def makeFakeKernelSet(sizeCell=128,
nCell=3,
deltaFunctionCounts=1.e4,
tGaussianWidth=1.0,
addNoise=True,
bgValue=100.,
display=False):
"""Generate test template and science images with sources.
Parameters
----------
sizeCell : `int`, optional
Size of the square spatial cells in pixels.
nCell : `int`, optional
Number of adjacent spatial cells in both direction in both images.
deltaFunctionCounts : `float`, optional
Flux value for the template image sources.
tGaussianWidth : `float`, optional
Sigma of the generated Gaussian PSF sources in the template image.
addNoise : `bool`, optional
If `True`, Poisson noise is added to both the generated template
and science images.
bgValue : `float`, optional
Background level to be added to the generated science image.
display : `bool`, optional
If `True` displays the generated template and science images by
`lsst.afw.display.Display`.
Notes
-----
- The generated images consist of adjacent ``nCell x nCell`` cells, each
of pixel size ``sizeCell x sizeCell``.
- The sources in the science image are generated by convolving the
template by ``sKernel``. ``sKernel`` is a spatial `LinearCombinationKernel`
of hard wired kernel bases functions. The linear combination has first
order polynomial spatial dependence with polynomial parameters from ``fakeCoeffs()``.
- The template image sources are generated in the center of each spatial
cell from one pixel, set to `deltaFunctionCounts` counts, then convolved
by a 2D Gaussian with sigma of `tGaussianWidth` along each axis.
- The sources are also returned in ``kernelCellSet`` each source is "detected"
exactly at the center of a cell.
Returns
-------
tMi : `lsst.afw.image.MaskedImage`
Generated template image.
sMi : `lsst.afw.image.MaskedImage`
Generated science image.
sKernel : `lsst.afw.math.LinearCombinationKernel`
The spatial kernel used to generate the sources in the science image.
kernelCellSet : `lsst.afw.math.SpatialCellSet`
Cell grid of `lsst.afw.math.SpatialCell` instances, containing
`lsst.ip.diffim.KernelCandidate` instances around all the generated sources
in the science image.
configFake : `lsst.ip.diffim.ImagePsfMatchConfig`
Config instance used in the image generation.
"""
from . import imagePsfMatch
configFake = imagePsfMatch.ImagePsfMatchConfig()
configFake.kernel.name = "AL"
subconfigFake = configFake.kernel.active
subconfigFake.alardNGauss = 1
subconfigFake.alardSigGauss = [
2.5,
]
subconfigFake.alardDegGauss = [
2,
]
subconfigFake.sizeCellX = sizeCell
subconfigFake.sizeCellY = sizeCell
subconfigFake.spatialKernelOrder = 1
subconfigFake.spatialModelType = "polynomial"
subconfigFake.singleKernelClipping = False # variance is a hack
subconfigFake.spatialKernelClipping = False # variance is a hack
if bgValue > 0.0:
subconfigFake.fitForBackground = True
policyFake = pexConfig.makePolicy(subconfigFake)
basisList = makeKernelBasisList(subconfigFake)
kSize = subconfigFake.kernelSize
# This sets the final extent of each convolved delta function
gaussKernelWidth = sizeCell // 2
# This sets the scale over which pixels are correlated in the
# spatial convolution; should be at least as big as the kernel you
# are trying to fit for
spatialKernelWidth = kSize
# Number of bad pixels due to convolutions
border = (gaussKernelWidth + spatialKernelWidth) // 2
# Make a fake image with a matrix of delta functions
totalSize = nCell * sizeCell + 2 * border
tim = afwImage.ImageF(afwGeom.Extent2I(totalSize, totalSize))
for x in range(nCell):
for y in range(nCell):
tim[x * sizeCell + sizeCell // 2 + border - 1,
y * sizeCell + sizeCell // 2 + border - 1,
afwImage.LOCAL] = deltaFunctionCounts
# Turn this into stars with a narrow width; conserve counts
gaussFunction = afwMath.GaussianFunction2D(tGaussianWidth, tGaussianWidth)
gaussKernel = afwMath.AnalyticKernel(gaussKernelWidth, gaussKernelWidth,
gaussFunction)
cim = afwImage.ImageF(tim.getDimensions())
afwMath.convolve(cim, tim, gaussKernel, True)
tim = cim
# Trim off border pixels
bbox = gaussKernel.shrinkBBox(tim.getBBox(afwImage.LOCAL))
tim = afwImage.ImageF(tim, bbox, afwImage.LOCAL)
# Now make a science image which is this convolved with some
# spatial function. Use input basis list.
polyFunc = afwMath.PolynomialFunction2D(1)
kCoeffs = fakeCoeffs()
nToUse = min(len(kCoeffs), len(basisList))
# Make the full convolved science image
sKernel = afwMath.LinearCombinationKernel(basisList[:nToUse], polyFunc)
sKernel.setSpatialParameters(kCoeffs[:nToUse])
sim = afwImage.ImageF(tim.getDimensions())
afwMath.convolve(sim, tim, sKernel, True)
# Get the good subregion
bbox = sKernel.shrinkBBox(sim.getBBox(afwImage.LOCAL))
# Add background
sim += bgValue
# Watch out for negative values
tim += 2 * np.abs(np.min(tim.getArray()))
# Add noise?
if addNoise:
sim = makePoissonNoiseImage(sim)
tim = makePoissonNoiseImage(tim)
# And turn into MaskedImages
sim = afwImage.ImageF(sim, bbox, afwImage.LOCAL)
svar = afwImage.ImageF(sim, True)
smask = afwImage.Mask(sim.getDimensions())
smask.set(0x0)
sMi = afwImage.MaskedImageF(sim, smask, svar)
tim = afwImage.ImageF(tim, bbox, afwImage.LOCAL)
tvar = afwImage.ImageF(tim, True)
tmask = afwImage.Mask(tim.getDimensions())
tmask.set(0x0)
tMi = afwImage.MaskedImageF(tim, tmask, tvar)
if display:
import lsst.afw.display as afwDisplay
afwDisplay.Display(frame=1).mtv(tMi)
afwDisplay.Display(frame=2).mtv(sMi)
# Finally, make a kernelSet from these 2 images
kernelCellSet = afwMath.SpatialCellSet(
afwGeom.Box2I(afwGeom.Point2I(0, 0),
afwGeom.Extent2I(sizeCell * nCell, sizeCell * nCell)),
sizeCell, sizeCell)
stampHalfWidth = 2 * kSize
for x in range(nCell):
for y in range(nCell):
xCoord = x * sizeCell + sizeCell // 2
yCoord = y * sizeCell + sizeCell // 2
p0 = afwGeom.Point2I(xCoord - stampHalfWidth,
yCoord - stampHalfWidth)
p1 = afwGeom.Point2I(xCoord + stampHalfWidth,
yCoord + stampHalfWidth)
bbox = afwGeom.Box2I(p0, p1)
tsi = afwImage.MaskedImageF(tMi, bbox, origin=afwImage.LOCAL)
ssi = afwImage.MaskedImageF(sMi, bbox, origin=afwImage.LOCAL)
kc = diffimLib.makeKernelCandidate(xCoord, yCoord, tsi, ssi,
policyFake)
kernelCellSet.insertCandidate(kc)
tMi.setXY0(0, 0)
sMi.setXY0(0, 0)
return tMi, sMi, sKernel, kernelCellSet, configFake
def makeFakeKernelSet(sizeCell=128,
nCell=3,
deltaFunctionCounts=1.e4,
tGaussianWidth=1.0,
addNoise=True,
bgValue=100.,
display=False):
from . import imagePsfMatch
configFake = imagePsfMatch.ImagePsfMatchConfig()
configFake.kernel.name = "AL"
subconfigFake = configFake.kernel.active
subconfigFake.alardNGauss = 1
subconfigFake.alardSigGauss = [
2.5,
]
subconfigFake.alardDegGauss = [
2,
]
subconfigFake.sizeCellX = sizeCell
subconfigFake.sizeCellY = sizeCell
subconfigFake.spatialKernelOrder = 1
subconfigFake.spatialModelType = "polynomial"
subconfigFake.singleKernelClipping = False # variance is a hack
subconfigFake.spatialKernelClipping = False # variance is a hack
if bgValue > 0.0:
subconfigFake.fitForBackground = True
policyFake = pexConfig.makePolicy(subconfigFake)
basisList = makeKernelBasisList(subconfigFake)
kSize = subconfigFake.kernelSize
# This sets the final extent of each convolved delta function
gaussKernelWidth = sizeCell // 2
# This sets the scale over which pixels are correlated in the
# spatial convolution; should be at least as big as the kernel you
# are trying to fit for
spatialKernelWidth = kSize
# Number of bad pixels due to convolutions
border = (gaussKernelWidth + spatialKernelWidth) // 2
# Make a fake image with a matrix of delta functions
totalSize = nCell * sizeCell + 2 * border
tim = afwImage.ImageF(afwGeom.Extent2I(totalSize, totalSize))
for x in range(nCell):
for y in range(nCell):
tim.set(x * sizeCell + sizeCell // 2 + border - 1,
y * sizeCell + sizeCell // 2 + border - 1,
deltaFunctionCounts)
# Turn this into stars with a narrow width; conserve counts
gaussFunction = afwMath.GaussianFunction2D(tGaussianWidth, tGaussianWidth)
gaussKernel = afwMath.AnalyticKernel(gaussKernelWidth, gaussKernelWidth,
gaussFunction)
cim = afwImage.ImageF(tim.getDimensions())
afwMath.convolve(cim, tim, gaussKernel, True)
tim = cim
# Trim off border pixels
bbox = gaussKernel.shrinkBBox(tim.getBBox(afwImage.LOCAL))
tim = afwImage.ImageF(tim, bbox, afwImage.LOCAL)
# Now make a science image which is this convolved with some
# spatial function. Use input basis list.
polyFunc = afwMath.PolynomialFunction2D(1)
kCoeffs = fakeCoeffs()
nToUse = min(len(kCoeffs), len(basisList))
# Make the full convolved science image
sKernel = afwMath.LinearCombinationKernel(basisList[:nToUse], polyFunc)
sKernel.setSpatialParameters(kCoeffs[:nToUse])
sim = afwImage.ImageF(tim.getDimensions())
afwMath.convolve(sim, tim, sKernel, True)
# Get the good subregion
bbox = sKernel.shrinkBBox(sim.getBBox(afwImage.LOCAL))
# Add background
sim += bgValue
# Watch out for negative values
tim += 2 * np.abs(np.min(tim.getArray()))
# Add noise?
if addNoise:
sim = makePoissonNoiseImage(sim)
tim = makePoissonNoiseImage(tim)
# And turn into MaskedImages
sim = afwImage.ImageF(sim, bbox, afwImage.LOCAL)
svar = afwImage.ImageF(sim, True)
smask = afwImage.Mask(sim.getDimensions())
smask.set(0x0)
sMi = afwImage.MaskedImageF(sim, smask, svar)
tim = afwImage.ImageF(tim, bbox, afwImage.LOCAL)
tvar = afwImage.ImageF(tim, True)
tmask = afwImage.Mask(tim.getDimensions())
tmask.set(0x0)
tMi = afwImage.MaskedImageF(tim, tmask, tvar)
if display:
import lsst.afw.display.ds9 as ds9
ds9.mtv(tMi, frame=1)
ds9.mtv(sMi, frame=2)
# Finally, make a kernelSet from these 2 images
kernelCellSet = afwMath.SpatialCellSet(
afwGeom.Box2I(afwGeom.Point2I(0, 0),
afwGeom.Extent2I(sizeCell * nCell, sizeCell * nCell)),
sizeCell, sizeCell)
stampHalfWidth = 2 * kSize
for x in range(nCell):
for y in range(nCell):
xCoord = x * sizeCell + sizeCell // 2
yCoord = y * sizeCell + sizeCell // 2
p0 = afwGeom.Point2I(xCoord - stampHalfWidth,
yCoord - stampHalfWidth)
p1 = afwGeom.Point2I(xCoord + stampHalfWidth,
yCoord + stampHalfWidth)
bbox = afwGeom.Box2I(p0, p1)
tsi = afwImage.MaskedImageF(tMi, bbox, origin=afwImage.LOCAL)
ssi = afwImage.MaskedImageF(sMi, bbox, origin=afwImage.LOCAL)
kc = diffimLib.makeKernelCandidate(xCoord, yCoord, tsi, ssi,
policyFake)
kernelCellSet.insertCandidate(kc)
tMi.setXY0(0, 0)
sMi.setXY0(0, 0)
return tMi, sMi, sKernel, kernelCellSet, configFake