def insert(self, source):
"""Insert source into the histogram."""
ixx, iyy, ixy = source.getIxx(), source.getIyy(), source.getIxy()
if self.detector:
tanSys = self.detector.makeCameraSys(TAN_PIXELS)
if tanSys in self.detector.getTransformMap():
pixToTanXYTransform = self.detector.getTransformMap()[tanSys]
p = afwGeom.Point2D(source.getX(), source.getY())
linTransform = pixToTanXYTransform.linearizeForwardTransform(
p).getLinear()
m = Quadrupole(ixx, iyy, ixy)
m.transform(linTransform)
ixx, iyy, ixy = m.getIxx(), m.getIyy(), m.getIxy()
try:
pixel = self.momentsToPixel(ixx, iyy)
i = int(pixel[0])
j = int(pixel[1])
except:
return 0
if i in range(0, self._xSize) and j in range(0, self._ySize):
if i != 0 or j != 0:
self._psfImage.set(i, j, self._psfImage.get(i, j) + 1)
self._num += 1
return 1 # success
return 0 # failure
def insert(self, source):
"""Insert source into the histogram."""
ixx, iyy, ixy = source.getIxx(), source.getIyy(), source.getIxy()
if self.detector:
tanSys = self.detector.makeCameraSys(TAN_PIXELS)
if tanSys in self.detector.getTransformMap():
pixToTanXYTransform = self.detector.getTransformMap()[tanSys]
p = afwGeom.Point2D(source.getX(), source.getY())
linTransform = pixToTanXYTransform.linearizeForwardTransform(p).getLinear()
m = Quadrupole(ixx, iyy, ixy)
m.transform(linTransform)
ixx, iyy, ixy = m.getIxx(), m.getIyy(), m.getIxy()
try:
pixel = self.momentsToPixel(ixx, iyy)
i = int(pixel[0])
j = int(pixel[1])
except:
return 0
if i in range(0, self._xSize) and j in range(0, self._ySize):
if i != 0 or j != 0:
self._psfImage.set(i, j, self._psfImage.get(i, j) + 1)
self._num += 1
return 1 # success
return 0 # failure
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 astrometric matches; ignored by this star selector
\return an lsst.pipe.base.Struct containing:
- starCat catalog of selected stars (a subset of sourceCat)
"""
import lsstDebug
display = lsstDebug.Info(__name__).display
displayExposure = lsstDebug.Info(
__name__).displayExposure # display the Exposure + spatialCells
plotMagSize = lsstDebug.Info(
__name__).plotMagSize # display the magnitude-size relation
dumpData = lsstDebug.Info(
__name__).dumpData # dump data to pickle file?
detector = exposure.getDetector()
pixToTanXYTransform = None
if detector is not None:
tanSys = detector.makeCameraSys(TAN_PIXELS)
pixToTanXYTransform = detector.getTransformMap().get(tanSys)
#
# Look at the distribution of stars in the magnitude-size plane
#
flux = sourceCat.get(self.config.sourceFluxField)
xx = numpy.empty(len(sourceCat))
xy = numpy.empty_like(xx)
yy = numpy.empty_like(xx)
for i, source in enumerate(sourceCat):
Ixx, Ixy, Iyy = source.getIxx(), source.getIxy(), source.getIyy()
if pixToTanXYTransform:
p = afwGeom.Point2D(source.getX(), source.getY())
linTransform = pixToTanXYTransform.linearizeForwardTransform(
p).getLinear()
m = Quadrupole(Ixx, Iyy, Ixy)
m.transform(linTransform)
Ixx, Iyy, Ixy = m.getIxx(), m.getIyy(), m.getIxy()
xx[i], xy[i], yy[i] = Ixx, Ixy, Iyy
width = numpy.sqrt(0.5 * (xx + yy))
bad = reduce(lambda x, y: numpy.logical_or(x, sourceCat.get(y)),
self.config.badFlags, False)
bad = numpy.logical_or(bad, flux < self.config.fluxMin)
bad = numpy.logical_or(bad, numpy.logical_not(numpy.isfinite(width)))
bad = numpy.logical_or(bad, numpy.logical_not(numpy.isfinite(flux)))
bad = numpy.logical_or(bad, width < self.config.widthMin)
bad = numpy.logical_or(bad, width > self.config.widthMax)
if self.config.fluxMax > 0:
bad = numpy.logical_or(bad, flux > self.config.fluxMax)
good = numpy.logical_not(bad)
if not numpy.any(good):
raise RuntimeError(
"No objects passed our cuts for consideration as psf stars")
mag = -2.5 * numpy.log10(flux[good])
width = width[good]
#
# Look for the maximum in the size histogram, then search upwards for the minimum that separates
# the initial peak (of, we presume, stars) from the galaxies
#
if dumpData:
import os
import pickle as pickle
_ii = 0
while True:
pickleFile = os.path.expanduser(
os.path.join("~", "widths-%d.pkl" % _ii))
if not os.path.exists(pickleFile):
break
_ii += 1
with open(pickleFile, "wb") as fd:
pickle.dump(mag, fd, -1)
pickle.dump(width, fd, -1)
centers, clusterId = _kcenters(
width,
nCluster=4,
useMedian=True,
widthStdAllowed=self.config.widthStdAllowed)
if display and plotMagSize:
fig = plot(
mag,
width,
centers,
clusterId,
magType=self.config.sourceFluxField.split(".")[-1].title(),
marker="+",
markersize=3,
markeredgewidth=None,
ltype=':',
clear=True)
else:
fig = None
clusterId = _improveCluster(
width,
centers,
clusterId,
nsigma=self.config.nSigmaClip,
widthStdAllowed=self.config.widthStdAllowed)
if display and plotMagSize:
plot(mag,
width,
centers,
clusterId,
marker="x",
markersize=3,
markeredgewidth=None,
clear=False)
stellar = (clusterId == 0)
#
# We know enough to plot, if so requested
#
frame = 0
if fig:
if display and displayExposure:
ds9.mtv(exposure.getMaskedImage(),
frame=frame,
title="PSF candidates")
global eventHandler
eventHandler = EventHandler(fig.get_axes()[0],
mag,
width,
sourceCat.getX()[good],
sourceCat.getY()[good],
frames=[frame])
fig.show()
while True:
try:
reply = input("continue? [c h(elp) q(uit) p(db)] ").strip()
except EOFError:
reply = None
if not reply:
reply = "c"
if reply:
if reply[0] == "h":
print("""\
We cluster the points; red are the stellar candidates and the other colours are other clusters.
Points labelled + are rejects from the cluster (only for cluster 0).
At this prompt, you can continue with almost any key; 'p' enters pdb, and 'h' prints this text
If displayExposure is true, you can put the cursor on a point and hit 'p' to see it in ds9.
""")
elif reply[0] == "p":
import pdb
pdb.set_trace()
elif reply[0] == 'q':
sys.exit(1)
else:
break
if display and displayExposure:
mi = exposure.getMaskedImage()
with ds9.Buffering():
for i, source in enumerate(sourceCat):
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)
starCat = SourceCatalog(sourceCat.table)
goodSources = [s for g, s in zip(good, sourceCat) if g]
for isStellar, source in zip(stellar, goodSources):
if isStellar:
starCat.append(source)
return Struct(starCat=starCat, )
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 astrometric matches; ignored by this star selector
@return an lsst.pipe.base.Struct containing:
- starCat catalog of selected stars (a subset of sourceCat)
"""
import lsstDebug
display = lsstDebug.Info(__name__).display
isGoodSource = CheckSource(sourceCat.getTable(), self.config.badFlags,
self.config.fluxLim, self.config.fluxMax)
detector = exposure.getDetector()
mi = exposure.getMaskedImage()
#
# Create an Image of Ixx v. Iyy, i.e. a 2-D histogram
#
# Use stats on our Ixx/yy values to determine the xMax/yMax range for clump image
iqqList = []
for s in sourceCat:
ixx, iyy = s.getIxx(), s.getIyy()
# ignore NaN and unrealistically large values
if (ixx == ixx and ixx < self.config.histMomentMax and iyy == iyy
and iyy < self.config.histMomentMax and isGoodSource(s)):
iqqList.append(s.getIxx())
iqqList.append(s.getIyy())
stat = afwMath.makeStatistics(
iqqList, afwMath.MEANCLIP | afwMath.STDEVCLIP | afwMath.MAX)
iqqMean = stat.getValue(afwMath.MEANCLIP)
iqqStd = stat.getValue(afwMath.STDEVCLIP)
iqqMax = stat.getValue(afwMath.MAX)
iqqLimit = max(iqqMean + self.config.histMomentClip * iqqStd,
self.config.histMomentMaxMultiplier * iqqMean)
# if the max value is smaller than our range, use max as the limit, but don't go below N*mean
if iqqLimit > iqqMax:
iqqLimit = max(self.config.histMomentMinMultiplier * iqqMean,
iqqMax)
psfHist = _PsfShapeHistogram(detector=detector,
xSize=self.config.histSize,
ySize=self.config.histSize,
ixxMax=iqqLimit,
iyyMax=iqqLimit)
if display:
frame = 0
ds9.mtv(mi, frame=frame, title="PSF candidates")
ctypes = []
for source in sourceCat:
good = isGoodSource(source)
if good:
notRejected = psfHist.insert(source)
if display:
if good:
if notRejected:
ctypes.append(ds9.GREEN) # good
else:
ctypes.append(ds9.MAGENTA) # rejected
else:
ctypes.append(ds9.RED) # bad
if display:
with ds9.Buffering():
for source, ctype in zip(sourceCat, ctypes):
ds9.dot("o",
source.getX() - mi.getX0(),
source.getY() - mi.getY0(),
frame=frame,
ctype=ctype)
clumps = psfHist.getClumps(display=display)
#
# Go through and find all the PSF-like objects
#
# We'll split the image into a number of cells, each of which contributes only
# one PSF candidate star
#
starCat = SourceCatalog(sourceCat.table)
pixToTanXYTransform = None
if detector is not None:
tanSys = detector.makeCameraSys(TAN_PIXELS)
pixToTanXYTransform = detector.getTransformMap().get(tanSys)
# psf candidate shapes must lie within this many RMS of the average shape
# N.b. if Ixx == Iyy, Ixy = 0 the criterion is
# dx^2 + dy^2 < self.config.clumpNSigma*(Ixx + Iyy) == 2*self.config.clumpNSigma*Ixx
for source in sourceCat:
if not isGoodSource(source):
continue
Ixx, Ixy, Iyy = source.getIxx(), source.getIxy(), source.getIyy()
if pixToTanXYTransform:
p = afwGeom.Point2D(source.getX(), source.getY())
linTransform = pixToTanXYTransform.linearizeForwardTransform(
p).getLinear()
m = Quadrupole(Ixx, Iyy, Ixy)
m.transform(linTransform)
Ixx, Iyy, Ixy = m.getIxx(), m.getIyy(), m.getIxy()
x, y = psfHist.momentsToPixel(Ixx, Iyy)
for clump in clumps:
dx, dy = (x - clump.x), (y - clump.y)
if math.sqrt(clump.a * dx * dx + 2 * clump.b * dx * dy +
clump.c * dy * dy) < 2 * self.config.clumpNSigma:
# A test for > would be confused by NaN
if not isGoodSource(source):
continue
try:
psfCandidate = algorithmsLib.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()
if not numpy.isfinite(
afwMath.makeStatistics(
im, afwMath.MAX).getValue()):
continue
starCat.append(source)
if display:
ds9.dot("o",
source.getX() - mi.getX0(),
source.getY() - mi.getY0(),
size=4,
frame=frame,
ctype=ds9.CYAN)
except Exception as err:
self.log.error("Failed on source %s: %s" %
(source.getId(), err))
break
return Struct(starCat=starCat, )
def _doComputeShape(self, position=None, color=None):
return Quadrupole(self.sigma**2, self.sigma**2, 0.0)
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 astrometric matches; ignored by this star selector
\return an lsst.pipe.base.Struct containing:
- starCat catalog of selected stars (a subset of sourceCat)
"""
import lsstDebug
display = lsstDebug.Info(__name__).display
displayExposure = lsstDebug.Info(__name__).displayExposure # display the Exposure + spatialCells
plotMagSize = lsstDebug.Info(__name__).plotMagSize # display the magnitude-size relation
dumpData = lsstDebug.Info(__name__).dumpData # dump data to pickle file?
detector = exposure.getDetector()
pixToTanXYTransform = None
if detector is not None:
tanSys = detector.makeCameraSys(TAN_PIXELS)
pixToTanXYTransform = detector.getTransformMap().get(tanSys)
#
# Look at the distribution of stars in the magnitude-size plane
#
flux = sourceCat.get(self.config.sourceFluxField)
xx = numpy.empty(len(sourceCat))
xy = numpy.empty_like(xx)
yy = numpy.empty_like(xx)
for i, source in enumerate(sourceCat):
Ixx, Ixy, Iyy = source.getIxx(), source.getIxy(), source.getIyy()
if pixToTanXYTransform:
p = afwGeom.Point2D(source.getX(), source.getY())
linTransform = pixToTanXYTransform.linearizeForwardTransform(p).getLinear()
m = Quadrupole(Ixx, Iyy, Ixy)
m.transform(linTransform)
Ixx, Iyy, Ixy = m.getIxx(), m.getIyy(), m.getIxy()
xx[i], xy[i], yy[i] = Ixx, Ixy, Iyy
width = numpy.sqrt(0.5*(xx + yy))
bad = reduce(lambda x, y: numpy.logical_or(x, sourceCat.get(y)), self.config.badFlags, False)
bad = numpy.logical_or(bad, flux < self.config.fluxMin)
bad = numpy.logical_or(bad, numpy.logical_not(numpy.isfinite(width)))
bad = numpy.logical_or(bad, numpy.logical_not(numpy.isfinite(flux)))
bad = numpy.logical_or(bad, width < self.config.widthMin)
bad = numpy.logical_or(bad, width > self.config.widthMax)
if self.config.fluxMax > 0:
bad = numpy.logical_or(bad, flux > self.config.fluxMax)
good = numpy.logical_not(bad)
if not numpy.any(good):
raise RuntimeError("No objects passed our cuts for consideration as psf stars")
mag = -2.5*numpy.log10(flux[good])
width = width[good]
#
# Look for the maximum in the size histogram, then search upwards for the minimum that separates
# the initial peak (of, we presume, stars) from the galaxies
#
if dumpData:
import os
import cPickle as pickle
_ii = 0
while True:
pickleFile = os.path.expanduser(os.path.join("~", "widths-%d.pkl" % _ii))
if not os.path.exists(pickleFile):
break
_ii += 1
with open(pickleFile, "wb") as fd:
pickle.dump(mag, fd, -1)
pickle.dump(width, fd, -1)
centers, clusterId = _kcenters(width, nCluster=4, useMedian=True,
widthStdAllowed=self.config.widthStdAllowed)
if display and plotMagSize and pyplot:
fig = plot(mag, width, centers, clusterId,
magType=self.config.sourceFluxField.split(".")[-1].title(),
marker="+", markersize=3, markeredgewidth=None, ltype=':', clear=True)
else:
fig = None
clusterId = _improveCluster(width, centers, clusterId,
nsigma = self.config.nSigmaClip,
widthStdAllowed=self.config.widthStdAllowed)
if display and plotMagSize and pyplot:
plot(mag, width, centers, clusterId, marker="x", markersize=3, markeredgewidth=None, clear=False)
stellar = (clusterId == 0)
#
# We know enough to plot, if so requested
#
frame = 0
if fig:
if display and displayExposure:
ds9.mtv(exposure.getMaskedImage(), frame=frame, title="PSF candidates")
global eventHandler
eventHandler = EventHandler(fig.get_axes()[0], mag, width,
sourceCat.getX()[good], sourceCat.getY()[good], frames=[frame])
fig.show()
#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
while True:
try:
reply = raw_input("continue? [c h(elp) q(uit) p(db)] ").strip()
except EOFError:
reply = None
if not reply:
reply = "c"
if reply:
if reply[0] == "h":
print """\
We cluster the points; red are the stellar candidates and the other colours are other clusters.
Points labelled + are rejects from the cluster (only for cluster 0).
At this prompt, you can continue with almost any key; 'p' enters pdb, and 'h' prints this text
If displayExposure is true, you can put the cursor on a point and hit 'p' to see it in ds9.
"""
elif reply[0] == "p":
import pdb
pdb.set_trace()
elif reply[0] == 'q':
sys.exit(1)
else:
break
if display and displayExposure:
mi = exposure.getMaskedImage()
with ds9.Buffering():
for i, source in enumerate(sourceCat):
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)
starCat = SourceCatalog(sourceCat.table)
goodSources = [s for g, s in zip(good, sourceCat) if g]
for isStellar, source in zip(stellar, goodSources):
if isStellar:
starCat.append(source)
return Struct(
starCat = starCat,
)
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 astrometric matches; ignored by this star selector
@return an lsst.pipe.base.Struct containing:
- starCat catalog of selected stars (a subset of sourceCat)
"""
import lsstDebug
display = lsstDebug.Info(__name__).display
isGoodSource = CheckSource(sourceCat.getTable(), self.config.badFlags, self.config.fluxLim,
self.config.fluxMax)
detector = exposure.getDetector()
mi = exposure.getMaskedImage()
#
# Create an Image of Ixx v. Iyy, i.e. a 2-D histogram
#
# Use stats on our Ixx/yy values to determine the xMax/yMax range for clump image
iqqList = []
for s in sourceCat:
ixx, iyy = s.getIxx(), s.getIyy()
# ignore NaN and unrealistically large values
if (ixx == ixx and ixx < self.config.histMomentMax and
iyy == iyy and iyy < self.config.histMomentMax and
isGoodSource(s)):
iqqList.append(s.getIxx())
iqqList.append(s.getIyy())
stat = afwMath.makeStatistics(iqqList, afwMath.MEANCLIP | afwMath.STDEVCLIP | afwMath.MAX)
iqqMean = stat.getValue(afwMath.MEANCLIP)
iqqStd = stat.getValue(afwMath.STDEVCLIP)
iqqMax = stat.getValue(afwMath.MAX)
iqqLimit = max(iqqMean + self.config.histMomentClip*iqqStd,
self.config.histMomentMaxMultiplier*iqqMean)
# if the max value is smaller than our range, use max as the limit, but don't go below N*mean
if iqqLimit > iqqMax:
iqqLimit = max(self.config.histMomentMinMultiplier*iqqMean, iqqMax)
psfHist = _PsfShapeHistogram(detector=detector,
xSize=self.config.histSize, ySize=self.config.histSize,
ixxMax=iqqLimit, iyyMax=iqqLimit)
if display:
frame = 0
ds9.mtv(mi, frame=frame, title="PSF candidates")
with ds9.Buffering():
for source in sourceCat:
if isGoodSource(source):
if psfHist.insert(source): # n.b. this call has the side effect of inserting
ctype = ds9.GREEN # good
else:
ctype = ds9.MAGENTA # rejected
else:
ctype = ds9.RED # bad
if display:
ds9.dot("o", source.getX() - mi.getX0(),
source.getY() - mi.getY0(), frame=frame, ctype=ctype)
clumps = psfHist.getClumps(display=display)
#
# Go through and find all the PSF-like objects
#
# We'll split the image into a number of cells, each of which contributes only
# one PSF candidate star
#
starCat = SourceCatalog(sourceCat.table)
pixToTanXYTransform = None
if detector is not None:
tanSys = detector.makeCameraSys(TAN_PIXELS)
pixToTanXYTransform = detector.getTransformMap().get(tanSys)
# psf candidate shapes must lie within this many RMS of the average shape
# N.b. if Ixx == Iyy, Ixy = 0 the criterion is
# dx^2 + dy^2 < self.config.clumpNSigma*(Ixx + Iyy) == 2*self.config.clumpNSigma*Ixx
for source in sourceCat:
if not isGoodSource(source):
continue
Ixx, Ixy, Iyy = source.getIxx(), source.getIxy(), source.getIyy()
if pixToTanXYTransform:
p = afwGeom.Point2D(source.getX(), source.getY())
linTransform = pixToTanXYTransform.linearizeForwardTransform(p).getLinear()
m = Quadrupole(Ixx, Iyy, Ixy)
m.transform(linTransform)
Ixx, Iyy, Ixy = m.getIxx(), m.getIyy(), m.getIxy()
x, y = psfHist.momentsToPixel(Ixx, Iyy)
for clump in clumps:
dx, dy = (x - clump.x), (y - clump.y)
if math.sqrt(clump.a*dx*dx + 2*clump.b*dx*dy + clump.c*dy*dy) < 2*self.config.clumpNSigma:
# A test for > would be confused by NaN
if not isGoodSource(source):
continue
try:
psfCandidate = algorithmsLib.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()
if not numpy.isfinite(afwMath.makeStatistics(im, afwMath.MAX).getValue()):
continue
starCat.append(source)
if display:
ds9.dot("o", source.getX() - mi.getX0(), source.getY() - mi.getY0(),
size=4, frame=frame, ctype=ds9.CYAN)
except Exception as err:
self.log.error("Failed on source %s: %s" % (source.getId(), err))
break
return Struct(
starCat = starCat,
)