def testMakeBadKernels(self):
"""Attempt to make various invalid kernels; make sure the constructor shows an exception
"""
kWidth = 4
kHeight = 3
gaussFunc1 = afwMath.GaussianFunction1D(1.0)
gaussFunc2 = afwMath.GaussianFunction2D(1.0, 1.0, 0.0)
spFunc = afwMath.PolynomialFunction2D(1)
kernelList = []
kernelList.append(afwMath.FixedKernel(
afwImage.ImageD(lsst.geom.Extent2I(kWidth, kHeight), 0.1)))
kernelList.append(afwMath.FixedKernel(
afwImage.ImageD(lsst.geom.Extent2I(kWidth, kHeight), 0.2)))
for numKernelParams in (2, 4):
spFuncList = []
for ii in range(numKernelParams):
spFuncList.append(spFunc.clone())
try:
afwMath.AnalyticKernel(kWidth, kHeight, gaussFunc2, spFuncList)
self.fail("Should have failed with wrong # of spatial functions")
except pexExcept.Exception:
pass
for numKernelParams in (1, 3):
spFuncList = []
for ii in range(numKernelParams):
spFuncList.append(spFunc.clone())
try:
afwMath.LinearCombinationKernel(kernelList, spFuncList)
self.fail("Should have failed with wrong # of spatial functions")
except pexExcept.Exception:
pass
kParamList = [0.2]*numKernelParams
try:
afwMath.LinearCombinationKernel(kernelList, kParamList)
self.fail("Should have failed with wrong # of kernel parameters")
except pexExcept.Exception:
pass
try:
afwMath.SeparableKernel(
kWidth, kHeight, gaussFunc1, gaussFunc1, spFuncList)
self.fail("Should have failed with wrong # of spatial functions")
except pexExcept.Exception:
pass
for pointX in range(-1, kWidth+2):
for pointY in range(-1, kHeight+2):
if (0 <= pointX < kWidth) and (0 <= pointY < kHeight):
continue
try:
afwMath.DeltaFunctionKernel(
kWidth, kHeight, lsst.geom.Point2I(pointX, pointY))
self.fail("Should have failed with point not on kernel")
except pexExcept.Exception:
pass
def testTicket873(self):
"""Demonstrate ticket 873: convolution of a MaskedImage with a spatially varying
LinearCombinationKernel of basis kernels with low covariance gives incorrect variance.
"""
# create spatial model
sFunc = afwMath.PolynomialFunction2D(1)
# spatial parameters are a list of entries, one per kernel parameter;
# each entry is a list of spatial parameters
sParams = (
(1.0, -0.5/self.width, -0.5/self.height),
(0.0, 1.0/self.width, 0.0/self.height),
(0.0, 0.0/self.width, 1.0/self.height),
)
# create three kernels with some non-overlapping pixels
# (non-zero pixels in one kernel vs. zero pixels in other kernels);
# note: the extreme example of this is delta function kernels, but this
# is less extreme
basisKernelList = []
kImArr = numpy.zeros([5, 5], dtype=float)
kImArr[1:4, 1:4] = 0.5
kImArr[2, 2] = 1.0
kImage = afwImage.makeImageFromArray(kImArr)
basisKernelList.append(afwMath.FixedKernel(kImage))
kImArr[:, :] = 0.0
kImArr[0:2, 0:2] = 0.125
kImArr[3:5, 3:5] = 0.125
kImage = afwImage.makeImageFromArray(kImArr)
basisKernelList.append(afwMath.FixedKernel(kImage))
kImArr[:, :] = 0.0
kImArr[0:2, 3:5] = 0.125
kImArr[3:5, 0:2] = 0.125
kImage = afwImage.makeImageFromArray(kImArr)
basisKernelList.append(afwMath.FixedKernel(kImage))
kernel = afwMath.LinearCombinationKernel(basisKernelList, sFunc)
kernel.setSpatialParameters(sParams)
for maxInterpDist, rtol, methodStr in (
(0, 1.0e-5, "brute force"),
(10, 3.0e-3, "interpolation over 10 x 10 pixels"),
):
self.runStdTest(
kernel,
kernelDescr="Spatially varying LinearCombinationKernel of basis "
"kernels with low covariance, using %s" % (
methodStr,),
maxInterpDist=maxInterpDist,
rtol=rtol)
def checkEdge(self, footprint):
"""Check that Footprint::findEdgePixels() works"""
bbox = footprint.getBBox()
bbox.grow(3)
def makeImage(area):
"""Make an ImageF with 1 in the footprint, and 0 elsewhere"""
ones = afwImage.ImageI(bbox)
ones.set(1)
image = afwImage.ImageI(bbox)
image.set(0)
if isinstance(area, afwDetect.Footprint):
area.spans.copyImage(ones, image)
if isinstance(area, afwGeom.SpanSet):
area.copyImage(ones, image)
return image
edges = self.foot.spans.findEdgePixels()
edgeImage = makeImage(edges)
# Find edges with an edge-detection kernel
image = makeImage(self.foot)
kernel = afwImage.ImageD(3, 3)
kernel[1, 1, afwImage.LOCAL] = 4
for x, y in [(1, 2), (0, 1), (1, 0), (2, 1)]:
kernel[x, y, afwImage.LOCAL] = -1
kernel.setXY0(1, 1)
result = afwImage.ImageI(bbox)
result.set(0)
afwMath.convolve(result, image, afwMath.FixedKernel(kernel),
afwMath.ConvolutionControl(False))
result.getArray().__imul__(image.getArray())
trueEdges = np.where(result.getArray() > 0, 1, 0)
self.assertTrue(np.all(trueEdges == edgeImage.getArray()))
def _computeVaryingPsf(self):
"""Compute a varying PSF as a linear combination of PCA (== Karhunen-Loeve) basis functions
We simply desire a PSF that is not constant across the image, so the precise choice of
parameters (e.g., sigmas, setSpatialParameters) are not crucial.
"""
kernelSize = 31
sigma1 = 1.75
sigma2 = 2.0 * sigma1
basisKernelList = []
for sigma in (sigma1, sigma2):
basisKernel = afwMath.AnalyticKernel(
kernelSize, kernelSize,
afwMath.GaussianFunction2D(sigma, sigma))
basisImage = afwImage.ImageD(basisKernel.getDimensions())
basisKernel.computeImage(basisImage, True)
basisImage /= np.sum(basisImage.getArray())
if sigma == sigma1:
basisImage0 = basisImage
else:
basisImage -= basisImage0
basisKernelList.append(afwMath.FixedKernel(basisImage))
order = 1
spFunc = afwMath.PolynomialFunction2D(order)
exactKernel = afwMath.LinearCombinationKernel(basisKernelList, spFunc)
exactKernel.setSpatialParameters([[1.0, 0, 0], [0.0, 0.5E-2, 0.2E-2]])
exactPsf = measAlg.PcaPsf(exactKernel)
return exactPsf
def makeExposure(imgArray, psfArray, imgVariance):
"""! Convert an image numpy.array and corresponding PSF numpy.array into an exposure.
Add the (constant) variance plane equal to `imgVariance`.
@param imgArray 2-d numpy.array containing the image
@param psfArray 2-d numpy.array containing the PSF image
@param imgVariance variance of input image
@return a new exposure containing the image, PSF and desired variance plane
"""
# All this code to convert the template image array/psf array into an exposure.
bbox = geom.Box2I(
geom.Point2I(0, 0),
geom.Point2I(imgArray.shape[1] - 1, imgArray.shape[0] - 1))
im1ex = afwImage.ExposureD(bbox)
im1ex.getMaskedImage().getImage().getArray()[:, :] = imgArray
im1ex.getMaskedImage().getVariance().getArray()[:, :] = imgVariance
psfBox = geom.Box2I(geom.Point2I(-12, -12),
geom.Point2I(12, 12)) # a 25x25 pixel psf
psf = afwImage.ImageD(psfBox)
psfBox.shift(geom.Extent2I(size[0] // 2, size[1] // 2))
im1_psf_sub = psfArray[psfBox.getMinX():psfBox.getMaxX() + 1,
psfBox.getMinY():psfBox.getMaxY() + 1]
psf.getArray()[:, :] = im1_psf_sub
psfK = afwMath.FixedKernel(psf)
psfNew = measAlg.KernelPsf(psfK)
im1ex.setPsf(psfNew)
wcs = makeWcs()
im1ex.setWcs(wcs)
return im1ex
def makeExposure(imgArray, psfArray, imgVariance):
"""Convert an image and corresponding PSF into an exposure.
Set the (constant) variance plane equal to ``imgVariance``.
Parameters
----------
imgArray : `numpy.ndarray`
2D array containing the image.
psfArray : `numpy.ndarray`
2D array containing the PSF image.
imgVariance : `float` or `numpy.ndarray`
Set the variance plane to this value. If an array, must be broadcastable to ``imgArray.shape``.
Returns
-------
im1ex : `lsst.afw.image.Exposure`
The new exposure.
"""
# All this code to convert the template image array/psf array into an exposure.
bbox = geom.Box2I(geom.Point2I(0, 0), geom.Point2I(imgArray.shape[1] - 1, imgArray.shape[0] - 1))
im1ex = afwImage.ExposureD(bbox)
im1ex.image.array[:, :] = imgArray
im1ex.variance.array[:, :] = imgVariance
psfBox = geom.Box2I(geom.Point2I(-12, -12), geom.Point2I(12, 12)) # a 25x25 pixel psf
psf = afwImage.ImageD(psfBox)
psfBox.shift(geom.Extent2I(-(-size[0]//2), -(-size[1]//2))) # -N//2 != -(N//2) for odd numbers
im1_psf_sub = psfArray[psfBox.getMinY():psfBox.getMaxY() + 1, psfBox.getMinX():psfBox.getMaxX() + 1]
psf.getArray()[:, :] = im1_psf_sub
psfK = afwMath.FixedKernel(psf)
psfNew = measAlg.KernelPsf(psfK)
im1ex.setPsf(psfNew)
wcs = makeWcs()
im1ex.setWcs(wcs)
return im1ex
def _doConvolve(exposure, kernel):
"""Convolve an Exposure with a decorrelation convolution kernel.
Parameters
----------
exposure : `lsst.afw.image.Exposure`
Input exposure to be convolved.
kernel : `numpy.array`
Input 2-d numpy.array to convolve the image with
Returns
-------
out : `lsst.afw.image.Exposure`
a new Exposure with the convolved pixels and the (possibly
re-centered) kernel.
Notes
-----
We re-center the kernel if necessary and return the possibly re-centered kernel
"""
kernelImg = afwImage.ImageD(kernel.shape[0], kernel.shape[1])
kernelImg.getArray()[:, :] = kernel
kern = afwMath.FixedKernel(kernelImg)
maxloc = np.unravel_index(np.argmax(kernel), kernel.shape)
kern.setCtrX(maxloc[0])
kern.setCtrY(maxloc[1])
outExp = exposure.clone() # Do this to keep WCS, PSF, masks, etc.
convCntrl = afwMath.ConvolutionControl(False, True, 0)
afwMath.convolve(outExp.getMaskedImage(), exposure.getMaskedImage(), kern, convCntrl)
return outExp, kern
def _makeAndTestUncorrectedDiffim(self):
"""Create the (un-decorrelated) diffim, and verify that its variance is too low.
"""
# Create the matching kernel. We used Gaussian PSFs for im1 and im2, so we can compute the "expected"
# matching kernel sigma.
psf1_sig = self.im1ex.getPsf().computeShape().getDeterminantRadius()
psf2_sig = self.im2ex.getPsf().computeShape().getDeterminantRadius()
sig_match = np.sqrt((psf1_sig**2. - psf2_sig**2.))
# Sanity check - make sure PSFs are correct.
self.assertFloatsAlmostEqual(sig_match,
np.sqrt((self.psf1_sigma**2. -
self.psf2_sigma**2.)),
rtol=2e-5)
# mKernel = measAlg.SingleGaussianPsf(31, 31, sig_match)
x0 = np.arange(-16, 16, 1)
y0 = x0.copy()
x0im, y0im = np.meshgrid(x0, y0)
matchingKernel = singleGaussian2d(x0im,
y0im,
-1.,
-1.,
sigma_x=sig_match,
sigma_y=sig_match)
kernelImg = afwImage.ImageD(matchingKernel.shape[0],
matchingKernel.shape[1])
kernelImg.getArray()[:, :] = matchingKernel
mKernel = afwMath.FixedKernel(kernelImg)
# Create the matched template by convolving the template with the matchingKernel
matched_im2ex = self.im2ex.clone()
convCntrl = afwMath.ConvolutionControl(False, True, 0)
afwMath.convolve(matched_im2ex.getMaskedImage(),
self.im2ex.getMaskedImage(), mKernel, convCntrl)
# Expected (ideal) variance of difference image
expected_var = self.svar + self.tvar
if verbose:
print('EXPECTED VARIANCE:', expected_var)
# Create the diffim (uncorrected)
# Uncorrected diffim exposure - variance plane is wrong (too low)
tmp_diffExp = self.im1ex.getMaskedImage().clone()
tmp_diffExp -= matched_im2ex.getMaskedImage()
var = self._computeVarianceMean(tmp_diffExp)
self.assertLess(var, expected_var)
# Uncorrected diffim exposure - variance is wrong (too low) - same as above but on pixels
diffExp = self.im1ex.clone()
tmp = diffExp.getMaskedImage()
tmp -= matched_im2ex.getMaskedImage()
var = self._computePixelVariance(diffExp.getMaskedImage())
self.assertLess(var, expected_var)
# Uncorrected diffim exposure - variance plane is wrong (too low)
mn = self._computeVarianceMean(diffExp.getMaskedImage())
self.assertLess(mn, expected_var)
if verbose:
print('UNCORRECTED VARIANCE:', var, mn)
return diffExp, mKernel, expected_var
def checkEdge(self, footprint):
"""Check that Footprint::findEdgePixels() works"""
bbox = footprint.getBBox()
bbox.grow(3)
def makeImage(footprint):
"""Make an ImageF with 1 in the footprint, and 0 elsewhere"""
ones = afwImage.ImageI(bbox)
ones.set(1)
image = afwImage.ImageI(bbox)
image.set(0)
afwDetect.copyWithinFootprintImage(footprint, ones, image)
return image
edges = self.foot.findEdgePixels()
edgeImage = makeImage(edges)
# Find edges with an edge-detection kernel
image = makeImage(self.foot)
kernel = afwImage.ImageD(3, 3)
kernel.set(1, 1, 4)
for x, y in [(1, 2), (0, 1), (1, 0), (2, 1)]:
kernel.set(x, y, -1)
kernel.setXY0(1, 1)
result = afwImage.ImageI(bbox)
result.set(0)
afwMath.convolve(result, image, afwMath.FixedKernel(kernel),
afwMath.ConvolutionControl(False))
result.getArray().__imul__(image.getArray())
trueEdges = numpy.where(result.getArray() > 0, 1, 0)
self.assertTrue(numpy.all(trueEdges == edgeImage.getArray()))
def performALZCExposureCorrection(templateExposure, exposure,
subtractedExposure, psfMatchingKernel, log):
kimg = alPsfMatchingKernelToArray(psfMatchingKernel, subtractedExposure)
# Compute the images' sigmas (sqrt of variance)
sig1 = templateExposure.getMaskedImage().getVariance().getArray()
sig2 = exposure.getMaskedImage().getVariance().getArray()
sig1squared, _, _, _ = computeClippedImageStats(sig1)
sig2squared, _, _, _ = computeClippedImageStats(sig2)
corrKernel = computeDecorrelationKernel(kimg, sig1squared, sig2squared)
# Eventually, use afwMath.convolve(), but for now just use scipy.
log.info("ALZC: Convolving.")
pci, _ = doConvolve(
subtractedExposure.getMaskedImage().getImage().getArray(), corrKernel)
subtractedExposure.getMaskedImage().getImage().getArray()[:, :] = pci
log.info("ALZC: Finished with convolution.")
# Compute the subtracted exposure's updated psf
psf = afwPsfToArray(subtractedExposure.getPsf(),
subtractedExposure) # .computeImage().getArray()
psfc = computeCorrectedDiffimPsf(corrKernel,
psf,
svar=sig1squared,
tvar=sig2squared)
psfcI = afwImage.ImageD(
subtractedExposure.getPsf().computeKernelImage().getBBox())
psfcI.getArray()[:, :] = psfc
psfcK = afwMath.FixedKernel(psfcI)
psfNew = measAlg.KernelPsf(psfcK)
subtractedExposure.setPsf(psfNew)
return subtractedExposure, corrKernel
def makeHSCExposure(self, galData, psfData, pixScale, variance):
ny, nx = galData.shape
exposure = afwImg.ExposureF(nx, ny)
exposure.getMaskedImage().getImage().getArray()[:, :] = galData
exposure.getMaskedImage().getVariance().getArray()[:, :] = variance
#Set the PSF
ngridPsf = psfData.shape[0]
psfLsst = afwImg.ImageF(ngridPsf, ngridPsf)
psfLsst.getArray()[:, :] = psfData
psfLsst = psfLsst.convertD()
kernel = afwMath.FixedKernel(psfLsst)
kernelPSF = meaAlg.KernelPsf(kernel)
exposure.setPsf(kernelPSF)
#prepare the wcs
#Rotation
cdelt = (pixScale * afwGeom.arcseconds)
CD = afwGeom.makeCdMatrix(cdelt, afwGeom.Angle(0.)) #no rotation
#wcs
crval = afwGeom.SpherePoint(afwGeom.Angle(0., afwGeom.degrees),
afwGeom.Angle(0., afwGeom.degrees))
#crval = afwCoord.IcrsCoord(0.*afwGeom.degrees, 0.*afwGeom.degrees) # hscpipe6
crpix = afwGeom.Point2D(0.0, 0.0)
dataWcs = afwGeom.makeSkyWcs(crpix, crval, CD)
exposure.setWcs(dataWcs)
#prepare the frc
dataCalib = afwImg.makePhotoCalibFromCalibZeroPoint(63095734448.0194)
exposure.setPhotoCalib(dataCalib)
return exposure
def testSVLinearCombinationKernel(self):
"""Test a spatially varying LinearCombinationKernel
"""
kWidth = 3
kHeight = 2
# create image arrays for the basis kernels
basisImArrList = []
imArr = np.zeros((kWidth, kHeight), dtype=float)
imArr += 0.1
imArr[kWidth // 2, :] = 0.9
basisImArrList.append(imArr)
imArr = np.zeros((kWidth, kHeight), dtype=float)
imArr += 0.2
imArr[:, kHeight // 2] = 0.8
basisImArrList.append(imArr)
# create a list of basis kernels from the images
kVec = []
for basisImArr in basisImArrList:
basisImage = afwImage.makeImageFromArray(
basisImArr.transpose().copy())
kernel = afwMath.FixedKernel(basisImage)
kVec.append(kernel)
# create spatially varying linear combination kernel
spFunc = afwMath.PolynomialFunction2D(1)
# spatial parameters are a list of entries, one per kernel parameter;
# each entry is a list of spatial parameters
sParams = (
(0.0, 1.0, 0.0),
(0.0, 0.0, 1.0),
)
k = afwMath.LinearCombinationKernel(kVec, spFunc)
k.setSpatialParameters(sParams)
with lsst.utils.tests.getTempFilePath(".fits") as filename:
k.writeFits(filename)
k2 = afwMath.LinearCombinationKernel.readFits(filename)
self.kernelCheck(k, k2)
kImage = afwImage.ImageD(lsst.geom.Extent2I(kWidth, kHeight))
for colPos, rowPos, coeff0, coeff1 in [
(0.0, 0.0, 0.0, 0.0),
(1.0, 0.0, 1.0, 0.0),
(0.0, 1.0, 0.0, 1.0),
(1.0, 1.0, 1.0, 1.0),
(0.5, 0.5, 0.5, 0.5),
]:
k2.computeImage(kImage, False, colPos, rowPos)
kImArr = kImage.getArray().transpose()
refKImArr = (basisImArrList[0] * coeff0) + \
(basisImArrList[1] * coeff1)
if not np.allclose(kImArr, refKImArr):
self.fail(f"{k2.__class__.__name__} = {kImArr} != {refKImArr} "
f"at colPos={colPos}, rowPos={rowPos}")
def arrayToAfwKernel(array):
kernelImg = afwImage.ImageD(array.shape[1], array.shape[0])
kernelImg.getArray()[:, :] = array
kern = afwMath.FixedKernel(kernelImg)
maxloc = np.unravel_index(np.argmax(array), array.shape)
kern.setCtrX(maxloc[0])
kern.setCtrY(maxloc[1])
return kern
def doALdecorrelation(alTaskResult,
sig1squared=None,
sig2squared=None,
preConvKernel=None):
kimg = alPsfMatchingKernelToArray(alTaskResult.psfMatchingKernel,
alTaskResult.subtractedExposure)
if preConvKernel is not None and kimg.shape[0] < preConvKernel.shape[0]:
# This is likely brittle and may only work if both kernels are odd-shaped.
#kimg[np.abs(kimg) < 1e-4] = np.sign(kimg)[np.abs(kimg) < 1e-4] * 1e-8
#kimg -= kimg[0, 0]
padSize0 = preConvKernel.shape[0] // 2 - kimg.shape[0] // 2
padSize1 = preConvKernel.shape[1] // 2 - kimg.shape[1] // 2
kimg = np.pad(kimg, ((padSize0, padSize0), (padSize1, padSize1)),
mode='constant',
constant_values=0)
#kimg /= kimg.sum()
#preConvKernel = preConvKernel[padSize0:-padSize0, padSize1:-padSize1]
if sig1squared is None:
sig1squared = computeVarianceMean(im1)
if sig2squared is None:
sig2squared = computeVarianceMean(im2)
pck = computeDecorrelationKernel(kimg,
sig1squared,
sig2squared,
preConvKernel=preConvKernel,
delta=0.)
#return kimg, preConvKernel, pck
diffim, _ = doConvolve(alTaskResult.subtractedExposure,
pck,
use_scipy=False)
# For some reason, border areas of img and variance planes can become infinite. Fix it.
img = diffim.getMaskedImage().getImage().getArray()
img[~np.isfinite(img)] = np.nan
img = diffim.getMaskedImage().getVariance().getArray()
img[~np.isfinite(img)] = np.nan
# TBD: also need to update the mask as it is not (apparently) set correctly.
psf = afwPsfToArray(alTaskResult.subtractedExposure.getPsf(),
img=alTaskResult.subtractedExposure)
# NOTE! Need to compute the updated PSF including preConvKernel !!! This doesn't do it:
psfc = computeCorrectedDiffimPsf(kimg,
psf,
tvar=sig1squared,
svar=sig2squared)
psfcI = afwImage.ImageD(psfc.shape[0], psfc.shape[1])
psfcI.getArray()[:, :] = psfc
psfcK = afwMath.FixedKernel(psfcI)
psfNew = measAlg.KernelPsf(psfcK)
diffim.setPsf(psfNew)
alTaskResult.decorrelatedDiffim = diffim
alTaskResult.preConvKernel = preConvKernel
alTaskResult.decorrelationKernel = pck
alTaskResult.kappaImg = kimg
return alTaskResult
def testSVAnalyticKernel(self):
"""Test spatially varying AnalyticKernel using a Gaussian function
Just tests cloning.
"""
kWidth = 5
kHeight = 8
# spatial model
spFunc = afwMath.PolynomialFunction2D(1)
# spatial parameters are a list of entries, one per kernel parameter;
# each entry is a list of spatial parameters
sParams = (
(1.0, 1.0, 0.0),
(1.0, 0.0, 1.0),
(0.5, 0.5, 0.5),
)
gaussFunc = afwMath.GaussianFunction2D(1.0, 1.0, 0.0)
kernel = afwMath.AnalyticKernel(kWidth, kHeight, gaussFunc, spFunc)
kernel.setSpatialParameters(sParams)
kernelClone = kernel.clone()
errStr = self.compareKernels(kernel, kernelClone)
if errStr:
self.fail(errStr)
newSParams = (
(0.1, 0.2, 0.5),
(0.1, 0.5, 0.2),
(0.2, 0.3, 0.3),
)
kernel.setSpatialParameters(newSParams)
errStr = self.compareKernels(kernel, kernelClone)
if not errStr:
self.fail(
"Clone was modified by changing original's spatial parameters")
#
# check that we can construct a FixedKernel from a LinearCombinationKernel
#
x, y = 100, 200
kernel2 = afwMath.FixedKernel(kernel, lsst.geom.PointD(x, y))
self.assertTrue(re.search("AnalyticKernel", kernel.toString()))
self.assertFalse(kernel2.isSpatiallyVarying())
self.assertTrue(re.search("FixedKernel", kernel2.toString()))
self.assertTrue(kernel.isSpatiallyVarying())
kim = afwImage.ImageD(kernel.getDimensions())
kernel.computeImage(kim, True, x, y)
kim2 = afwImage.ImageD(kernel2.getDimensions())
kernel2.computeImage(kim2, True)
assert_allclose(kim.getArray(), kim2.getArray())
def doConvolve(exposure, kernel, use_scipy=False):
"""! Convolve an Exposure with a decorrelation convolution kernel.
@param exposure Input afw.image.Exposure to be convolved.
@param kernel Input 2-d numpy.array to convolve the image with
@param use_scipy Use scipy to do convolution instead of afwMath
@return a new Exposure with the convolved pixels and the (possibly
re-centered) kernel.
@note We use afwMath.convolve() but keep scipy.convolve for debugging.
@note We re-center the kernel if necessary and return the possibly re-centered kernel
"""
def _fixEvenKernel(kernel):
"""! Take a kernel with even dimensions and make them odd, centered correctly.
@param kernel a numpy.array
@return a fixed kernel numpy.array
"""
# Make sure the peak (close to a delta-function) is in the center!
maxloc = np.unravel_index(np.argmax(kernel), kernel.shape)
out = np.roll(kernel, kernel.shape[0] // 2 - maxloc[0], axis=0)
out = np.roll(out, out.shape[1] // 2 - maxloc[1], axis=1)
# Make sure it is odd-dimensioned by trimming it.
if (out.shape[0] % 2) == 0:
maxloc = np.unravel_index(np.argmax(out), out.shape)
if out.shape[0] - maxloc[0] > maxloc[0]:
out = out[:-1, :]
else:
out = out[1:, :]
if out.shape[1] - maxloc[1] > maxloc[1]:
out = out[:, :-1]
else:
out = out[:, 1:]
return out
outExp = kern = None
fkernel = _fixEvenKernel(kernel)
if use_scipy:
from scipy.ndimage.filters import convolve
pci = convolve(exposure.getMaskedImage().getImage().getArray(),
fkernel,
mode='constant',
cval=np.nan)
outExp = exposure.clone()
outExp.getMaskedImage().getImage().getArray()[:, :] = pci
kern = fkernel
else:
kernelImg = afwImage.ImageD(fkernel.shape[0], fkernel.shape[1])
kernelImg.getArray()[:, :] = fkernel
kern = afwMath.FixedKernel(kernelImg)
maxloc = np.unravel_index(np.argmax(fkernel), fkernel.shape)
kern.setCtrX(maxloc[0])
kern.setCtrY(maxloc[1])
outExp = exposure.clone() # Do this to keep WCS, PSF, masks, etc.
convCntrl = afwMath.ConvolutionControl(False, True, 0)
afwMath.convolve(outExp.getMaskedImage(), exposure.getMaskedImage(),
kern, convCntrl)
return outExp, kern
def assess(self, cand, kFn1, bgFn1, kFn2, bgFn2, frame0):
tmi = cand.getTemplateMaskedImage()
smi = cand.getScienceMaskedImage()
im1 = afwImage.ImageD(kFn1.getDimensions())
kFn1.computeImage(im1, False,
afwImage.indexToPosition(int(cand.getXCenter())),
afwImage.indexToPosition(int(cand.getYCenter())))
fk1 = afwMath.FixedKernel(im1)
bg1 = bgFn1(afwImage.indexToPosition(int(cand.getXCenter())),
afwImage.indexToPosition(int(cand.getYCenter())))
d1 = ipDiffim.convolveAndSubtract(tmi, smi, fk1, bg1)
####
im2 = afwImage.ImageD(kFn2.getDimensions())
kFn2.computeImage(im2, False,
afwImage.indexToPosition(int(cand.getXCenter())),
afwImage.indexToPosition(int(cand.getYCenter())))
fk2 = afwMath.FixedKernel(im2)
bg2 = bgFn2(afwImage.indexToPosition(int(cand.getXCenter())),
afwImage.indexToPosition(int(cand.getYCenter())))
d2 = ipDiffim.convolveAndSubtract(tmi, smi, fk2, bg2)
if display:
disp = afwDisplay.Display(frame=frame0)
disp.mtv(tmi, title="Template Masked Image")
disp.dot("Cand %d" % (cand.getId()), 0, 0)
afwDisplay.Display(frame=frame0 + 1).mtv(
smi, title="Science Masked Image")
afwDisplay.Display(frame=frame0 + 2).mtv(im1,
title="Masked Image: 1")
afwDisplay.Display(frame=frame0 + 3).mtv(
d1, title="Difference Image: 1")
afwDisplay.Display(frame=frame0 + 4).mtv(im2,
title="Masked Image: 2")
afwDisplay.Display(frame=frame0 + 5).mtv(
d2, title="Difference Image: 2")
logger.debug("Full Spatial Model")
self.stats(cand.getId(), d1)
logger.debug("N-1 Spatial Model")
self.stats(cand.getId(), d2)
def _setNewPsf(self, exposure, psfArr):
"""Utility method to set an exposure's PSF when provided as a 2-d numpy.array
"""
psfI = afwImage.ImageD(psfArr.shape[0], psfArr.shape[1])
psfI.getArray()[:, :] = psfArr
psfK = afwMath.FixedKernel(psfI)
psfNew = measAlg.KernelPsf(psfK)
exposure.setPsf(psfNew)
return exposure
def makeRatingVector(kernelCellSet, spatialKernel, spatialBg):
imstats = diffimLib.ImageStatisticsF()
#sdqaVector = sdqa.SdqaRatingSet()
width, height = spatialKernel.getDimensions()
kImage = afwImage.ImageD(width, height)
# find the kernel sum and its Rms by looking at the 4 corners of the image
kSums = afwMath.vectorD()
for x in (0, width):
for y in (0, height):
kSum = spatialKernel.computeImage(kImage, False, x, y)
kSums.push_back(kSum)
#afwStat = afwMath.makeStatistics(kSums, afwMath.MEAN | afwMath.STDEV)
#kSumRating = sdqa.SdqaRating("lsst.ip.diffim.kernel_sum",
# afwStat.getValue(afwMath.MEAN),
# afwStat.getValue(afwMath.STDEV),
# scope)
#sdqaVector.append(kSumRating)
nGood = 0
nBad = 0
for cell in kernelCellSet.getCellList():
for cand in cell.begin(False): # False = include bad candidates
cand = diffimLib.cast_KernelCandidateF(cand)
if cand.getStatus() == afwMath.SpatialCellCandidate.GOOD:
# this has been used for processing
nGood += 1
xCand = int(cand.getXCenter())
yCand = int(cand.getYCenter())
# evaluate kernel and background at position of candidate
kSum = spatialKernel.computeImage(kImage, False, xCand, yCand)
kernel = afwMath.FixedKernel(kImage)
background = spatialBg(xCand, yCand)
diffIm = cand.getDifferenceImage(kernel, background)
imstats.apply(diffIm)
#candMean = imstats.getMean()
#candRms = imstats.getRms()
#candRating = sdqa.SdqaRating("lsst.ip.diffim.residuals_%d_%d" % (xCand, yCand),
# candMean, candRms, scope)
#sdqaVector.append(candRating)
elif cand.getStatus() == afwMath.SpatialCellCandidate.BAD:
nBad += 1
#nGoodRating = sdqa.SdqaRating("lsst.ip.diffim.nCandGood", nGood, 0, scope)
#sdqaVector.append(nGoodRating)
#nBadRating = sdqa.SdqaRating("lsst.ip.diffim.nCandBad", nBad, 0, scope)
#sdqaVector.append(nBadRating)
nKernelTerms = spatialKernel.getNSpatialParameters()
if nKernelTerms == 0: # order 0
nKernelTerms = 1
def runMeasurement(self, algorithmName, imageid, x, y, v):
"""Run the measurement algorithm on an image"""
# load the test image
imgFile = os.path.join(self.dataDir, "image.%d.fits" % imageid)
img = afwImage.ImageF(imgFile)
img -= self.bkgd
nx, ny = img.getWidth(), img.getHeight()
msk = afwImage.Mask(geom.Extent2I(nx, ny), 0x0)
var = afwImage.ImageF(geom.Extent2I(nx, ny), v)
mimg = afwImage.MaskedImageF(img, msk, var)
msk.getArray()[:] = np.where(np.fabs(img.getArray()) < 1.0e-8, msk.getPlaneBitMask("BAD"), 0)
# Put it in a bigger image, in case it matters
big = afwImage.MaskedImageF(self.offset + mimg.getDimensions())
big.getImage().set(0)
big.getMask().set(0)
big.getVariance().set(v)
subBig = afwImage.MaskedImageF(big, geom.Box2I(big.getXY0() + self.offset, mimg.getDimensions()))
subBig <<= mimg
mimg = big
mimg.setXY0(self.xy0)
exposure = afwImage.makeExposure(mimg)
cdMatrix = np.array([1.0/(2.53*3600.0), 0.0, 0.0, 1.0/(2.53*3600.0)])
cdMatrix.shape = (2, 2)
exposure.setWcs(afwGeom.makeSkyWcs(crpix=geom.Point2D(1.0, 1.0),
crval=geom.SpherePoint(0, 0, geom.degrees),
cdMatrix=cdMatrix))
# load the corresponding test psf
psfFile = os.path.join(self.dataDir, "psf.%d.fits" % imageid)
psfImg = afwImage.ImageD(psfFile)
psfImg -= self.bkgd
kernel = afwMath.FixedKernel(psfImg)
kernelPsf = algorithms.KernelPsf(kernel)
exposure.setPsf(kernelPsf)
# perform the shape measurement
msConfig = base.SingleFrameMeasurementConfig()
alg = base.SingleFramePlugin.registry[algorithmName].PluginClass.AlgClass
control = base.SingleFramePlugin.registry[algorithmName].PluginClass.ConfigClass().makeControl()
msConfig.algorithms.names = [algorithmName]
# Note: It is essential to remove the floating point part of the position for the
# Algorithm._apply. Otherwise, when the PSF is realised it will have been warped
# to account for the sub-pixel offset and we won't get *exactly* this PSF.
plugin, table = makePluginAndCat(alg, algorithmName, control, centroid="centroid")
center = geom.Point2D(int(x), int(y)) + geom.Extent2D(self.offset + geom.Extent2I(self.xy0))
source = table.makeRecord()
source.set("centroid_x", center.getX())
source.set("centroid_y", center.getY())
source.setFootprint(afwDetection.Footprint(afwGeom.SpanSet(exposure.getBBox(afwImage.PARENT))))
plugin.measure(source, exposure)
return source
def assess(self, cand, kFn1, bgFn1, kFn2, bgFn2, frame0):
tmi = cand.getTemplateMaskedImage()
smi = cand.getScienceMaskedImage()
im1 = afwImage.ImageD(kFn1.getDimensions())
kFn1.computeImage(im1, False,
afwImage.indexToPosition(int(cand.getXCenter())),
afwImage.indexToPosition(int(cand.getYCenter())))
fk1 = afwMath.FixedKernel(im1)
bg1 = bgFn1(afwImage.indexToPosition(int(cand.getXCenter())),
afwImage.indexToPosition(int(cand.getYCenter())))
d1 = ipDiffim.convolveAndSubtract(tmi, smi, fk1, bg1)
####
im2 = afwImage.ImageD(kFn2.getDimensions())
kFn2.computeImage(im2, False,
afwImage.indexToPosition(int(cand.getXCenter())),
afwImage.indexToPosition(int(cand.getYCenter())))
fk2 = afwMath.FixedKernel(im2)
bg2 = bgFn2(afwImage.indexToPosition(int(cand.getXCenter())),
afwImage.indexToPosition(int(cand.getYCenter())))
d2 = ipDiffim.convolveAndSubtract(tmi, smi, fk2, bg2)
if display:
ds9.mtv(tmi, frame=frame0+0)
ds9.dot("Cand %d" % (cand.getId()), 0, 0, frame=frame0+0)
ds9.mtv(smi, frame=frame0+1)
ds9.mtv(im1, frame=frame0+2)
ds9.mtv(d1, frame=frame0+3)
ds9.mtv(im2, frame=frame0+4)
ds9.mtv(d2, frame=frame0+5)
pexLog.Trace("lsst.ip.diffim.JackknifeResampleKernel", 1,
"Full Spatial Model")
self.stats(cand.getId(), d1)
pexLog.Trace("lsst.ip.diffim.JackknifeResampleKernel", 1,
"N-1 Spatial Model")
self.stats(cand.getId(), d2)
def testFixedKernelConvolve(self):
"""Test convolve with a fixed kernel
"""
kWidth = 6
kHeight = 7
kFunc = afwMath.GaussianFunction2D(2.5, 1.5, 0.5)
analyticKernel = afwMath.AnalyticKernel(kWidth, kHeight, kFunc)
kernelImage = afwImage.ImageD(afwGeom.Extent2I(kWidth, kHeight))
analyticKernel.computeImage(kernelImage, False)
fixedKernel = afwMath.FixedKernel(kernelImage)
self.runStdTest(fixedKernel, kernelDescr="Gaussian FixedKernel")
def testFixedKernel(self):
"""Test FixedKernel using a ramp function
"""
kWidth = 5
kHeight = 6
inArr = np.arange(kWidth * kHeight, dtype=float)
inArr.shape = [kWidth, kHeight]
inImage = afwImage.ImageD(afwGeom.Extent2I(kWidth, kHeight))
for row in range(inImage.getHeight()):
for col in range(inImage.getWidth()):
inImage.set(col, row, inArr[col, row])
k = afwMath.FixedKernel(inImage)
pol = pexPolicy.Policy()
additionalData = dafBase.PropertySet()
loc = dafPersist.LogicalLocation(
os.path.join(testPath, "data", "kernel1.boost"))
persistence = dafPersist.Persistence.getPersistence(pol)
storageList = dafPersist.StorageList()
storage = persistence.getPersistStorage("XmlStorage", loc)
storageList.append(storage)
persistence.persist(k, storageList, additionalData)
storageList2 = dafPersist.StorageList()
storage2 = persistence.getRetrieveStorage("XmlStorage", loc)
storageList2.append(storage2)
x = persistence.unsafeRetrieve("FixedKernel", storageList2,
additionalData)
k2 = afwMath.FixedKernel.swigConvert(x)
self.kernelCheck(k, k2)
outImage = afwImage.ImageD(k2.getDimensions())
k2.computeImage(outImage, False)
outArr = outImage.getArray().transpose()
if not np.allclose(inArr, outArr):
self.fail("%s = %s != %s (not normalized)" %
(k2.__class__.__name__, inArr, outArr))
normInArr = inArr / inArr.sum()
normOutImage = afwImage.ImageD(k2.getDimensions())
k2.computeImage(normOutImage, True)
normOutArr = normOutImage.getArray().transpose()
if not np.allclose(normOutArr, normInArr):
self.fail("%s = %s != %s (normalized)" %
(k2.__class__.__name__, normInArr, normOutArr))
def _growPsf(exp, extraPix=(2, 3)):
bbox = exp.getBBox()
center = ((bbox.getBeginX() + bbox.getEndX()) // 2., (bbox.getBeginY() + bbox.getEndY()) // 2.)
center = geom.Point2D(center[0], center[1])
kern = exp.getPsf().computeKernelImage(center).convertF()
kernSize = kern.getDimensions()
paddedKern = afwImage.ImageF(kernSize[0] + extraPix[0], kernSize[1] + extraPix[1])
bboxToPlace = geom.Box2I(geom.Point2I((kernSize[0] + extraPix[0] - kern.getWidth()) // 2,
(kernSize[1] + extraPix[1] - kern.getHeight()) // 2),
kern.getDimensions())
paddedKern.assign(kern, bboxToPlace)
fixedKern = afwMath.FixedKernel(paddedKern.convertD())
psfNew = measAlg.KernelPsf(fixedKern, center)
exp.setPsf(psfNew)
return exp
def makeKernelPsfFromArray(A):
"""Create a non spatially varying PSF from a `numpy.ndarray`.
Parameters
----------
A : `numpy.ndarray`
2D array to use as the new psf image. The pixels are copied.
Returns
-------
psfNew : `lsst.meas.algorithms.KernelPsf`
The constructed PSF.
"""
psfImg = afwImage.ImageD(A.astype(np.float64, copy=True), deep=False)
psfNew = measAlg.KernelPsf(afwMath.FixedKernel(psfImg))
return psfNew
def writeKernelCellSet(kernelCellSet, psfMatchingKernel, backgroundModel,
outdir):
"""TODO: DM-17458
Parameters
----------
kernelCellSet : TODO: DM-17458
TODO: DM-17458
psfMatchingKernel : TODO: DM-17458
TODO: DM-17458
backgroundModel : TODO: DM-17458
TODO: DM-17458
outdir : TODO: DM-17458
TODO: DM-17458
"""
if not os.path.isdir(outdir):
os.makedirs(outdir)
for cell in kernelCellSet.getCellList():
for cand in cell.begin(False): # False = include bad candidates
if cand.getStatus() == afwMath.SpatialCellCandidate.GOOD:
xCand = int(cand.getXCenter())
yCand = int(cand.getYCenter())
idCand = cand.getId()
diffIm = cand.getDifferenceImage(
diffimLib.KernelCandidateF.ORIG)
kernel = cand.getKernelImage(diffimLib.KernelCandidateF.ORIG)
diffIm.writeFits(
os.path.join(
outdir,
'diffim_c%d_x%d_y%d.fits' % (idCand, xCand, yCand)))
kernel.writeFits(
os.path.join(
outdir,
'kernel_c%d_x%d_y%d.fits' % (idCand, xCand, yCand)))
# Diffim from spatial model
ski = afwImage.ImageD(kernel.getDimensions())
psfMatchingKernel.computeImage(ski, False, xCand, yCand)
sk = afwMath.FixedKernel(ski)
sbg = backgroundModel(xCand, yCand)
sdmi = cand.getDifferenceImage(sk, sbg)
sdmi.writeFits(
os.path.join(
outdir,
'sdiffim_c%d_x%d_y%d.fits' % (idCand, xCand, yCand)))
def getCoaddPsf(self, exposure):
import lsst.afw.table as afwTable
import lsst.afw.image as afwImage
import lsst.afw.math as afwMath
import lsst.afw.geom as afwGeom
import lsst.meas.algorithms as measAlg
schema = afwTable.ExposureTable.makeMinimalSchema()
schema.addField("weight", type="D", doc="Coadd weight")
mycatalog = afwTable.ExposureCatalog(schema)
wcsref = exposure.getWcs()
extentX = int(exposure.getWidth() * 0.05)
extentY = int(exposure.getHeight() * 0.05)
ind = 0
for x in np.linspace(extentX, exposure.getWidth() - extentX, 10):
for y in np.linspace(extentY, exposure.getHeight() - extentY, 10):
x = int(x)
y = int(y)
image = self.getImage(x, y)
psf = afwImage.ImageD(image.shape[0], image.shape[1])
psf.getArray()[:, :] = image
psfK = afwMath.FixedKernel(psf)
psf = measAlg.KernelPsf(psfK)
record = mycatalog.getTable().makeRecord()
record.setPsf(psf)
record.setWcs(wcsref)
bbox = afwGeom.Box2I(
afwGeom.Point2I(
int(np.floor(x - extentX)) - 5,
int(np.floor(y - extentY)) - 5),
afwGeom.Point2I(
int(np.floor(x + extentX)) + 5,
int(np.floor(y + extentY)) + 5))
record.setBBox(bbox)
record['weight'] = 1.0
record['id'] = ind
ind += 1
mycatalog.append(record)
# create the coaddpsf
psf = measAlg.CoaddPsf(mycatalog, wcsref, 'weight')
return psf
def testZeroWidthKernel(self):
"""Convolution by a 0x0 kernel should raise an exception.
The only way to produce a 0x0 kernel is to use the default constructor
(which exists only to support persistence; it does not produce a useful kernel).
"""
kernelList = [
afwMath.FixedKernel(),
afwMath.AnalyticKernel(),
afwMath.SeparableKernel(),
# afwMath.DeltaFunctionKernel(), # DeltaFunctionKernel has no default constructor
afwMath.LinearCombinationKernel(),
]
convolutionControl = afwMath.ConvolutionControl()
for kernel in kernelList:
self.assertRaises(Exception, afwMath.convolve, self.cnvMaskedImage,
self.maskedImage, kernel, convolutionControl)
def makeLsstExposure(galData, psfData, pixScale, variance):
"""
make an LSST exposure object
Parameters:
galData (ndarray): array of galaxy image
psfData (ndarray): array of PSF image
pixScale (float): pixel scale
variance (float): noise variance
Returns:
exposure: LSST exposure object
"""
if not with_lsst:
raise ImportError('Do not have lsstpipe!')
ny, nx = galData.shape
exposure = afwImg.ExposureF(nx, ny)
exposure.getMaskedImage().getImage().getArray()[:, :] = galData
exposure.getMaskedImage().getVariance().getArray()[:, :] = variance
#Set the PSF
ngridPsf = psfData.shape[0]
psfLsst = afwImg.ImageF(ngridPsf, ngridPsf)
psfLsst.getArray()[:, :] = psfData
psfLsst = psfLsst.convertD()
kernel = afwMath.FixedKernel(psfLsst)
kernelPSF = meaAlg.KernelPsf(kernel)
exposure.setPsf(kernelPSF)
#prepare the wcs
#Rotation
cdelt = (pixScale * afwGeom.arcseconds)
CD = afwGeom.makeCdMatrix(cdelt, afwGeom.Angle(0.)) #no rotation
#wcs
crval = afwGeom.SpherePoint(afwGeom.Angle(0., afwGeom.degrees),
afwGeom.Angle(0., afwGeom.degrees))
#crval = afwCoord.IcrsCoord(0.*afwGeom.degrees, 0.*afwGeom.degrees) # hscpipe6
crpix = afwGeom.Point2D(0.0, 0.0)
dataWcs = afwGeom.makeSkyWcs(crpix, crval, CD)
exposure.setWcs(dataWcs)
#prepare the frc
dataCalib = afwImg.makePhotoCalibFromCalibZeroPoint(63095734448.0194)
exposure.setPhotoCalib(dataCalib)
return exposure
def testFixedKernel(self):
"""Test FixedKernel using a ramp function
"""
kWidth = 5
kHeight = 6
inArr = np.arange(kWidth * kHeight, dtype=float)
inArr.shape = [kWidth, kHeight]
inImage = afwImage.ImageD(afwGeom.Extent2I(kWidth, kHeight))
for row in range(inImage.getHeight()):
for col in range(inImage.getWidth()):
inImage.set(col, row, inArr[col, row])
kernel = afwMath.FixedKernel(inImage)
self.basicTests(kernel, 0)
kernelResized = self.verifyResized(kernel, oddPadRaises=True)
self.basicTests(kernelResized, 0)
outImage = afwImage.ImageD(kernel.getDimensions())
kernel.computeImage(outImage, False)
outArr = outImage.getArray().transpose()
if not np.allclose(inArr, outArr):
self.fail("%s = %s != %s (not normalized)" %
(kernel.__class__.__name__, inArr, outArr))
normInArr = inArr / inArr.sum()
normOutImage = afwImage.ImageD(kernel.getDimensions())
kernel.computeImage(normOutImage, True)
normOutArr = normOutImage.getArray().transpose()
if not np.allclose(normOutArr, normInArr):
self.fail("%s = %s != %s (normalized)" %
(kernel.__class__.__name__, normInArr, normOutArr))
errStr = self.compareKernels(kernel, kernel.clone())
if errStr:
self.fail(errStr)
self.verifyCache(kernel, hasCache=False)
