def testRejectBlends(self):
"""Test the PcaPsfDeterminer blend removal
We give it a single blended source, asking it to remove blends,
and check that it barfs in the expected way.
"""
factory = measAlg.psfDeterminerRegistry["pca"]
config = factory.ConfigClass()
config.doRejectBlends = True
psfDeterminer = factory(config)
schema = afwTable.SourceTable.makeMinimalSchema()
# Use The single frame measurement task to populate the schema with standard keys
sfm = measBase.SingleFrameMeasurementTask(schema)
catalog = afwTable.SourceCatalog(schema)
source = catalog.addNew()
# Make the source blended, with necessary information to calculate pca
foot = afwDetection.Footprint(afwGeom.Point2I(45, 123), 6, self.exposure.getBBox())
foot.addPeak(45, 123, 6)
foot.addPeak(47, 126, 5)
source.setFootprint(foot)
centerKey = afwTable.Point2DKey(source.schema['slot_Centroid'])
shapeKey = afwTable.QuadrupoleKey(schema['slot_Shape'])
source.set(centerKey, afwTable.Point2D(46, 124))
source.set(shapeKey, afwTable.Quadrupole(1.1, 2.2, 1))
candidates = [measAlg.makePsfCandidate(source, self.exposure)]
metadata = dafBase.PropertyList()
with self.assertRaises(RuntimeError) as cm:
psfDeterminer.determinePsf(self.exposure, candidates, metadata)
self.assertEqual(cm.exception.message, "All PSF candidates removed as blends")
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)
bbox = afwGeom.BoxI(afwGeom.PointI(0, 0),
afwGeom.ExtentI(self.width, self.height))
self.cellSet = afwMath.SpatialCellSet(bbox, 100)
self.footprintSet = afwDetection.FootprintSet(
self.mi, afwDetection.Threshold(self.detectThresh), "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 testRejectBlends(self):
"""Test the PcaPsfDeterminer blend removal
We give it a single blended source, asking it to remove blends,
and check that it barfs in the expected way.
"""
factory = measAlg.psfDeterminerRegistry["pca"]
config = factory.ConfigClass()
config.doRejectBlends = True
psfDeterminer = factory(config)
schema = afwTable.SourceTable.makeMinimalSchema()
schema.addField("position", afwGeom.Point2D, doc="Position")
schema.addField("flux.psf", float, doc="psf")
schema.addField("flux.psf.flags", "Flag", doc="psf")
catalog = afwTable.SourceCatalog(schema)
catalog.defineCentroid("position")
catalog.definePsfFlux("flux.psf")
source = catalog.addNew()
foot = afwDetection.Footprint(afwGeom.Point2I(123, 45), 6, self.exposure.getBBox())
foot.addPeak(123, 45, 6)
foot.addPeak(126, 47, 5)
source.setFootprint(foot)
candidates = [measAlg.makePsfCandidate(source, self.exposure)]
metadata = dafBase.PropertyList()
with self.assertRaises(RuntimeError) as cm:
psfDeterminer.determinePsf(self.exposure, candidates, metadata)
self.assertEqual(cm.exception.message, "All PSF candidates removed as blends")
def selectPsfSources(exposure, matches, psfPolicy):
"""Get a list of suitable stars to construct a PSF."""
import lsstDebug
display = lsstDebug.Info(__name__).display
displayExposure = lsstDebug.Info(__name__).displayExposure # display the Exposure + spatialCells
#
# Unpack policy
#
kernelSize = psfPolicy.get("kernelSize")
borderWidth = psfPolicy.get("borderWidth")
sizePsfCellX = psfPolicy.get("sizeCellX")
sizePsfCellY = psfPolicy.get("sizeCellY")
#
mi = exposure.getMaskedImage()
if display and displayExposure:
disp = afwDisplay.Display(frame=0)
disp.mtv(mi, title="PSF candidates")
psfCellSet = afwMath.SpatialCellSet(mi.getBBox(), sizePsfCellX, sizePsfCellY)
psfStars = []
for val in matches:
ref, source = val[0:2]
if not (ref.getFlagForDetection() & measAlg.Flags.STAR) or \
(source.getFlagForDetection() & measAlg.Flags.BAD):
continue
try:
cand = measAlg.makePsfCandidate(source, mi)
#
# The setXXX methods are class static, but it's convenient to call them on
# an instance as we don't know Exposure's pixel type (and hence cand's exact type)
if cand.getWidth() == 0:
cand.setBorderWidth(borderWidth)
cand.setWidth(kernelSize + 2*borderWidth)
cand.setHeight(kernelSize + 2*borderWidth)
im = cand.getMaskedImage().getImage()
max = afwMath.makeStatistics(im, afwMath.MAX).getValue()
if not np.isfinite(max):
continue
psfCellSet.insertCandidate(cand)
if display and displayExposure:
disp.dot("+", source.getXAstrom() - mi.getX0(), source.getYAstrom() - mi.getY0(),
size=4, ctype=afwDisplay.CYAN)
disp.dot("o", source.getXAstrom() - mi.getX0(), source.getYAstrom() - mi.getY0(),
size=4, ctype=afwDisplay.CYAN)
except Exception:
continue
source.setFlagForDetection(source.getFlagForDetection() | measAlg.Flags.STAR)
psfStars += [source]
return psfStars, psfCellSet
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 createCandidate(self, threshold=0.1):
"""Create a PSF candidate from self.exposure
@param threshold: Threshold for creating footprints on image
"""
source = createFakeSource(self.x, self.y, self.catalog, self.exposure,
threshold)
return measAlg.makePsfCandidate(source, self.exposure)
def createCandidate(self, threshold=0.1):
"""Create a PSF candidate from self.exposure.
Parameters
----------
threshold : `float`, optional
Threshold for creating footprints on image.
"""
source = createFakeSource(self.x, self.y, self.catalog, self.exposure, threshold)
return measAlg.makePsfCandidate(source, self.exposure)
def select(self, exposure, matches, psfPolicy):
"""Get a list of suitable stars to construct a PSF."""
import lsstDebug
display = lsstDebug.Info(__name__).display
displayExposure = lsstDebug.Info(__name__).displayExposure # display the Exposure + spatialCells
#
# Unpack policy
#
kernelSize = psfPolicy["kernelSizeMin"]
borderWidth = psfPolicy["borderWidth"]
#
mi = exposure.getMaskedImage()
if display and displayExposure:
frame = 0
ds9.mtv(mi, frame=frame, title="PSF candidates")
psfCandidates = []
for val in matches:
ref, source = val[0:2]
if not (ref.getFlagForDetection() & measAlg.Flags.STAR) or \
(source.getFlagForDetection() & measAlg.Flags.BAD):
continue
if source.getPsfFlux() <= 0.0:
continue
try:
cand = measAlg.makePsfCandidate(source, mi)
#
# The setXXX methods are class static, but it's convenient to call them on
# an instance as we don't know Exposure's pixel type (and hence cand's exact type)
if cand.getWidth() == 0:
cand.setBorderWidth(borderWidth)
cand.setWidth(kernelSize + 2*borderWidth)
cand.setHeight(kernelSize + 2*borderWidth)
im = cand.getImage().getImage()
max = afwMath.makeStatistics(im, afwMath.MAX).getValue()
if not numpy.isfinite(max):
continue
if display and displayExposure:
ds9.dot("+", source.getXAstrom() - mi.getX0(), source.getYAstrom() - mi.getY0(),
size=4, frame=frame, ctype=ds9.CYAN)
ds9.dot("o", source.getXAstrom() - mi.getX0(), source.getYAstrom() - mi.getY0(),
size=4, frame=frame, ctype=ds9.CYAN)
except Exception, e:
continue
source.setFlagForDetection(source.getFlagForDetection() | measAlg.Flags.STAR)
psfCandidates.append(cand)
def createCandidate(self, threshold=0.1):
"""Create a PSF candidate from self.exposure.
Parameters
----------
threshold : `float`, optional
Threshold for creating footprints on image.
"""
source = createFakeSource(self.x, self.y, self.catalog, self.exposure,
threshold)
return measAlg.makePsfCandidate(source, self.exposure)
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)
bbox = afwGeom.BoxI(afwGeom.PointI(0, 0), afwGeom.ExtentI(self.width, self.height))
self.cellSet = afwMath.SpatialCellSet(bbox, 100)
self.footprintSet = afwDetection.FootprintSet(self.mi, afwDetection.Threshold(self.detectThresh),
"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 testRejectBlends(self):
"""Test the PcaPsfDeterminerTask blend removal."""
"""
We give it a single blended source, asking it to remove blends,
and check that it barfs in the expected way.
"""
psfDeterminerClass = measAlg.psfDeterminerRegistry["pca"]
config = psfDeterminerClass.ConfigClass()
config.doRejectBlends = True
psfDeterminer = psfDeterminerClass(config=config)
schema = afwTable.SourceTable.makeMinimalSchema()
# Use The single frame measurement task to populate the schema with standard keys
measBase.SingleFrameMeasurementTask(schema)
catalog = afwTable.SourceCatalog(schema)
source = catalog.addNew()
# Make the source blended, with necessary information to calculate pca
spanShift = afwGeom.Point2I(54, 123)
spans = afwGeom.SpanSet.fromShape(6, offset=spanShift)
foot = afwDetection.Footprint(spans, self.exposure.getBBox())
foot.addPeak(45, 123, 6)
foot.addPeak(47, 126, 5)
source.setFootprint(foot)
centerKey = afwTable.Point2DKey(source.schema['slot_Centroid'])
shapeKey = afwTable.QuadrupoleKey(schema['slot_Shape'])
source.set(centerKey, afwGeom.Point2D(46, 124))
source.set(shapeKey, afwGeom.Quadrupole(1.1, 2.2, 1))
candidates = [measAlg.makePsfCandidate(source, self.exposure)]
metadata = dafBase.PropertyList()
with self.assertRaises(RuntimeError) as cm:
psfDeterminer.determinePsf(self.exposure, candidates, metadata)
self.assertEqual(str(cm.exception),
"All PSF candidates removed as blends")
def selectStars(self, exposure, catalog, matches=None):
"""Return a list of PSF candidates that represent likely stars
A list of PSF candidates may be used by a PSF fitter to construct a PSF.
@param[in] exposure: the exposure containing the sources
@param[in] catalog: a SourceCatalog containing sources that may be stars
@param[in] matches: astrometric matches; ignored by this star selector
@return psfCandidateList: a list of PSF candidates.
"""
import lsstDebug
display = lsstDebug.Info(__name__).display
displayExposure = display and \
lsstDebug.Info(__name__).displayExposure # display the Exposure + spatialCells
plotFwhmHistogram = display and plt and \
lsstDebug.Info(__name__).plotFwhmHistogram # Plot histogram of FWHM
plotFlags = display and plt and \
lsstDebug.Info(__name__).plotFlags # Plot the sources coloured by their flags
plotRejection = display and plt and \
lsstDebug.Info(__name__).plotRejection # Plot why sources are rejected
# create a log for my application
logger = pexLogging.Log(pexLogging.getDefaultLog(), "meas.extensions.psfex.psfexStarSelector")
#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
#
fluxName = self.config.fluxName
fluxErrName = self.config.fluxErrName
minFwhm = self.config.minFwhm
maxFwhm = self.config.maxFwhm
maxFwhmVariability = self.config.maxFwhmVariability
maxbad = self.config.maxbad
maxbadflag = self.config.maxbadflag
maxellip = self.config.maxellip
minsn = self.config.minsn
maxelong = (maxellip + 1.0)/(1.0 - maxellip) if maxellip < 1.0 else 100
# Unpack the catalogue
shape = catalog.getShapeDefinition()
ixx = catalog.get("%s.xx" % shape)
iyy = catalog.get("%s.yy" % shape)
fwhm = 2*np.sqrt(2*np.log(2))*np.sqrt(0.5*(ixx + iyy))
elong = 0.5*(ixx - iyy)/(ixx + iyy)
flux = catalog.get(fluxName)
fluxErr = catalog.get(fluxErrName)
sn = flux/np.where(fluxErr > 0, fluxErr, 1)
sn[fluxErr <= 0] = -psfex.psfex.cvar.BIG
flags = 0x0
for i, f in enumerate(self.config.badFlags):
flags = np.bitwise_or(flags, np.where(catalog.get(f), 1 << i, 0))
#
# Estimate the acceptable range of source widths
#
good = np.logical_and(sn > minsn, np.logical_not(flags))
good = np.logical_and(good, elong < maxelong)
good = np.logical_and(good, fwhm >= minFwhm)
good = np.logical_and(good, fwhm < maxFwhm)
fwhmMode, fwhmMin, fwhmMax = psfex.compute_fwhmrange(fwhm[good], maxFwhmVariability, minFwhm, maxFwhm,
plot=dict(fwhmHistogram=plotFwhmHistogram))
#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
#
# Here's select_candidates
#
#---- Apply some selection over flags, fluxes...
bad = (flags != 0)
#set.setBadFlags(int(sum(bad)))
if plotRejection:
selectionVectors = []
selectionVectors.append((bad, "flags %d" % sum(bad)))
dbad = sn < minsn
#set.setBadSN(int(sum(dbad)))
bad = np.logical_or(bad, dbad)
if plotRejection:
selectionVectors.append((dbad, "S/N %d" % sum(dbad)))
dbad = fwhm < fwhmMin
#set.setBadFrmin(int(sum(dbad)))
bad = np.logical_or(bad, dbad)
if plotRejection:
selectionVectors.append((dbad, "fwhmMin %d" % sum(dbad)))
dbad = fwhm > fwhmMax
#set.setBadFrmax(int(sum(dbad)))
bad = np.logical_or(bad, dbad)
if plotRejection:
selectionVectors.append((dbad, "fwhmMax %d" % sum(dbad)))
dbad = elong > maxelong
#set.setBadElong(int(sum(dbad)))
bad = np.logical_or(bad, dbad)
if plotRejection:
selectionVectors.append((dbad, "elong %d" % sum(dbad)))
#-- ... and check the integrity of the sample
if maxbadflag:
nbad = np.array([(v <= -psfex.psfex.cvar.BIG).sum() for v in vignet])
dbad = nbad > maxbad
#set.setBadPix(int(sum(dbad)))
bad = np.logical_or(bad, dbad)
if plotRejection:
selectionVectors.append((dbad, "badpix %d" % sum(dbad)))
good = np.logical_not(bad)
#
# We know enough to plot, if so requested
#
frame = 0
if displayExposure:
mi = exposure.getMaskedImage()
ds9.mtv(mi, frame=frame, title="PSF candidates")
with ds9.Buffering():
for i, source in enumerate(catalog):
if good[i]:
ctype = ds9.GREEN # star candidate
else:
ctype = ds9.RED # not star
ds9.dot("+", source.getX() - mi.getX0(), source.getY() - mi.getY0(),
frame=frame, ctype=ctype)
if plotFlags or plotRejection:
imag = -2.5*np.log10(flux)
plt.clf()
alpha = 0.5
if plotFlags:
isSet = np.where(flags == 0x0)[0]
plt.plot(imag[isSet], fwhm[isSet], 'o', alpha=alpha, label="good")
for i, f in enumerate(self.config.badFlags):
mask = 1 << i
isSet = np.where(np.bitwise_and(flags, mask))[0]
if isSet.any():
if np.isfinite(imag[isSet] + fwhm[isSet]).any():
label = re.sub(r"\_flag", "",
re.sub(r"^base\_", "",
re.sub(r"^.*base\_PixelFlags\_flag\_", "", f)))
plt.plot(imag[isSet], fwhm[isSet], 'o', alpha=alpha, label=label)
else:
for bad, label in selectionVectors:
plt.plot(imag[bad], fwhm[bad], 'o', alpha=alpha, label=label)
plt.plot(imag[good], fwhm[good], 'o', color="black", label="selected")
[plt.axhline(_, color='red') for _ in [fwhmMin, fwhmMax]]
plt.xlim(np.median(imag[good]) + 5*np.array([-1, 1]))
plt.ylim(fwhm[np.where(np.isfinite(fwhm + imag))].min(), 2*fwhmMax)
plt.legend(loc=2)
plt.xlabel("Instrumental %s Magnitude" % fluxName.split(".")[-1].title())
plt.ylabel("fwhm")
title = "PSFEX Star Selection"
plt.title("%s %d selected" % (title, sum(good)))
if displayExposure:
global eventHandler
eventHandler = EventHandler(plt.axes(), imag, fwhm, catalog.getX(), catalog.getY(), frames=[frame])
if plotFlags or plotRejection:
while True:
try:
reply = raw_input("continue? [y[es] h(elp) p(db) q(uit)] ").strip()
except EOFError:
reply = "y"
if not reply:
reply = "y"
if reply[0] == "h":
print """\
At this prompt, you can continue with almost any key; 'p' enters pdb,
'q' returns to the shell, and
'h' prints this text
""",
if displayExposure:
print """
If you put the cursor on a point in the matplotlib scatter plot and hit 'p' you'll see it in ds9."""
elif reply[0] == "p":
import pdb; pdb.set_trace()
elif reply[0] == 'q':
sys.exit(1)
else:
break
#
# Time to use that stellar classification to generate psfCandidateList
#
with ds9.Buffering():
psfCandidateList = []
if True:
catalog = [s for s,g in zip(catalog, good) if g]
else:
catalog = catalog[good]
for source in catalog:
try:
psfCandidate = measAlg.makePsfCandidate(source, exposure)
# The setXXX methods are class static, but it's convenient to call them on
# an instance as we don't know Exposure's pixel type
# (and hence psfCandidate's exact type)
if psfCandidate.getWidth() == 0:
psfCandidate.setBorderWidth(self.config.borderWidth)
psfCandidate.setWidth(self.config.kernelSize + 2*self.config.borderWidth)
psfCandidate.setHeight(self.config.kernelSize + 2*self.config.borderWidth)
im = psfCandidate.getMaskedImage().getImage()
vmax = afwMath.makeStatistics(im, afwMath.MAX).getValue()
if not np.isfinite(vmax):
continue
psfCandidateList.append(psfCandidate)
if display and displayExposure:
ds9.dot("o", source.getX() - mi.getX0(), source.getY() - mi.getY0(),
size=4, frame=frame, ctype=ds9.CYAN)
except Exception as err:
logger.logdebug("Failed to make a psfCandidate from source %d: %s" % (source.getId(), err))
return psfCandidateList
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 = 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 * 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))
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.gaussianFlux = 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(lsst.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.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.image[ix, iy, afwImage.LOCAL] += Isample
self.mi.variance[ix, iy, afwImage.LOCAL] += intensity
#
bbox = lsst.geom.BoxI(lsst.geom.PointI(0, 0), lsst.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 selectStars(self, exposure, sourceCat, matches=None):
"""!Return a list of PSF candidates that represent likely stars
A list of PSF candidates may be used by a PSF fitter to construct a PSF.
@param[in] exposure the exposure containing the sources
@param[in] sourceCat catalog of sources that may be stars (an lsst.afw.table.SourceCatalog)
@param[in] matches a match vector as produced by meas_astrom; required
(defaults to None to match the StarSelector API and improve error handling)
@return psfCandidateList: a list of PSF candidates.
"""
import lsstDebug
display = lsstDebug.Info(__name__).display
displayExposure = lsstDebug.Info(__name__).displayExposure # display the Exposure + spatialCells
pauseAtEnd = lsstDebug.Info(__name__).pauseAtEnd # pause when done
if matches is None:
raise RuntimeError("CatalogStarSelector requires matches")
mi = exposure.getMaskedImage()
if display:
frames = {}
if displayExposure:
frames["displayExposure"] = 1
ds9.mtv(mi, frame=frames["displayExposure"], title="PSF candidates")
#
# Read the reference catalogue
#
isGoodSource = CheckSource(sourceCat, self._fluxLim, self._fluxMax, self._badStarPixelFlags)
#
# Go through and find all the PSFs in the catalogue
#
# We'll split the image into a number of cells, each of which contributes only
# one PSF candidate star
#
psfCandidateList = []
with ds9.Buffering():
for ref, source, d in matches:
if not ref.get("resolved"):
if not isGoodSource(source):
symb, ctype = "+", ds9.RED
else:
try:
psfCandidate = measAlg.makePsfCandidate(source, exposure)
# The setXXX methods are class static, but it's convenient to call them on
# an instance as we don't know Exposure's pixel type
# (and hence psfCandidate's exact type)
if psfCandidate.getWidth() == 0:
psfCandidate.setBorderWidth(self._borderWidth)
psfCandidate.setWidth(self._kernelSize + 2*self._borderWidth)
psfCandidate.setHeight(self._kernelSize + 2*self._borderWidth)
im = psfCandidate.getMaskedImage().getImage()
max = afwMath.makeStatistics(im, afwMath.MAX).getValue()
if not numpy.isfinite(max):
continue
psfCandidateList.append(psfCandidate)
symb, ctype = "+", ds9.GREEN
except Exception as err:
symb, ctype = "o", ds9.RED
print "RHL", err
pass # FIXME: should log this!
else:
symb, ctype = "o", ds9.BLUE
if display and displayExposure:
ds9.dot(symb, source.getX() - mi.getX0(), source.getY() - mi.getY0(),
size=4, frame=frames["displayExposure"], ctype=ctype)
if display and pauseAtEnd:
raw_input("Continue? y[es] p[db] ")
return psfCandidateList
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
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 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)
#
# 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 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))
