def testSetFluxConvervation(self):
"""Test that flux is preserved"""
outImage = afwMath.offsetImage(self.inImage, 0, 0, self.algorithm)
self.assertEqual(outImage.get(50, 50), self.background)
outImage = afwMath.offsetImage(self.inImage, 0.5, 0, self.algorithm)
self.assertAlmostEqual(outImage.get(50, 50), self.background, 4)
outImage = afwMath.offsetImage(self.inImage, 0.5, 0.5, self.algorithm)
self.assertAlmostEqual(outImage.get(50, 50), self.background, 4)
def testSetFluxConvervation(self):
"""Test that flux is preserved"""
for algorithm in ("lanczos5", "bilinear", "nearest"):
outImage = afwMath.offsetImage(self.inImage, 0, 0, algorithm)
self.assertEqual(outImage.get(50, 50), self.background)
outImage = afwMath.offsetImage(self.inImage, 0.5, 0, algorithm)
self.assertAlmostEqual(outImage.get(50, 50), self.background, 4)
outImage = afwMath.offsetImage(self.inImage, 0.5, 0.5, algorithm)
self.assertAlmostEqual(outImage.get(50, 50), self.background, 4)
def testSetFluxConvervation(self):
"""Test that flux is preserved"""
for algorithm in ("lanczos5", "bilinear", "nearest"):
outImage = afwMath.offsetImage(self.inImage, 0, 0, algorithm)
self.assertEqual(outImage.get(50, 50), self.background)
outImage = afwMath.offsetImage(self.inImage, 0.5, 0, algorithm)
self.assertAlmostEqual(outImage.get(50, 50), self.background, 4)
outImage = afwMath.offsetImage(self.inImage, 0.5, 0.5, algorithm)
self.assertAlmostEqual(outImage.get(50, 50), self.background, 4)
def testSetIntegerOffset(self):
"""Test that we can offset by positive and negative amounts.
"""
self.inImage[50, 50, afwImage.LOCAL] = 400
if False and display:
frame = 0
disp = afwDisplay.Display(frame=frame)
disp.mtv(self.inImage, title="Image for Integer Offset Test")
disp.pan(50, 50)
disp.dot("+", 50, 50)
for algorithm in ("lanczos5", "bilinear", "nearest"):
frame = 1
for delta in [-0.49, 0.51]:
for dx, dy in [(2, 3), (-2, 3), (-2, -3), (2, -3)]:
outImage = afwMath.offsetImage(self.inImage, dx + delta,
dy + delta, algorithm)
if False and display:
frame += 1
disp = afwDisplay.Display(frame=frame)
disp.mtv(outImage,
title=algorithm +
": offset image (dx, dy) = (%d, %d)" %
(dx, dy))
disp.pan(50, 50)
disp.dot("+", 50 + dx + delta - outImage.getX0(),
50 + dy + delta - outImage.getY0())
def testSetIntegerOffset(self):
"""Test that we can offset by positive and negative amounts"""
self.inImage.set(50, 50, 400)
if False and display:
frame = 0
ds9.mtv(self.inImage, frame=frame)
ds9.pan(50, 50, frame=frame)
ds9.dot("+", 50, 50, frame=frame)
for algorithm in ("lanczos5", "bilinear", "nearest"):
for delta in [-0.49, 0.51]:
for dx, dy in [(2, 3), (-2, 3), (-2, -3), (2, -3)]:
outImage = afwMath.offsetImage(self.inImage, dx + delta,
dy + delta, algorithm)
if False and display:
frame += 1
ds9.mtv(outImage, frame=frame)
ds9.pan(50, 50, frame=frame)
ds9.dot("+",
50 + dx + delta - outImage.getX0(),
50 + dy + delta - outImage.getY0(),
frame=frame)
def testSetIntegerOffset(self):
"""Test that we can offset by positive and negative amounts.
"""
self.inImage[50, 50, afwImage.LOCAL] = 400
if False and display:
frame = 0
disp = afwDisplay.Display(frame=frame)
disp.mtv(self.inImage, title="Image for Integer Offset Test")
disp.pan(50, 50)
disp.dot("+", 50, 50)
for algorithm in ("lanczos5", "bilinear", "nearest"):
frame = 1
for delta in [-0.49, 0.51]:
for dx, dy in [(2, 3), (-2, 3), (-2, -3), (2, -3)]:
outImage = afwMath.offsetImage(
self.inImage, dx + delta, dy + delta, algorithm)
if False and display:
frame += 1
disp = afwDisplay.Display(frame=frame)
disp.mtv(outImage, title=algorithm + ": offset image (dx, dy) = (%d, %d)" % (dx, dy))
disp.pan(50, 50)
disp.dot("+", 50 + dx + delta - outImage.getX0(), 50 + dy + delta - outImage.getY0())
def saveSpatialCellSet(psfCellSet, fileName="foo.fits", display=None):
"""Write the contents of a SpatialCellSet to a many-MEF fits file"""
mode = "w"
for cell in psfCellSet.getCellList():
for cand in cell.begin(False): # include bad candidates
dx = afwImage.positionToIndex(cand.getXCenter(), True)[1]
dy = afwImage.positionToIndex(cand.getYCenter(), True)[1]
im = afwMath.offsetImage(cand.getMaskedImage(), -dx, -dy, "lanczos5")
md = dafBase.PropertySet()
md.set("CELL", cell.getLabel())
md.set("ID", cand.getId())
md.set("XCENTER", cand.getXCenter())
md.set("YCENTER", cand.getYCenter())
md.set("BAD", cand.isBad())
md.set("AMPL", cand.getAmplitude())
md.set("FLUX", cand.getSource().getPsfInstFlux())
md.set("CHI2", cand.getSource().getChi2())
im.writeFits(fileName, md, mode)
mode = "a"
if display:
display.mtv(im, title="saveSpatialCellSet: image")
def saveSpatialCellSet(psfCellSet, fileName="foo.fits", frame=None):
"""Write the contents of a SpatialCellSet to a many-MEF fits file"""
mode = "w"
for cell in psfCellSet.getCellList():
for cand in cell.begin(False): # include bad candidates
cand = algorithmsLib.cast_PsfCandidateF(cand)
dx = afwImage.positionToIndex(cand.getXCenter(), True)[1]
dy = afwImage.positionToIndex(cand.getYCenter(), True)[1]
im = afwMath.offsetImage(cand.getMaskedImage(), -dx, -dy, "lanczos5")
md = dafBase.PropertySet()
md.set("CELL", cell.getLabel())
md.set("ID", cand.getId())
md.set("XCENTER", cand.getXCenter())
md.set("YCENTER", cand.getYCenter())
md.set("BAD", cand.isBad())
md.set("AMPL", cand.getAmplitude())
md.set("FLUX", cand.getSource().getPsfFlux())
md.set("CHI2", cand.getSource().getChi2())
im.writeFits(fileName, md, mode)
mode = "a"
if frame is not None:
ds9.mtv(im, frame=frame)
def testOffsetGaussian(self):
"""Insert a Gaussian, offset, and check the residuals.
"""
size = 50
refIm = afwImage.ImageF(size, size)
unshiftedIm = afwImage.ImageF(size, size)
xc, yc = size / 2.0, size / 2.0
amp, sigma1 = 1.0, 3
#
# Calculate Gaussian directly at (xc, yc)
#
self.calcGaussian(refIm, xc, yc, amp, sigma1)
for dx in (-55.5, -1.500001, -1.5, -1.499999, -1.00001, -1.0, -0.99999,
-0.5, 0.0, 0.5, 0.99999, 1.0, 1.00001, 1.499999, 1.5,
1.500001, 99.3):
for dy in (-3.7, -1.500001, -1.5, -1.499999, -1.00001, -1.0,
-0.99999, -0.5, 0.0, 0.5, 0.99999, 1.0, 1.00001,
1.499999, 1.5, 1.500001, 2.99999):
dOrigX, dOrigY, dFracX, dFracY = getOrigFracShift(dx, dy)
self.calcGaussian(unshiftedIm, xc - dFracX, yc - dFracY, amp,
sigma1)
for algorithm, maxMean, maxLim in (
("lanczos5", 1e-8, 0.0015),
("bilinear", 1e-8, 0.03),
("nearest", 1e-8, 0.2),
):
im = afwImage.ImageF(size, size)
im = afwMath.offsetImage(unshiftedIm, dx, dy, algorithm)
if display:
afwDisplay.Display(frame=0).mtv(
im, title=f"{algorithm}: image")
im -= refIm
if display:
afwDisplay.Display(frame=1).mtv(
im, title=f"{algorithm}: diff image ({dx}, {dy})")
imArr = im.getArray()
imGoodVals = np.ma.array(
imArr, copy=False, mask=np.isnan(imArr)).compressed()
try:
imXY0 = tuple(im.getXY0())
self.assertEqual(imXY0, (dOrigX, dOrigY))
self.assertLess(abs(imGoodVals.mean()), maxMean * amp)
self.assertLess(abs(imGoodVals.max()), maxLim * amp)
self.assertLess(abs(imGoodVals.min()), maxLim * amp)
except Exception:
print(
f"failed on algorithm={algorithm}; dx = {dx}; dy = {dy}"
)
raise
def setUp(self):
width, height = 100, 300
self.mi = afwImage.MaskedImageF(afwGeom.ExtentI(width, height))
self.mi.set(0)
self.mi.getVariance().set(10)
self.mi.getMask().addMaskPlane("DETECTED")
self.FWHM = 5
self.ksize = 25 # size of desired kernel
self.exposure = afwImage.makeExposure(self.mi)
psf = roundTripPsf(2, algorithms.DoubleGaussianPsf(self.ksize, self.ksize,
self.FWHM/(2*sqrt(2*log(2))), 1, 0.1))
self.exposure.setPsf(psf)
for x, y in [(20, 20),
#(30, 35), (50, 50),
(60, 20), (60, 210), (20, 210)]:
flux = 10000 - 0*x - 10*y
sigma = 3 + 0.01*(y - self.mi.getHeight()/2)
psf = roundTripPsf(3, algorithms.DoubleGaussianPsf(self.ksize, self.ksize, sigma, 1, 0.1))
im = psf.computeImage().convertF()
im *= flux
smi = self.mi.getImage().Factory(self.mi.getImage(),
afwGeom.BoxI(afwGeom.PointI(x - self.ksize/2, y - self.ksize/2),
afwGeom.ExtentI(self.ksize)),
afwImage.LOCAL)
if False: # Test subtraction with non-centered psfs
im = afwMath.offsetImage(im, 0.5, 0.5)
smi += im
del psf; del im; del smi
psf = roundTripPsf(4, algorithms.DoubleGaussianPsf(self.ksize, self.ksize,
self.FWHM/(2*sqrt(2*log(2))), 1, 0.1))
self.cellSet = afwMath.SpatialCellSet(afwGeom.BoxI(afwGeom.PointI(0, 0), afwGeom.ExtentI(width, height)), 100)
ds = afwDetection.FootprintSet(self.mi, afwDetection.Threshold(10), "DETECTED")
#
# Prepare to measure
#
msConfig = algorithms.SourceMeasurementConfig()
msConfig.load("tests/config/MeasureSources.py")
schema = afwTable.SourceTable.makeMinimalSchema()
measureSources = msConfig.makeMeasureSources(schema)
catalog = afwTable.SourceCatalog(schema)
msConfig.slots.calibFlux = None
msConfig.slots.setupTable(catalog.table)
ds.makeSources(catalog)
for i, source in enumerate(catalog):
measureSources.applyWithPeak(source, self.exposure)
self.cellSet.insertCandidate(algorithms.makePsfCandidate(source, self.exposure))
def testOffsetGaussian(self):
"""Insert a Gaussian, offset, and check the residuals.
"""
size = 50
refIm = afwImage.ImageF(size, size)
unshiftedIm = afwImage.ImageF(size, size)
xc, yc = size/2.0, size/2.0
amp, sigma1 = 1.0, 3
#
# Calculate Gaussian directly at (xc, yc)
#
self.calcGaussian(refIm, xc, yc, amp, sigma1)
for dx in (-55.5, -1.500001, -1.5, -1.499999, -1.00001, -1.0, -0.99999, -0.5,
0.0, 0.5, 0.99999, 1.0, 1.00001, 1.499999, 1.5, 1.500001, 99.3):
for dy in (-3.7, -1.500001, -1.5, -1.499999, -1.00001, -1.0, -0.99999, -0.5,
0.0, 0.5, 0.99999, 1.0, 1.00001, 1.499999, 1.5, 1.500001, 2.99999):
dOrigX, dOrigY, dFracX, dFracY = getOrigFracShift(dx, dy)
self.calcGaussian(unshiftedIm, xc - dFracX,
yc - dFracY, amp, sigma1)
for algorithm, maxMean, maxLim in (
("lanczos5", 1e-8, 0.0015),
("bilinear", 1e-8, 0.03),
("nearest", 1e-8, 0.2),
):
im = afwImage.ImageF(size, size)
im = afwMath.offsetImage(unshiftedIm, dx, dy, algorithm)
if display:
afwDisplay.Display(frame=0).mtv(im, title=algorithm + ": image")
im -= refIm
if display:
afwDisplay.Display(frame=1).mtv(im, title=algorithm +
": diff image (dx, dy) = (%f, %f)" % (dx, dy))
imArr = im.getArray()
imGoodVals = np.ma.array(
imArr, copy=False, mask=np.isnan(imArr)).compressed()
try:
imXY0 = tuple(im.getXY0())
self.assertEqual(imXY0, (dOrigX, dOrigY))
self.assertLess(abs(imGoodVals.mean()), maxMean*amp)
self.assertLess(abs(imGoodVals.max()), maxLim*amp)
self.assertLess(abs(imGoodVals.min()), maxLim*amp)
except Exception:
print("failed on algorithm=%s; dx = %s; dy = %s" %
(algorithm, dx, dy))
raise
def addFakeSources(self, image, fakeImages, sourceType):
"""Add the fake sources to the given image
Parameters
----------
image : `lsst.afw.image.exposure.exposure.ExposureF`
The image into which the fake sources should be added
fakeImages : `typing.Iterator` [`tuple` ['lsst.afw.image.ImageF`, `lsst.geom.Point2d`]]
An iterator of tuples that contains (or generates) images of fake sources,
and the locations they are to be inserted at.
sourceType : `str`
The type (star/galaxy) of fake sources input
Returns
-------
image : `lsst.afw.image.exposure.exposure.ExposureF`
Notes
-----
Uses the x, y information in the ``fakeCat`` to position an image of the fake interpolated onto the
pixel grid of the image. Sets the ``FAKE`` mask plane for the pixels added with the fake source.
"""
imageBBox = image.getBBox()
imageMI = image.maskedImage
for (fakeImage, xy) in fakeImages:
X0 = xy.getX() - fakeImage.getWidth() / 2 + 0.5
Y0 = xy.getY() - fakeImage.getHeight() / 2 + 0.5
self.log.debug("Adding fake source at %d, %d" %
(xy.getX(), xy.getY()))
if sourceType == "galaxy":
interpFakeImage = afwMath.offsetImage(fakeImage, X0, Y0,
"lanczos3")
interpFakeImBBox = interpFakeImage.getBBox()
else:
interpFakeImage = fakeImage
interpFakeImBBox = fakeImage.getBBox()
interpFakeImBBox.clip(imageBBox)
imageMIView = imageMI.Factory(imageMI, interpFakeImBBox)
if interpFakeImBBox.getArea() > 0:
clippedFakeImage = interpFakeImage.Factory(
interpFakeImage, interpFakeImBBox)
clippedFakeImageMI = afwImage.MaskedImageF(clippedFakeImage)
clippedFakeImageMI.mask.set(self.bitmask)
imageMIView += clippedFakeImageMI
return image
def testOffsetGaussian(self):
"""Insert a Gaussian, offset, and check the residuals"""
size = 100
im = afwImage.ImageF(afwGeom.Extent2I(size, size))
xc, yc = size/2.0, size/2.0
amp, sigma1 = 1.0, 3
#
# Calculate an image with a Gaussian at (xc -dx, yc - dy) and then shift it to (xc, yc)
#
dx, dy = 0.5, -0.5
self.calcGaussian(im, xc - dx, yc - dy, amp, sigma1)
im2 = afwMath.offsetImage(im, dx, dy, "lanczos5")
#
# Calculate Gaussian directly at (xc, yc)
#
self.calcGaussian(im, xc, yc, amp, sigma1)
#
# See how they differ
#
if display:
ds9.mtv(im, frame=0)
im -= im2
if display:
ds9.mtv(im, frame=1)
imArr = im.getArray()
imGoodVals = numpy.ma.array(imArr, copy=False, mask=numpy.isnan(imArr)).compressed()
imMean = imGoodVals.mean()
imMax = imGoodVals.max()
imMin = imGoodVals.min()
if False:
print "mean = %g, min = %g, max = %g" % (imMean, imMin, imMax)
self.assertTrue(abs(imMean) < 1e-7)
self.assertTrue(abs(imMin) < 1.2e-3*amp)
self.assertTrue(abs(imMax) < 1.2e-3*amp)
def testSetIntegerOffset(self):
"""Test that we can offset by positive and negative amounts"""
self.inImage.set(50, 50, 400)
if False and display:
frame = 0
ds9.mtv(self.inImage, frame=frame)
ds9.pan(50, 50, frame=frame)
ds9.dot("+", 50, 50, frame=frame)
for delta in [-0.49, 0.51]:
for dx, dy in [(2, 3), (-2, 3), (-2, -3), (2, -3)]:
outImage = afwMath.offsetImage(self.inImage, dx + delta, dy + delta, self.algorithm)
if False and display:
frame += 1
ds9.mtv(outImage, frame=frame)
ds9.pan(50, 50, frame=frame)
ds9.dot("+", 50 + dx + delta - outImage.getX0(), 50 + dy + delta - outImage.getY0(),
frame=frame)
def setUp(self):
width, height = 100, 300
self.mi = afwImage.MaskedImageF(lsst.geom.ExtentI(width, height))
self.mi.set(0)
self.mi.getVariance().set(10)
self.mi.getMask().addMaskPlane("DETECTED")
self.FWHM = 5
self.ksize = 25 # size of desired kernel
self.exposure = afwImage.makeExposure(self.mi)
psf = roundTripPsf(2, algorithms.DoubleGaussianPsf(self.ksize, self.ksize,
self.FWHM/(2*math.sqrt(2*math.log(2))), 1, 0.1))
self.exposure.setPsf(psf)
for x, y in [(20, 20),
# (30, 35), (50, 50),
(60, 20), (60, 210), (20, 210)]:
flux = 10000 - 0*x - 10*y
sigma = 3 + 0.01*(y - self.mi.getHeight()/2)
psf = roundTripPsf(3, algorithms.DoubleGaussianPsf(self.ksize, self.ksize, sigma, 1, 0.1))
im = psf.computeImage().convertF()
im *= flux
x0y0 = lsst.geom.PointI(x - self.ksize//2, y - self.ksize//2)
smi = self.mi.getImage().Factory(self.mi.getImage(),
lsst.geom.BoxI(x0y0, lsst.geom.ExtentI(self.ksize)),
afwImage.LOCAL)
if False: # Test subtraction with non-centered psfs
im = afwMath.offsetImage(im, 0.5, 0.5)
smi += im
del psf
del im
del smi
roundTripPsf(4, algorithms.DoubleGaussianPsf(self.ksize, self.ksize,
self.FWHM/(2*math.sqrt(2*math.log(2))), 1, 0.1))
self.cellSet = afwMath.SpatialCellSet(lsst.geom.BoxI(lsst.geom.PointI(0, 0),
lsst.geom.ExtentI(width, height)), 100)
ds = afwDetection.FootprintSet(self.mi, afwDetection.Threshold(10), "DETECTED")
#
# Prepare to measure
#
schema = afwTable.SourceTable.makeMinimalSchema()
sfm_config = measBase.SingleFrameMeasurementConfig()
sfm_config.plugins = ["base_SdssCentroid", "base_CircularApertureFlux", "base_PsfFlux",
"base_SdssShape", "base_GaussianFlux", "base_PixelFlags"]
sfm_config.slots.centroid = "base_SdssCentroid"
sfm_config.slots.shape = "base_SdssShape"
sfm_config.slots.psfFlux = "base_PsfFlux"
sfm_config.slots.gaussianFlux = None
sfm_config.slots.apFlux = "base_CircularApertureFlux_3_0"
sfm_config.slots.modelFlux = "base_GaussianFlux"
sfm_config.slots.calibFlux = None
sfm_config.plugins["base_SdssShape"].maxShift = 10.0
sfm_config.plugins["base_CircularApertureFlux"].radii = [3.0]
task = measBase.SingleFrameMeasurementTask(schema, config=sfm_config)
measCat = afwTable.SourceCatalog(schema)
# detect the sources and run with the measurement task
ds.makeSources(measCat)
task.run(measCat, self.exposure)
for source in measCat:
self.cellSet.insertCandidate(algorithms.makePsfCandidate(source, self.exposure))
def setUp(self):
width, height = 100, 300
self.mi = afwImage.MaskedImageF(afwGeom.ExtentI(width, height))
self.mi.set(0)
self.mi.getVariance().set(10)
self.mi.getMask().addMaskPlane("DETECTED")
self.FWHM = 5
self.ksize = 25 # size of desired kernel
self.exposure = afwImage.makeExposure(self.mi)
psf = roundTripPsf(
2,
algorithms.DoubleGaussianPsf(self.ksize, self.ksize,
self.FWHM / (2 * sqrt(2 * log(2))), 1,
0.1))
self.exposure.setPsf(psf)
for x, y in [
(20, 20),
#(30, 35), (50, 50),
(60, 20),
(60, 210),
(20, 210)
]:
flux = 10000 - 0 * x - 10 * y
sigma = 3 + 0.01 * (y - self.mi.getHeight() / 2)
psf = roundTripPsf(
3,
algorithms.DoubleGaussianPsf(self.ksize, self.ksize, sigma, 1,
0.1))
im = psf.computeImage().convertF()
im *= flux
smi = self.mi.getImage().Factory(
self.mi.getImage(),
afwGeom.BoxI(
afwGeom.PointI(x - self.ksize / 2, y - self.ksize / 2),
afwGeom.ExtentI(self.ksize)), afwImage.LOCAL)
if False: # Test subtraction with non-centered psfs
im = afwMath.offsetImage(im, 0.5, 0.5)
smi += im
del psf
del im
del smi
psf = roundTripPsf(
4,
algorithms.DoubleGaussianPsf(self.ksize, self.ksize,
self.FWHM / (2 * sqrt(2 * log(2))), 1,
0.1))
self.cellSet = afwMath.SpatialCellSet(
afwGeom.BoxI(afwGeom.PointI(0, 0), afwGeom.ExtentI(width, height)),
100)
ds = afwDetection.FootprintSet(self.mi, afwDetection.Threshold(10),
"DETECTED")
#
# Prepare to measure
#
msConfig = algorithms.SourceMeasurementConfig()
msConfig.load("tests/config/MeasureSources.py")
schema = afwTable.SourceTable.makeMinimalSchema()
measureSources = msConfig.makeMeasureSources(schema)
catalog = afwTable.SourceCatalog(schema)
msConfig.slots.calibFlux = None
msConfig.slots.setupTable(catalog.table)
ds.makeSources(catalog)
for i, source in enumerate(catalog):
measureSources.applyWithPeak(source, self.exposure)
self.cellSet.insertCandidate(
algorithms.makePsfCandidate(source, self.exposure))
def setUp(self):
width, height = 100, 300
self.mi = afwImage.MaskedImageF(afwGeom.ExtentI(width, height))
self.mi.set(0)
self.mi.getVariance().set(10)
self.mi.getMask().addMaskPlane("DETECTED")
self.FWHM = 5
self.ksize = 25 # size of desired kernel
self.exposure = afwImage.makeExposure(self.mi)
psf = roundTripPsf(
2,
algorithms.DoubleGaussianPsf(
self.ksize, self.ksize,
self.FWHM / (2 * math.sqrt(2 * math.log(2))), 1, 0.1))
self.exposure.setPsf(psf)
for x, y in [
(20, 20),
#(30, 35), (50, 50),
(60, 20),
(60, 210),
(20, 210)
]:
flux = 10000 - 0 * x - 10 * y
sigma = 3 + 0.01 * (y - self.mi.getHeight() / 2)
psf = roundTripPsf(
3,
algorithms.DoubleGaussianPsf(self.ksize, self.ksize, sigma, 1,
0.1))
im = psf.computeImage().convertF()
im *= flux
x0y0 = afwGeom.PointI(x - self.ksize // 2, y - self.ksize // 2)
smi = self.mi.getImage().Factory(
self.mi.getImage(),
afwGeom.BoxI(x0y0, afwGeom.ExtentI(self.ksize)),
afwImage.LOCAL)
if False: # Test subtraction with non-centered psfs
im = afwMath.offsetImage(im, 0.5, 0.5)
smi += im
del psf
del im
del smi
roundTripPsf(
4,
algorithms.DoubleGaussianPsf(
self.ksize, self.ksize,
self.FWHM / (2 * math.sqrt(2 * math.log(2))), 1, 0.1))
self.cellSet = afwMath.SpatialCellSet(
afwGeom.BoxI(afwGeom.PointI(0, 0), afwGeom.ExtentI(width, height)),
100)
ds = afwDetection.FootprintSet(self.mi, afwDetection.Threshold(10),
"DETECTED")
#
# Prepare to measure
#
schema = afwTable.SourceTable.makeMinimalSchema()
sfm_config = measBase.SingleFrameMeasurementConfig()
sfm_config.plugins = [
"base_SdssCentroid", "base_CircularApertureFlux", "base_PsfFlux",
"base_SdssShape", "base_GaussianFlux", "base_PixelFlags"
]
sfm_config.slots.centroid = "base_SdssCentroid"
sfm_config.slots.shape = "base_SdssShape"
sfm_config.slots.psfFlux = "base_PsfFlux"
sfm_config.slots.instFlux = None
sfm_config.slots.apFlux = "base_CircularApertureFlux_3_0"
sfm_config.slots.modelFlux = "base_GaussianFlux"
sfm_config.slots.calibFlux = None
sfm_config.plugins["base_SdssShape"].maxShift = 10.0
sfm_config.plugins["base_CircularApertureFlux"].radii = [3.0]
task = measBase.SingleFrameMeasurementTask(schema, config=sfm_config)
measCat = afwTable.SourceCatalog(schema)
# detect the sources and run with the measurement task
ds.makeSources(measCat)
task.run(measCat, self.exposure)
for source in measCat:
self.cellSet.insertCandidate(
algorithms.makePsfCandidate(source, self.exposure))
# add Noise and write image/psf
for filter_name, image in images.items():
tot_images[filter_name].addNoise(noise)
tot_images[filter_name].write('%s/image_%s.fits' %
(output_dir, filter_name))
psf_bounds = galsim.BoundsI(0, 41, 0, 41)
psf_image = galsim.ImageF(psf_bounds, scale=scale)
psf.drawImage(image=psf_image)
psf_image.write('%s/psf_image.fits' % output_dir)
# Read in PSF
lsst_psf_image = afwImage.ImageF('%s/psf_image.fits' % output_dir)
bbox = afwGeom.Box2I(afwGeom.Point2I(0, 0), afwGeom.Extent2I(41, 41))
lsst_psf_image = lsst_psf_image[bbox].convertD()
lsst_psf_image = afwMath.offsetImage(lsst_psf_image, -0.5, -0.5)
kernel = afwMath.FixedKernel(lsst_psf_image)
kernelPsf = measAlg.KernelPsf(kernel)
# Exposure setup
wcs = afwImage.makeWcs(
afwCoord.Coord(0. * afwGeom.degrees, 0. * afwGeom.degrees),
afwGeom.Point2D(0.0, 0.0), 0.168 * 3600, 0.0, 0.0, 0.168 * 3600)
calib = afwImage.Calib()
calib.setFluxMag0(1e12)
# Config and task setup
measure_config = SingleFrameMeasurementTask.ConfigClass()
measure_config.load(
'/tigress/rea3/lsst/DM-8059/obs_subaru/config/apertures.py')
measure_config.load('/tigress/rea3/lsst/DM-8059/obs_subaru/config/kron.py')
