)
config = subconfigAL
config.alardDegGauss = (8, 6, 4)
elif switch == 'C':
# Delta function
config = subconfigDF
config.useRegularization = False
kList = ipDiffim.makeKernelBasisList(config)
policy = pexConfig.makePolicy(config)
bskv = ipDiffim.BuildSingleKernelVisitorF(kList, policy)
# TEST 1
tmi, smi = makeTest1(doAddNoise)
kc = ipDiffim.makeKernelCandidate(0.0, 0.0, tmi, smi, policy)
bskv.processCandidate(kc)
kernel = kc.getKernel(ipDiffim.KernelCandidateF.ORIG)
kimage = afwImage.ImageD(kernel.getDimensions())
kernel.computeImage(kimage, False)
diffim = kc.getDifferenceImage(ipDiffim.KernelCandidateF.ORIG)
ds9.mtv(tmi, frame=fnum)
fnum += 1
ds9.mtv(smi, frame=fnum)
fnum += 1
ds9.mtv(kimage, frame=fnum)
fnum += 1
ds9.mtv(diffim, frame=fnum)
fnum += 1
def testAllVariation2(self):
# OK, I ran HP on all the things in this image. Enough for
# second order spatial variation
self.ps['constantVarianceWeighting'] = True
self.ps['spatialKernelOrder'] = 2
self.ps['spatialBgOrder'] = 2
# Ignore the whole kernelCellSet thing
cands = []
for fp in self.footprints:
bbox = fp.getBBox()
# Grab the centers in the parent's coordinate system
xC = int(0.5 * (bbox.getMinX() + bbox.getMaxX()))
yC = int(0.5 * (bbox.getMinY() + bbox.getMaxY()))
bbox = geom.Box2I(geom.Point2I(int(xC) - 24,
int(yC) - 24),
geom.Extent2I(49, 49))
tsmi = afwImage.MaskedImageF(self.tmi, bbox, origin=afwImage.LOCAL)
ssmi = afwImage.MaskedImageF(self.smi, bbox, origin=afwImage.LOCAL)
# Hotpants centroids go from -1 to 1
cand = ipDiffim.makeKernelCandidate(
(xC - 0.5 * self.smi.getWidth()) / (0.5 * self.smi.getWidth()),
(yC - 0.5 * self.smi.getHeight()) /
(0.5 * self.smi.getHeight()), tsmi, ssmi, self.ps)
cands.append(cand)
# Visitors
bbox = self.kernelCellSet.getBBox()
bsikv = ipDiffim.BuildSingleKernelVisitorF(self.basisList, self.ps)
bspkv = ipDiffim.BuildSpatialKernelVisitorF(self.basisList, bbox,
self.ps)
for cand in cands:
bsikv.processCandidate(cand)
bspkv.processCandidate(cand)
HPspatialSolution = [
[0.968505, 0., 0., 0., 0., 0.],
[-0.000094, -0.000206, -0.000027, 0.000025, -0.000705, 0.000162],
[-0.188375, 0.001801, -0.027534, 0.008718, 0.016346, -0.033879],
[0.000004, 0.000012, 0.000023, 0.000004, 0.000017, -0.000020],
[0.000128, -0.000218, 0.000304, -0.000038, -0.000151, -0.000531],
[-0.000011, -0.000013, 0.000038, -0.000017, 0.000133, 0.000093],
[-0.000003, 0.000003, 0.000006, 0.000008, 0.000002, -0.000010],
[-0.212235, -0.000856, 0.012246, -0.010893, 0.049302, 0.008249],
[0.000014, -0.000002, -0.000050, -0.000001, 0.000030, 0.000020],
[-0.000001, 0.000010, -0.000012, -0.000007, 0.000015, 0.000019],
[0.812488, 0.096456, -1.140900, 0.132670, -0.571923, -0.284670]
]
bspkv.solveLinearEquation()
sk, sb = bspkv.getSolutionPair()
spatialSolution = sk.getSpatialParameters()
# Kernel
spReorder = [0, 3, 1, 5, 4, 2]
for i in range(len(spatialSolution)):
for j in range(len(spReorder)):
self.assertAlmostEqual(
HPspatialSolution[i][spReorder[j]] * self.parity[i],
spatialSolution[i][j], 5)
# Bg
spatialSolution = sb.getParameters()
for i in range(len(spatialSolution)):
self.assertAlmostEqual(HPspatialSolution[-1][spReorder[i]],
spatialSolution[i], 5)