def testCreate(self):
rngs = []
for name in afwMath.Random.getAlgorithmNames():
rngs.append(afwMath.Random(name))
for r in rngs:
assert afwMath.Random(r.getAlgorithmName()).uniform() == r.uniform()
r2 = afwMath.Random(r.getAlgorithm())
r2.uniform()
assert r2.uniform() == r.uniform()
def testPolicy(self):
"""
Tests that policy files and environment variables can override
user specified algorithms and seed values.
"""
pol = pexPolicy.Policy()
seed = getSeed()
pol.set("rngSeed", str(seed))
pol.set("rngAlgorithm", afwMath.Random.getAlgorithmNames()[afwMath.Random.RANLXD2])
r1 = afwMath.Random(afwMath.Random.RANLXD2, seed)
r2 = afwMath.Random(pol)
checkRngEquivalence(r1, r2)
def testWeightedStats(self):
"""Test that bug from #1697 (weighted stats returning NaN) stays fixed."""
rand = afwMath.Random()
mu = 10000
edgeMask = afwImage.MaskU.getPlaneBitMask("EDGE")
badPixelMask = afwImage.MaskU.getPlaneBitMask("EDGE")
statsCtrl = afwMath.StatisticsControl()
statsCtrl.setNumSigmaClip(3.0)
statsCtrl.setNumIter(2)
statsCtrl.setAndMask(badPixelMask)
for weight in (300.0, 10.0, 1.0):
print "Testing with weight=%0.1f" % (weight,)
maskedImageList = afwImage.vectorMaskedImageF() # [] is rejected by afwMath.statisticsStack
weightList = []
nx, ny = 256, 256
for i in range(3):
print "Processing ", i
maskedImage = afwImage.MaskedImageF(nx, ny)
maskedImageList.append(maskedImage)
afwMath.randomPoissonImage(maskedImage.getImage(), rand, mu)
maskedImage.getVariance().set(mu)
weightList.append(weight)
self.reportBadPixels(maskedImage, badPixelMask)
print "Stack: ",
coaddMaskedImage = afwMath.statisticsStack(
maskedImageList, afwMath.MEANCLIP, statsCtrl, weightList)
self.reportBadPixels(coaddMaskedImage, badPixelMask)
def _testAverageVersusCopy(self, withNaNs=False):
"""Re-run `testExampleTaskNoOverlaps` and `testExampleTaskWithOverlaps`
on a more complex image (with random noise). Ensure that the results are
identical (within between 'copy' and 'average' reduceOperation.
"""
exposure1 = self.exposure.clone()
img = exposure1.getMaskedImage().getImage()
afwMath.randomGaussianImage(img, afwMath.Random())
exposure2 = exposure1.clone()
config = AddAmountImageMapReduceConfig()
task = ImageMapReduceTask(config)
config.mapper.addAmount = 5.
newExp = task.run(exposure1, addNans=withNaNs).exposure
newMI1 = newExp.getMaskedImage()
config.gridStepX = config.gridStepY = 8.
config.reducer.reduceOperation = 'average'
task = ImageMapReduceTask(config)
newExp = task.run(exposure2, addNans=withNaNs).exposure
newMI2 = newExp.getMaskedImage()
newMA1 = newMI1.getImage().getArray()
isnan = np.isnan(newMA1)
if not withNaNs:
self.assertEqual(np.sum(isnan), 0)
newMA2 = newMI2.getImage().getArray()
# Because the average uses a float accumulator, we can have differences, set a tolerance.
# Turns out (in practice for this test), only 7 pixels seem to have a small difference.
self.assertFloatsAlmostEqual(newMA1[~isnan], newMA2[~isnan], rtol=1e-7)
def testCopy(self):
"""
Test that the generator returned by deepCopy() produces an
identical random number sequence to its prototype
"""
rng1 = afwMath.Random(afwMath.Random.MT19937, getSeed())
rng2 = rng1.deepCopy()
checkRngEquivalence(rng1, rng2)
def addNoise(self, mi):
img = mi.getImage()
seed = int(afwMath.makeStatistics(mi.getVariance(), afwMath.MEDIAN).getValue())
rdm = afwMath.Random(afwMath.Random.MT19937, seed)
rdmImage = img.Factory(img.getDimensions())
afwMath.randomGaussianImage(rdmImage, rdm)
img += rdmImage
return afwMath.makeStatistics(rdmImage, afwMath.MEAN).getValue(afwMath.MEAN)
def makeFlatNoiseImage(mi, seedStat=afwMath.MAX):
img = mi.getImage()
seed = int(10. *
afwMath.makeStatistics(mi.getImage(), seedStat).getValue() + 1)
rdm = afwMath.Random(afwMath.Random.MT19937, seed)
rdmImage = img.Factory(img.getDimensions())
afwMath.randomGaussianImage(rdmImage, rdm)
return rdmImage
def __init__(self, **kwargs):
BaseFakeSourcesTask.__init__(self, **kwargs)
print("RNG seed:", self.config.seed)
self.rng = afwMath.Random(seed=self.config.seed)
self.npRand = np.random.RandomState(self.config.seed)
try:
self.starData = fits.open(self.config.starList)[1].data
except Exception:
raise
def testChipGapHorizontalBackground(self):
""" Test able to match image with horizontal chip gap (row of nans) with .Background"""
self.matcher.config.usePolynomial = False
self.matcher.config.binSize = 64
chipGapHorizontal = afwImage.ExposureF(600, 600)
im = chipGapHorizontal.getMaskedImage().getImage()
afwMath.randomGaussianImage(im, afwMath.Random())
im += 10
im.getArray()[200:300, :] = np.nan # simulate 100pix chip gap horizontal
chipGapHorizontal.getMaskedImage().getVariance().set(1.0)
self.checkAccuracy(self.vanilla, chipGapHorizontal)
def addNoise(mi):
img = mi.getImage()
seed = int(afwMath.makeStatistics(mi.getVariance(), afwMath.MAX).getValue())+1
rdm = afwMath.Random(afwMath.Random.MT19937, seed)
rdmImage = img.Factory(img.getDimensions())
afwMath.randomGaussianImage(rdmImage, rdm)
rdmImage *= num.sqrt(seed)
img += rdmImage
# and don't forget to add to the variance
var = mi.getVariance()
var += 1.0
def setUp(self):
np.random.seed(1)
# Make a few test images here
# 1) full coverage (plain vanilla image) has mean = 50counts
self.vanilla = afwImage.ExposureF(600, 600)
im = self.vanilla.getMaskedImage().getImage()
afwMath.randomGaussianImage(im, afwMath.Random('MT19937', 1))
im += 50
self.vanilla.getMaskedImage().getVariance().set(1.0)
# 2) has chip gap and mean = 10 counts
self.chipGap = afwImage.ExposureF(600, 600)
im = self.chipGap.getMaskedImage().getImage()
afwMath.randomGaussianImage(im, afwMath.Random('MT19937', 2))
im += 10
im.getArray()[:, 200:300] = np.nan # simulate 100pix chip gap
self.chipGap.getMaskedImage().getVariance().set(1.0)
# 3) has low coverage and mean = 20 counts
self.lowCover = afwImage.ExposureF(600, 600)
im = self.lowCover.getMaskedImage().getImage()
afwMath.randomGaussianImage(im, afwMath.Random('MT19937', 3))
im += 20
self.lowCover.getMaskedImage().getImage().getArray()[:, 200:] = np.nan
self.lowCover.getMaskedImage().getVariance().set(1.0)
# make a matchBackgrounds object
self.matcher = MatchBackgroundsTask()
self.matcher.config.usePolynomial = True
self.matcher.binSize = 64
self.matcher.debugDataIdString = 'Test Visit'
self.sctrl = afwMath.StatisticsControl()
self.sctrl.setNanSafe(True)
self.sctrl.setAndMask(
afwImage.Mask.getPlaneBitMask([
"NO_DATA", "DETECTED", "DETECTED_NEGATIVE", "SAT", "BAD",
"INTRP", "CR"
]))
def testRampApproximate(self):
"""Test basic matching of a linear gradient with Approximate."""
self.matcher.config.binSize = 64
testExp = afwImage.ExposureF(self.vanilla, True)
testIm = testExp.getMaskedImage().getImage()
afwMath.randomGaussianImage(testIm, afwMath.Random())
nx, ny = testExp.getDimensions()
dzdx, dzdy, z0 = 1, 2, 0.0
for x in range(nx):
for y in range(ny):
z = testIm.get(x, y)
testIm.set(x, y, z + dzdx * x + dzdy * y + z0)
self.checkAccuracy(testExp, self.vanilla)
def testRampBackground(self):
"""Test basic matching of a linear gradient with .Background."""
self.matcher.config.usePolynomial = False
self.matcher.config.binSize = 64
testExp = afwImage.ExposureF(self.vanilla, True)
testIm = testExp.getMaskedImage().getImage()
afwMath.randomGaussianImage(testIm, afwMath.Random())
nx, ny = testExp.getDimensions()
dzdx, dzdy, z0 = 1, 2, 0.0
for x in range(nx):
for y in range(ny):
z = testIm[x, y, afwImage.LOCAL]
testIm[x, y, afwImage.LOCAL] = z + dzdx * x + dzdy * y + z0
self.checkAccuracy(testExp, self.vanilla)
def makeImage(width=500, height=1000):
mi = afwImage.MaskedImageF(width, height)
var = 50
mi.set(1000, 0x0, var)
addSaturated(mi, addCrosstalk=True)
ralg, rseed = "MT19937", int(time.time()) if True else 1234
noise = afwImage.ImageF(width, height)
afwMath.randomGaussianImage(noise, afwMath.Random(ralg, rseed))
noise *= math.sqrt(var)
mi += noise
return mi
def setUp(self):
self.random = afwMath.Random()
self.imWidth = 200
self.imHeight = 200
self.maskedImage0 = afwImage.MaskedImageF(
lsst.geom.Extent2I(self.imWidth, self.imHeight))
afwMath.randomUniformImage(self.maskedImage0.getImage(), self.random)
afwMath.randomUniformImage(self.maskedImage0.getVariance(),
self.random)
# afwMath.randomUniformImage(self.maskedImage0.getMask(), self.random)
self.maskedImage1 = afwImage.MaskedImageF(
lsst.geom.Extent2I(self.imWidth, self.imHeight))
afwMath.randomUniformImage(self.maskedImage1.getImage(), self.random)
afwMath.randomUniformImage(self.maskedImage1.getVariance(),
self.random)
def test1(self):
task = measAlg.ReplaceWithNoiseTask()
schema = afwTable.SourceTable.makeMinimalSchema()
table = afwTable.SourceTable.make(schema)
sources = afwTable.SourceCatalog(table)
im = afwImage.ImageF(200, 50)
seed = 42
rand = afwMath.Random(afwMath.Random.MT19937, seed)
afwMath.randomGaussianImage(im, rand)
s = sources.addNew()
s.setId(1)
fp = afwDet.Footprint()
y, x0, x1 = (10, 10, 190)
im.getArray()[y, x0:x1] = 10
fp.addSpan(y, x0, x1)
s.setFootprint(fp)
s = sources.addNew()
s.setId(2)
fp = afwDet.Footprint()
y, x0, x1 = (40, 10, 190)
im.getArray()[y, x0:x1] = 10
fp.addSpan(y, x0, x1)
s.setFootprint(fp)
mi = afwImage.MaskedImageF(im)
exposure = afwImage.makeExposure(mi)
self._save(mi, 'a')
task.begin(exposure, sources)
self._save(mi, 'b')
sourcei = 0
task.insertSource(exposure, sourcei)
self._save(mi, 'c')
# do something
task.removeSource(exposure, sources, sources[sourcei])
self._save(mi, 'd')
sourcei = 1
task.insertSource(exposure, sourcei)
self._save(mi, 'e')
# do something
task.removeSource(exposure, sources, sources[sourcei])
self._save(mi, 'f')
task.end(exposure, sources)
self._save(mi, 'g')
def addCosmicRays(image, nCR=100, emin=800, emax=1000, seed=None):
"""Add nCR fake cosmic rays to a frame, with pixel values between emin and emax (and some extra associated fainter pixels too)"""
#
if seed is None:
seed = int(afwMath.makeStatistics(image, afwMath.MAX).getValue())
if seed == 0:
seed = 1
width = image.getWidth()
height = image.getHeight()
rand = afwMath.Random(afwMath.Random.RANLUX, seed)
for i in range(nCR):
#
# Initial point in CR
#
x = rand.uniformInt(width)
y = rand.uniformInt(height)
amp = emin + rand.uniformInt(emax - emin)
#
# Extra contamination at about the initial amplitude
#
badPixels = []
while True:
badPixels.append([x, y, amp + 0.1*(emax - emin)*rand.uniform()])
if rand.uniform() > 0.5:
break
x += rand.uniformInt(3) - 1
y += rand.uniformInt(3) - 1
#
# Add a little extra CR flux to the pixels surrounding badPixels
#
for x, y, amp in badPixels:
while rand.uniform() < 0.5:
image.set(x, y, amp*rand.uniform())
x += rand.uniformInt(3) - 1
y += rand.uniformInt(3) - 1
#
# And set the initial badPixels themselves
#
for x, y, amp in badPixels:
if x >= 0 and x < width and y >= 0 and y < height:
image.set(x, y, amp)
def setUp(self):
self.min, self.range, self.Q = 0, 5, 20 # asinh
width, height = 85, 75
self.images = []
self.images.append(afwImage.ImageF(lsst.geom.ExtentI(width, height)))
self.images.append(afwImage.ImageF(lsst.geom.ExtentI(width, height)))
self.images.append(afwImage.ImageF(lsst.geom.ExtentI(width, height)))
for (x, y, A, g_r, r_i) in [
(15, 15, 1000, 1.0, 2.0),
(50, 45, 5500, -1.0, -0.5),
(30, 30, 600, 1.0, 2.5),
(45, 15, 20000, 1.0, 1.0),
]:
for i in range(len(self.images)):
if i == B:
amp = A
elif i == G:
amp = A * math.pow(10, 0.4 * g_r)
elif i == R:
amp = A * math.pow(10, 0.4 * r_i)
self.images[i][x, y, afwImage.LOCAL] = amp
psf = afwMath.AnalyticKernel(15, 15,
afwMath.GaussianFunction2D(2.5, 1.5, 0.5))
convolvedImage = type(self.images[0])(self.images[0].getDimensions())
randomImage = type(self.images[0])(self.images[0].getDimensions())
rand = afwMath.Random("MT19937", 666)
convolutionControl = afwMath.ConvolutionControl()
convolutionControl.setDoNormalize(True)
convolutionControl.setDoCopyEdge(True)
for i in range(len(self.images)):
afwMath.convolve(convolvedImage, self.images[i], psf,
convolutionControl)
afwMath.randomGaussianImage(randomImage, rand)
randomImage *= 2
convolvedImage += randomImage
self.images[i][:] = convolvedImage
del convolvedImage
del randomImage
def addCosmicRays(image, nCR=100, emin=100, emax=1000):
seed = int(afwMath.makeStatistics(image, afwMath.MAX).getValue())
if seed == 0:
seed = 1
width = image.getWidth()
height = image.getHeight()
rand = afwMath.Random(afwMath.Random.RANLUX, seed)
for i in range(nCR):
#
# Initial point in CR
#
x = rand.uniformInt(width)
y = rand.uniformInt(height)
amp = emin + rand.uniformInt(emax - emin)
badPixels = []
while True:
badPixels.append([x, y, amp + 0.1*(emax - emin)*rand.uniform()])
if rand.uniform() > 0.5:
break
x += rand.uniformInt(3) - 1
y += rand.uniformInt(3) - 1
for x, y, amp in badPixels:
if x >= 0 and x < width and y >= 0 and y < height:
image.set(x, y, image.get(x,y)+amp)
while rand.uniform() < 0.1:
image.set(x, y, image.get(x,y)+(emax - emin)*rand.uniform())
x += rand.uniformInt(3) - 1
y += rand.uniformInt(3) - 1
for x, y, amp in badPixels:
if x >= 0 and x < width and y >= 0 and y < height:
image.set(x, y, image.get(x,y)+amp)
def __init__(self, **kwargs):
FakeSourcesTask.__init__(self, **kwargs)
print "RNG seed:", self.config.seed
self.rng = afwMath.Random(self.config.seed)
self.npRand = np.random.RandomState(self.config.seed)
self.starData = fits.open(self.config.starList)[1].data
def setUp(self):
self.schema = afwTable.SourceTable.makeMinimalSchema()
config = measBase.SingleFrameMeasurementConfig()
config.algorithms.names = [
"base_PixelFlags",
"base_SdssCentroid",
"base_GaussianFlux",
"base_SdssShape",
"base_CircularApertureFlux",
"base_PsfFlux",
]
config.algorithms["base_CircularApertureFlux"].radii = [3.0]
config.slots.centroid = "base_SdssCentroid"
config.slots.psfFlux = "base_PsfFlux"
config.slots.apFlux = "base_CircularApertureFlux_3_0"
config.slots.modelFlux = None
config.slots.instFlux = None
config.slots.calibFlux = None
config.slots.shape = "base_SdssShape"
self.measureTask = measBase.SingleFrameMeasurementTask(self.schema,
config=config)
width, height = 110, 301
self.mi = afwImage.MaskedImageF(afwGeom.ExtentI(width, height))
self.mi.set(0)
sd = 3 # standard deviation of image
self.mi.getVariance().set(sd * sd)
self.mi.getMask().addMaskPlane("DETECTED")
self.FWHM = 5
self.ksize = 31 # size of desired kernel
sigma1 = 1.75
sigma2 = 2 * sigma1
self.exposure = afwImage.makeExposure(self.mi)
self.exposure.setPsf(
measAlg.DoubleGaussianPsf(self.ksize, self.ksize, 1.5 * sigma1, 1,
0.1))
self.exposure.setDetector(DetectorWrapper().detector)
#
# Make a kernel with the exactly correct basis functions. Useful for debugging
#
basisKernelList = []
for sigma in (sigma1, sigma2):
basisKernel = afwMath.AnalyticKernel(
self.ksize, self.ksize,
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 # 1 => up to linear
spFunc = afwMath.PolynomialFunction2D(order)
exactKernel = afwMath.LinearCombinationKernel(basisKernelList, spFunc)
exactKernel.setSpatialParameters([[1.0, 0, 0],
[0.0, 0.5 * 1e-2, 0.2e-2]])
self.exactPsf = measAlg.PcaPsf(exactKernel)
rand = afwMath.Random() # make these tests repeatable by setting seed
addNoise = True
if addNoise:
im = self.mi.getImage()
afwMath.randomGaussianImage(im, rand) # N(0, 1)
im *= sd # N(0, sd^2)
del im
xarr, yarr = [], []
for x, y in [
(20, 20),
(60, 20),
(30, 35),
(50, 50),
(20, 90),
(70, 160),
(25, 265),
(75, 275),
(85, 30),
(50, 120),
(70, 80),
(60, 210),
(20, 210),
]:
xarr.append(x)
yarr.append(y)
for x, y in zip(xarr, yarr):
dx = rand.uniform() - 0.5 # random (centered) offsets
dy = rand.uniform() - 0.5
k = exactKernel.getSpatialFunction(1)(
x, y) # functional variation of Kernel ...
b = (k * sigma1**2 / ((1 - k) * sigma2**2)
) # ... converted double Gaussian's "b"
# flux = 80000 - 20*x - 10*(y/float(height))**2
flux = 80000 * (1 + 0.1 * (rand.uniform() - 0.5))
I0 = flux * (1 + b) / (2 * np.pi * (sigma1**2 + b * sigma2**2))
for iy in range(y - self.ksize // 2, y + self.ksize // 2 + 1):
if iy < 0 or iy >= self.mi.getHeight():
continue
for ix in range(x - self.ksize // 2, x + self.ksize // 2 + 1):
if ix < 0 or ix >= self.mi.getWidth():
continue
intensity = I0 * psfVal(ix, iy, x + dx, y + dy, sigma1,
sigma2, b)
Isample = rand.poisson(
intensity) if addNoise else intensity
self.mi.getImage().set(
ix, iy,
self.mi.getImage().get(ix, iy) + Isample)
self.mi.getVariance().set(
ix, iy,
self.mi.getVariance().get(ix, iy) + intensity)
#
bbox = afwGeom.BoxI(afwGeom.PointI(0, 0),
afwGeom.ExtentI(width, height))
self.cellSet = afwMath.SpatialCellSet(bbox, 100)
self.footprintSet = afwDetection.FootprintSet(
self.mi, afwDetection.Threshold(100), "DETECTED")
self.catalog = self.measure(self.footprintSet, self.exposure)
for source in self.catalog:
try:
cand = measAlg.makePsfCandidate(source, self.exposure)
self.cellSet.insertCandidate(cand)
except Exception as e:
print(e)
continue
import lsst.afw.image as afwImage
import lsst.afw.geom as afwGeom
import lsst.afw.math as afwMath
import lsst.ip.diffim as ipDiffim
import numpy as num
import lsst.pex.logging as pexLogging
import lsst.pex.config as pexConfig
import lsst.afw.display.ds9 as ds9
verbosity = 5
pexLogging.Trace_setVerbosity("lsst.ip.diffim", verbosity)
imSize = 2**7
kSize = 2**5 + 1
rdm = afwMath.Random(afwMath.Random.MT19937, 10101)
scaling = 10000
doNorm = True
#gScale = 1.0 # FFT works!
gScale = 5.0 # FFT fails!?
doAddNoise = False
writeFits = False
def makeTest1(doAddNoise):
gaussian1 = afwMath.GaussianFunction2D(1. * gScale, 1. * gScale, 0.)
kernel1 = afwMath.AnalyticKernel(imSize, imSize, gaussian1)
image1 = afwImage.ImageD(kernel1.getDimensions())
kernel1.computeImage(image1, doNorm)
image1 *= scaling # total counts = scaling
image1 = image1.convertF()
mask1 = afwImage.MaskU(kernel1.getDimensions())
def showPsfCandidates(exposure, psfCellSet, psf=None, display=None, normalize=True, showBadCandidates=True,
fitBasisComponents=False, variance=None, chi=None):
"""Display the PSF candidates.
If psf is provided include PSF model and residuals; if normalize is true normalize the PSFs
(and residuals)
If chi is True, generate a plot of residuals/sqrt(variance), i.e. chi
If fitBasisComponents is true, also find the best linear combination of the PSF's components
(if they exist)
"""
if not display:
display = afwDisplay.Display()
if chi is None:
if variance is not None: # old name for chi
chi = variance
#
# Show us the ccandidates
#
mos = displayUtils.Mosaic()
#
candidateCenters = []
candidateCentersBad = []
candidateIndex = 0
for cell in psfCellSet.getCellList():
for cand in cell.begin(False): # include bad candidates
rchi2 = cand.getChi2()
if rchi2 > 1e100:
rchi2 = numpy.nan
if not showBadCandidates and cand.isBad():
continue
if psf:
im_resid = displayUtils.Mosaic(gutter=0, background=-5, mode="x")
try:
im = cand.getMaskedImage() # copy of this object's image
xc, yc = cand.getXCenter(), cand.getYCenter()
margin = 0 if True else 5
w, h = im.getDimensions()
bbox = lsst.geom.BoxI(lsst.geom.PointI(margin, margin), im.getDimensions())
if margin > 0:
bim = im.Factory(w + 2*margin, h + 2*margin)
stdev = numpy.sqrt(afwMath.makeStatistics(im.getVariance(), afwMath.MEAN).getValue())
afwMath.randomGaussianImage(bim.getImage(), afwMath.Random())
bim.getVariance().set(stdev**2)
bim.assign(im, bbox)
im = bim
xc += margin
yc += margin
im = im.Factory(im, True)
im.setXY0(cand.getMaskedImage().getXY0())
except Exception:
continue
if not variance:
im_resid.append(im.Factory(im, True))
if True: # tweak up centroids
mi = im
psfIm = mi.getImage()
config = measBase.SingleFrameMeasurementTask.ConfigClass()
config.slots.centroid = "base_SdssCentroid"
schema = afwTable.SourceTable.makeMinimalSchema()
measureSources = measBase.SingleFrameMeasurementTask(schema, config=config)
catalog = afwTable.SourceCatalog(schema)
extra = 10 # enough margin to run the sdss centroider
miBig = mi.Factory(im.getWidth() + 2*extra, im.getHeight() + 2*extra)
miBig[extra:-extra, extra:-extra, afwImage.LOCAL] = mi
miBig.setXY0(mi.getX0() - extra, mi.getY0() - extra)
mi = miBig
del miBig
exp = afwImage.makeExposure(mi)
exp.setPsf(psf)
footprintSet = afwDet.FootprintSet(mi,
afwDet.Threshold(0.5*numpy.max(psfIm.getArray())),
"DETECTED")
footprintSet.makeSources(catalog)
if len(catalog) == 0:
raise RuntimeError("Failed to detect any objects")
measureSources.run(catalog, exp)
if len(catalog) == 1:
source = catalog[0]
else: # more than one source; find the once closest to (xc, yc)
dmin = None # an invalid value to catch logic errors
for i, s in enumerate(catalog):
d = numpy.hypot(xc - s.getX(), yc - s.getY())
if i == 0 or d < dmin:
source, dmin = s, d
xc, yc = source.getCentroid()
# residuals using spatial model
try:
subtractPsf(psf, im, xc, yc)
except Exception:
continue
resid = im
if variance:
resid = resid.getImage()
var = im.getVariance()
var = var.Factory(var, True)
numpy.sqrt(var.getArray(), var.getArray()) # inplace sqrt
resid /= var
im_resid.append(resid)
# Fit the PSF components directly to the data (i.e. ignoring the spatial model)
if fitBasisComponents:
im = cand.getMaskedImage()
im = im.Factory(im, True)
im.setXY0(cand.getMaskedImage().getXY0())
try:
noSpatialKernel = psf.getKernel()
except Exception:
noSpatialKernel = None
if noSpatialKernel:
candCenter = lsst.geom.PointD(cand.getXCenter(), cand.getYCenter())
fit = fitKernelParamsToImage(noSpatialKernel, im, candCenter)
params = fit[0]
kernels = afwMath.KernelList(fit[1])
outputKernel = afwMath.LinearCombinationKernel(kernels, params)
outImage = afwImage.ImageD(outputKernel.getDimensions())
outputKernel.computeImage(outImage, False)
im -= outImage.convertF()
resid = im
if margin > 0:
bim = im.Factory(w + 2*margin, h + 2*margin)
afwMath.randomGaussianImage(bim.getImage(), afwMath.Random())
bim *= stdev
bim.assign(resid, bbox)
resid = bim
if variance:
resid = resid.getImage()
resid /= var
im_resid.append(resid)
im = im_resid.makeMosaic()
else:
im = cand.getMaskedImage()
if normalize:
im /= afwMath.makeStatistics(im, afwMath.MAX).getValue()
objId = splitId(cand.getSource().getId(), True)["objId"]
if psf:
lab = "%d chi^2 %.1f" % (objId, rchi2)
ctype = afwDisplay.RED if cand.isBad() else afwDisplay.GREEN
else:
lab = "%d flux %8.3g" % (objId, cand.getSource().getPsfInstFlux())
ctype = afwDisplay.GREEN
mos.append(im, lab, ctype)
if False and numpy.isnan(rchi2):
display.mtv(cand.getMaskedImage().getImage(), title="showPsfCandidates: candidate")
print("amp", cand.getAmplitude())
im = cand.getMaskedImage()
center = (candidateIndex, xc - im.getX0(), yc - im.getY0())
candidateIndex += 1
if cand.isBad():
candidateCentersBad.append(center)
else:
candidateCenters.append(center)
if variance:
title = "chi(Psf fit)"
else:
title = "Stars & residuals"
mosaicImage = mos.makeMosaic(display=display, title=title)
with display.Buffering():
for centers, color in ((candidateCenters, afwDisplay.GREEN), (candidateCentersBad, afwDisplay.RED)):
for cen in centers:
bbox = mos.getBBox(cen[0])
display.dot("+", cen[1] + bbox.getMinX(), cen[2] + bbox.getMinY(), ctype=color)
return mosaicImage
def setUp(self):
config = SingleFrameMeasurementTask.ConfigClass()
config.slots.apFlux = 'base_CircularApertureFlux_12_0'
self.schema = afwTable.SourceTable.makeMinimalSchema()
self.measureSources = SingleFrameMeasurementTask(self.schema,
config=config)
width, height = 110, 301
self.mi = afwImage.MaskedImageF(geom.ExtentI(width, height))
self.mi.set(0)
sd = 3 # standard deviation of image
self.mi.getVariance().set(sd * sd)
self.mi.getMask().addMaskPlane("DETECTED")
self.ksize = 31 # size of desired kernel
sigma1 = 1.75
sigma2 = 2 * sigma1
self.exposure = afwImage.makeExposure(self.mi)
self.exposure.setPsf(
measAlg.DoubleGaussianPsf(self.ksize, self.ksize, 1.5 * sigma1, 1,
0.1))
cdMatrix = np.array([1.0, 0.0, 0.0, 1.0])
cdMatrix.shape = (2, 2)
wcs = afwGeom.makeSkyWcs(crpix=geom.PointD(0, 0),
crval=geom.SpherePoint(
0.0, 0.0, geom.degrees),
cdMatrix=cdMatrix)
self.exposure.setWcs(wcs)
#
# Make a kernel with the exactly correct basis functions.
# Useful for debugging
#
basisKernelList = []
for sigma in (sigma1, sigma2):
basisKernel = afwMath.AnalyticKernel(
self.ksize, self.ksize,
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 # 1 => up to linear
spFunc = afwMath.PolynomialFunction2D(order)
exactKernel = afwMath.LinearCombinationKernel(basisKernelList, spFunc)
exactKernel.setSpatialParameters([[1.0, 0, 0],
[0.0, 0.5 * 1e-2, 0.2e-2]])
rand = afwMath.Random() # make these tests repeatable by setting seed
addNoise = True
if addNoise:
im = self.mi.getImage()
afwMath.randomGaussianImage(im, rand) # N(0, 1)
im *= sd # N(0, sd^2)
del im
xarr, yarr = [], []
for x, y in [
(20, 20),
(60, 20),
(30, 35),
(50, 50),
(20, 90),
(70, 160),
(25, 265),
(75, 275),
(85, 30),
(50, 120),
(70, 80),
(60, 210),
(20, 210),
]:
xarr.append(x)
yarr.append(y)
for x, y in zip(xarr, yarr):
dx = rand.uniform() - 0.5 # random (centered) offsets
dy = rand.uniform() - 0.5
k = exactKernel.getSpatialFunction(1)(
x, y) # functional variation of Kernel ...
b = (k * sigma1**2 / ((1 - k) * sigma2**2)
) # ... converted double Gaussian's "b"
# flux = 80000 - 20*x - 10*(y/float(height))**2
flux = 80000 * (1 + 0.1 * (rand.uniform() - 0.5))
I0 = flux * (1 + b) / (2 * np.pi * (sigma1**2 + b * sigma2**2))
for iy in range(y - self.ksize // 2, y + self.ksize // 2 + 1):
if iy < 0 or iy >= self.mi.getHeight():
continue
for ix in range(x - self.ksize // 2, x + self.ksize // 2 + 1):
if ix < 0 or ix >= self.mi.getWidth():
continue
II = I0 * psfVal(ix, iy, x + dx, y + dy, sigma1, sigma2, b)
Isample = rand.poisson(II) if addNoise else II
self.mi.image[ix, iy, afwImage.LOCAL] += Isample
self.mi.variance[ix, iy, afwImage.LOCAL] += II
bbox = geom.BoxI(geom.PointI(0, 0), geom.ExtentI(width, height))
self.cellSet = afwMath.SpatialCellSet(bbox, 100)
self.footprintSet = afwDetection.FootprintSet(
self.mi, afwDetection.Threshold(100), "DETECTED")
self.catalog = self.measure(self.footprintSet, self.exposure)
for source in self.catalog:
try:
cand = measAlg.makePsfCandidate(source, self.exposure)
self.cellSet.insertCandidate(cand)
except Exception as e:
print(e)
continue
def setUp(self):
width, height = 110, 301
self.mi = afwImage.MaskedImageF(afwGeom.ExtentI(width, height))
self.mi.set(0)
sd = 3 # standard deviation of image
self.mi.getVariance().set(sd*sd)
self.mi.getMask().addMaskPlane("DETECTED")
self.FWHM = 5
self.ksize = 31 # size of desired kernel
sigma1 = 1.75
sigma2 = 2*sigma1
self.exposure = afwImage.makeExposure(self.mi)
self.exposure.setPsf(measAlg.DoubleGaussianPsf(self.ksize, self.ksize,
1.5*sigma1, 1, 0.1))
crval = afwCoord.makeCoord(afwCoord.ICRS, 0.0*afwGeom.degrees, 0.0*afwGeom.degrees)
wcs = afwImage.makeWcs(crval, afwGeom.PointD(0, 0), 1.0, 0, 0, 1.0)
self.exposure.setWcs(wcs)
ccd = cameraGeom.Ccd(cameraGeom.Id(1))
ccd.addAmp(cameraGeom.Amp(cameraGeom.Id(0),
afwGeom.BoxI(afwGeom.PointI(0,0), self.exposure.getDimensions()),
afwGeom.BoxI(afwGeom.PointI(0,0), afwGeom.ExtentI(0,0)),
afwGeom.BoxI(afwGeom.PointI(0,0), self.exposure.getDimensions()),
cameraGeom.ElectronicParams(1.0, 100.0, 65535)))
self.exposure.setDetector(ccd)
self.exposure.getDetector().setDistortion(None)
#
# Make a kernel with the exactly correct basis functions. Useful for debugging
#
basisKernelList = afwMath.KernelList()
for sigma in (sigma1, sigma2):
basisKernel = afwMath.AnalyticKernel(self.ksize, self.ksize,
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 # 1 => up to linear
spFunc = afwMath.PolynomialFunction2D(order)
exactKernel = afwMath.LinearCombinationKernel(basisKernelList, spFunc)
exactKernel.setSpatialParameters([[1.0, 0, 0],
[0.0, 0.5*1e-2, 0.2e-2]])
rand = afwMath.Random() # make these tests repeatable by setting seed
addNoise = True
if addNoise:
im = self.mi.getImage()
afwMath.randomGaussianImage(im, rand) # N(0, 1)
im *= sd # N(0, sd^2)
del im
xarr, yarr = [], []
for x, y in [(20, 20), (60, 20),
(30, 35),
(50, 50),
(20, 90), (70, 160), (25, 265), (75, 275), (85, 30),
(50, 120), (70, 80),
(60, 210), (20, 210),
]:
xarr.append(x)
yarr.append(y)
for x, y in zip(xarr, yarr):
dx = rand.uniform() - 0.5 # random (centered) offsets
dy = rand.uniform() - 0.5
k = exactKernel.getSpatialFunction(1)(x, y) # functional variation of Kernel ...
b = (k*sigma1**2/((1 - k)*sigma2**2)) # ... converted double Gaussian's "b"
#flux = 80000 - 20*x - 10*(y/float(height))**2
flux = 80000*(1 + 0.1*(rand.uniform() - 0.5))
I0 = flux*(1 + b)/(2*np.pi*(sigma1**2 + b*sigma2**2))
for iy in range(y - self.ksize//2, y + self.ksize//2 + 1):
if iy < 0 or iy >= self.mi.getHeight():
continue
for ix in range(x - self.ksize//2, x + self.ksize//2 + 1):
if ix < 0 or ix >= self.mi.getWidth():
continue
I = I0*psfVal(ix, iy, x + dx, y + dy, sigma1, sigma2, b)
Isample = rand.poisson(I) if addNoise else I
self.mi.getImage().set(ix, iy, self.mi.getImage().get(ix, iy) + Isample)
self.mi.getVariance().set(ix, iy, self.mi.getVariance().get(ix, iy) + I)
#
bbox = afwGeom.BoxI(afwGeom.PointI(0,0), afwGeom.ExtentI(width, height))
self.cellSet = afwMath.SpatialCellSet(bbox, 100)
self.footprintSet = afwDetection.FootprintSet(self.mi, afwDetection.Threshold(100), "DETECTED")
self.catalog = SpatialModelPsfTestCase.measure(self.footprintSet, self.exposure)
for source in self.catalog:
try:
cand = measAlg.makePsfCandidate(source, self.exposure)
self.cellSet.insertCandidate(cand)
except Exception, e:
print e
continue
def __init__(self, rand=None):
if rand is None:
rand = afwMath.Random()
self.rand = rand
def getNoiseGenerator(self,
exposure,
noiseImage,
noiseMeanVar,
exposureId=None):
"""Return a generator of artificial noise.
Returns
-------
noiseGenerator : `lsst.afw.image.noiseReplacer.NoiseGenerator`
"""
if noiseImage is not None:
return ImageNoiseGenerator(noiseImage)
rand = None
if self.noiseSeedMultiplier:
# default plugin, our seed
if exposureId is not None and exposureId != 0:
seed = exposureId * self.noiseSeedMultiplier
else:
seed = self.noiseSeedMultiplier
rand = afwMath.Random(afwMath.Random.MT19937, seed)
if noiseMeanVar is not None:
try:
# Assume noiseMeanVar is an iterable of floats
noiseMean, noiseVar = noiseMeanVar
noiseMean = float(noiseMean)
noiseVar = float(noiseVar)
noiseStd = math.sqrt(noiseVar)
if self.log:
self.log.debug(
'Using passed-in noise mean = %g, variance = %g -> stdev %g',
noiseMean, noiseVar, noiseStd)
return FixedGaussianNoiseGenerator(noiseMean,
noiseStd,
rand=rand)
except Exception:
if self.log:
self.log.debug(
'Failed to cast passed-in noiseMeanVar to floats: %s',
str(noiseMeanVar))
offset = self.noiseOffset
noiseSource = self.noiseSource
if noiseSource == 'meta':
# check the exposure metadata
meta = exposure.getMetadata()
# this key name correspond to SubtractBackgroundTask() in meas_algorithms
try:
bgMean = meta.getAsDouble('BGMEAN')
# We would have to adjust for GAIN if ip_isr didn't make it 1.0
noiseStd = math.sqrt(bgMean)
if self.log:
self.log.debug(
'Using noise variance = (BGMEAN = %g) from exposure metadata',
bgMean)
return FixedGaussianNoiseGenerator(offset, noiseStd, rand=rand)
except Exception:
if self.log:
self.log.debug(
'Failed to get BGMEAN from exposure metadata')
if noiseSource == 'variance':
if self.log:
self.log.debug(
'Will draw noise according to the variance plane.')
var = exposure.getMaskedImage().getVariance()
return VariancePlaneNoiseGenerator(var, mean=offset, rand=rand)
if noiseSource == 'variance_median':
if self.log:
self.log.debug(
'Will draw noise using the median of the variance plane.')
var = exposure.getMaskedImage().getVariance()
s = afwMath.makeStatistics(var, afwMath.MEDIAN)
varMedian = s.getValue(afwMath.MEDIAN)
if self.log:
self.log.debug("Measured from variance: median variance = %g",
varMedian)
return FixedGaussianNoiseGenerator(offset,
math.sqrt(varMedian),
rand=rand)
# Compute an image-wide clipped variance.
im = exposure.getMaskedImage().getImage()
s = afwMath.makeStatistics(im, afwMath.MEANCLIP | afwMath.STDEVCLIP)
noiseMean = s.getValue(afwMath.MEANCLIP)
noiseStd = s.getValue(afwMath.STDEVCLIP)
if self.log:
self.log.debug(
"Measured from image: clipped mean = %g, stdev = %g",
noiseMean, noiseStd)
return FixedGaussianNoiseGenerator(noiseMean + offset,
noiseStd,
rand=rand)
def test2(self):
# Check that doReplaceWithNoise works with deblended source
# hierarchies.
seed = 42
rand = afwMath.Random(afwMath.Random.MT19937, seed)
psf = self.getpsf()
im = afwImage.ImageF(200, 50)
skystd = 100
afwMath.randomGaussianImage(im, rand)
im *= skystd
imorig = afwImage.ImageF(im, True)
noiseim = imorig
mi = afwImage.MaskedImageF(im)
mi.getVariance().set(skystd**2)
exposure = afwImage.makeExposure(mi)
exposure.setPsf(psf)
detconf = measAlg.SourceDetectionConfig()
detconf.returnOriginalFootprints = True
detconf.reEstimateBackground = False
measconf = measAlg.SourceMeasurementConfig()
measconf.doReplaceWithNoise = True
measconf.replaceWithNoise.noiseSeed = 42
schema = afwTable.SourceTable.makeMinimalSchema()
detect = measAlg.SourceDetectionTask(config=detconf, schema=schema)
measure = MySourceMeasurementTask(config=measconf,
schema=schema,
doplot=plots)
table = afwTable.SourceTable.make(schema)
table.preallocate(10)
# We're going to fake up a perfect deblend hierarchy here, by
# creating individual images containing single sources and
# measuring them, and then creating a deblend hierarchy where
# the children have the correct HeavyFootprints. We want to
# find that the measurements on the deblend hierarchy and the
# blended image are equal to the individual images.
#
# Note that in the normal setup we don't expect the
# measurements to be *identical* because of the faint wings of
# the objects; when measuring a deblended child, we pick up
# the wings of the other objects.
#
# In order to get exactly equal measurements, we'll fake some
# sources that have no wings -- we'll copy just the source
# pixels within the footprint. This means that all the
# footprints are the same, and the pixels inside the footprint
# are the same.
fullim = None
sources = None
# "normal" measurements
xx0, yy0, vx0, vy0 = [], [], [], []
# "no-wing" measurements
xx1, yy1, vx1, vy1 = [], [], [], []
y = 25
for i in range(5):
# no-noise source image
sim = afwImage.ImageF(imorig.getWidth(), imorig.getHeight())
# Put all four sources in the parent (i==0), and one
# source in each child (i=[1 to 4])
if i in [0, 1]:
addPsf(sim, psf, 20, y, 1000)
if i in [0, 2]:
addGaussian(sim, 40, y, 10, 3, 2e5)
if i in [0, 3]:
addGaussian(sim, 75, y, 10, 3, 2e5)
if i in [0, 4]:
addPsf(sim, psf, 95, y, 1000)
imcopy = afwImage.ImageF(imorig, True)
imcopy += sim
# copy the pixels into the exposure object
im <<= imcopy
if i == 0:
detected = detect.makeSourceCatalog(table, exposure)
sources = detected.sources
print 'detected', len(sources), 'sources'
self.assertEqual(len(sources), 1)
else:
fpSets = detect.detectFootprints(exposure)
print 'detected', fpSets.numPos, 'sources'
fpSets.positive.makeSources(sources)
self.assertEqual(fpSets.numPos, 1)
print len(sources), 'sources total'
measure.plotpat = 'single-%i.png' % i
measure.run(exposure, sources[-1:])
s = sources[-1]
fp = s.getFootprint()
if i == 0:
# This is the blended image
fullim = imcopy
else:
print 'Creating heavy footprint...'
heavy = afwDet.makeHeavyFootprint(fp, mi)
s.setFootprint(heavy)
# Record the single-source measurements.
xx0.append(s.getX())
yy0.append(s.getY())
vx0.append(s.getIxx())
vy0.append(s.getIyy())
# "no-wings": add just the source pixels within the footprint
im <<= sim
h = afwDet.makeHeavyFootprint(fp, mi)
sim2 = afwImage.ImageF(imorig.getWidth(), imorig.getHeight())
h.insert(sim2)
imcopy = afwImage.ImageF(imorig, True)
imcopy += sim2
im <<= imcopy
measure.plotpat = 'single2-%i.png' % i
measure.run(exposure, sources[i:i + 1], noiseImage=noiseim)
s = sources[i]
xx1.append(s.getX())
yy1.append(s.getY())
vx1.append(s.getIxx())
vy1.append(s.getIyy())
if i == 0:
fullim2 = imcopy
# Now we'll build the fake deblended hierarchy.
parent = sources[0]
kids = sources[1:]
# Ensure that the parent footprint contains all the child footprints
pfp = parent.getFootprint()
for s in kids:
for span in s.getFootprint().getSpans():
pfp.addSpan(span)
pfp.normalize()
#parent.setFootprint(pfp)
# The parent-child relationship is established through the IDs
parentid = parent.getId()
for s in kids:
s.setParent(parentid)
# Reset all the measurements
shkey = sources.getTable().getShapeKey()
ckey = sources.getTable().getCentroidKey()
for s in sources:
sh = s.get(shkey)
sh.setIxx(np.nan)
sh.setIyy(np.nan)
sh.setIxy(np.nan)
s.set(shkey, sh)
c = s.get(ckey)
c.setX(np.nan)
c.setY(np.nan)
s.set(ckey, c)
# Measure the "deblended" normal sources
im <<= fullim
measure.plotpat = 'joint-%(sourcenum)i.png'
measure.run(exposure, sources)
xx2, yy2, vx2, vy2 = [], [], [], []
for s in sources:
xx2.append(s.getX())
yy2.append(s.getY())
vx2.append(s.getIxx())
vy2.append(s.getIyy())
# Measure the "deblended" no-wings sources
im <<= fullim2
measure.plotpat = 'joint2-%(sourcenum)i.png'
measure.run(exposure, sources, noiseImage=noiseim)
xx3, yy3, vx3, vy3 = [], [], [], []
for s in sources:
xx3.append(s.getX())
yy3.append(s.getY())
vx3.append(s.getIxx())
vy3.append(s.getIyy())
print 'Normal:'
print 'xx ', xx0
print ' vs', xx2
print 'yy ', yy0
print ' vs', yy2
print 'vx ', vx0
print ' vs', vx2
print 'vy ', vy0
print ' vs', vy2
print 'No wings:'
print 'xx ', xx1
print ' vs', xx3
print 'yy ', yy1
print ' vs', yy3
print 'vx ', vx1
print ' vs', vx3
print 'vy ', vy1
print ' vs', vy3
# These "normal" tests are not very stringent.
# 0.1-pixel centroids
self.assertTrue(all([abs(v1 - v2) < 0.1 for v1, v2 in zip(xx0, xx2)]))
self.assertTrue(all([abs(v1 - v2) < 0.1 for v1, v2 in zip(yy0, yy2)]))
# 10% variances
self.assertTrue(
all([
abs(v1 - v2) / ((v1 + v2) / 2.) < 0.1
for v1, v2 in zip(vx0, vx2)
]))
self.assertTrue(
all([
abs(v1 - v2) / ((v1 + v2) / 2.) < 0.1
for v1, v2 in zip(vy0, vy2)
]))
# The "no-wings" tests should be exact.
self.assertTrue(xx1 == xx3)
self.assertTrue(yy1 == yy3)
self.assertTrue(vx1 == vx3)
self.assertTrue(vy1 == vy3)
# Reset sources
for s in sources:
sh = s.get(shkey)
sh.setIxx(np.nan)
sh.setIyy(np.nan)
sh.setIxy(np.nan)
s.set(shkey, sh)
c = s.get(ckey)
c.setX(np.nan)
c.setY(np.nan)
s.set(ckey, c)
# Test that the parent/child order is unimportant.
im <<= fullim2
measure.doplot = False
sources2 = sources.copy()
perm = [2, 1, 0, 3, 4]
for i, j in enumerate(perm):
sources2[i] = sources[j]
# I'm not convinced that HeavyFootprints get copied correctly...
sources2[i].setFootprint(sources[j].getFootprint())
measure.run(exposure, sources2, noiseImage=noiseim)
# "measure.run" reorders the sources!
xx3, yy3, vx3, vy3 = [], [], [], []
for s in sources:
xx3.append(s.getX())
yy3.append(s.getY())
vx3.append(s.getIxx())
vy3.append(s.getIyy())
self.assertTrue(xx1 == xx3)
self.assertTrue(yy1 == yy3)
self.assertTrue(vx1 == vx3)
self.assertTrue(vy1 == vy3)
# Reset sources
for s in sources:
sh = s.get(shkey)
sh.setIxx(np.nan)
sh.setIyy(np.nan)
sh.setIxy(np.nan)
s.set(shkey, sh)
c = s.get(ckey)
c.setX(np.nan)
c.setY(np.nan)
s.set(ckey, c)
# Test that it still works when the parent ID falls in the middle of
# the child IDs.
im <<= fullim2
measure.doplot = False
sources2 = sources.copy()
parentid = 3
ids = [parentid, 1, 2, 4, 5]
for i, s in enumerate(sources2):
s.setId(ids[i])
if i != 0:
s.setParent(parentid)
s.setFootprint(sources[i].getFootprint())
measure.run(exposure, sources2, noiseImage=noiseim)
# The sources get reordered!
xx3, yy3, vx3, vy3 = [], [], [], []
xx3, yy3, vx3, vy3 = [0] * 5, [0] * 5, [0] * 5, [0] * 5
for i, j in enumerate(ids):
xx3[i] = sources2[j - 1].getX()
yy3[i] = sources2[j - 1].getY()
vx3[i] = sources2[j - 1].getIxx()
vy3[i] = sources2[j - 1].getIyy()
self.assertTrue(xx1 == xx3)
self.assertTrue(yy1 == yy3)
self.assertTrue(vx1 == vx3)
self.assertTrue(vy1 == vy3)
def test1(self):
seed = 42
rand = afwMath.Random(afwMath.Random.MT19937, seed)
#for k in range(5):
self.runone(1, rand)
def setUp(self):
self.rand = afwMath.Random()
self.image = afwImage.ImageF(afwGeom.Extent2I(1000, 1000))