def process(self):
logging.Trace_setVerbosity("lsst.detection", 5)
logging.Trace(
"lsst.detection.DetectionStage", 3,
'Python DetectionStage process : _rank %i stageId %d' %
(self._rank, self.stageId))
activeClipboard = self.inputQueue.getNextDataset()
###########
#
# Get objects from clipboard
#
triggerEvent = activeClipboard.get('triggerVisitEvent')
filterNameItem = triggerEvent.findUnique('filterName')
filterName = filterNameItem.getValueString()
exposureIdItem = triggerEvent.findUnique('exposureId')
exposureId = exposureIdItem.getValueInt()
visitTimeItem = triggerEvent.findUnique('visitTime')
visitTime = visitTimeItem.getValueDouble()
###########
#
# Log the beginning of Detection stage for this slice
#
LogRec(self.detectionLog, Log.INFO) \
<< "Began detection stage" \
<< DataProperty("exposureId", exposureId) \
<< DataProperty("visitTime", visitTime) \
<< DataProperty("filterName", filterName) \
<< LogRec.endr
#
# Instantiate a Filter object to get the id of filterName
#
dbLocation = dafper.LogicalLocation(
'mysql://lsst10.ncsa.uiuc.edu:3306/test')
filterDB = lsst.afw.image.Filter(dbLocation, filterName)
filterId = filterDB.getId()
logging.Trace("lsst.detection.DetectionStage", 3,
'FilterName %s FilterId %d' % (filterName, filterId))
differenceImageExposure = activeClipboard.get('DifferenceExposure')
diaSourceCollection = Detection.detection(
differenceImageExposure=differenceImageExposure,
policy=self._policy,
filterId=filterId,
useLog=self.detectionLog,
footprintList=None,
)
###########
#
# Post results to clipboard
#
activeClipboard.put('DiaSources', diaSourceCollection)
self.outputQueue.addDataset(activeClipboard)
def stats(self, cid, diffim, core=5):
self.dStats.apply(diffim)
pexLog.Trace("lsst.ip.diffim.JackknifeResampleKernel", 1,
"Candidate %d : Residuals all (%d px): %.3f +/- %.3f" % (cid,
self.dStats.getNpix(),
self.dStats.getMean(),
self.dStats.getRms()))
self.dStats.apply(diffim, core)
pexLog.Trace("lsst.ip.diffim.JackknifeResampleKernel", 1,
"Candidate %d : Residuals core (%d px): %.3f +/- %.3f" % (cid,
self.dStats.getNpix(),
self.dStats.getMean(),
self.dStats.getRms()))
def _createPcaBasis(self, kernelCellSet, nStarPerCell, policy):
"""!Create Principal Component basis
If a principal component analysis is requested, typically when using a delta function basis,
perform the PCA here and return a new basis list containing the new principal components.
@param kernelCellSet: a SpatialCellSet containing KernelCandidates, from which components are derived
@param nStarPerCell: the number of stars per cell to visit when doing the PCA
@param policy: input policy controlling the single kernel visitor
@return
- nRejectedPca: number of KernelCandidates rejected during PCA loop
- spatialBasisList: basis list containing the principal shapes as Kernels
"""
nComponents = self.kConfig.numPrincipalComponents
imagePca = diffimLib.KernelPcaD()
importStarVisitor = diffimLib.KernelPcaVisitorF(imagePca)
kernelCellSet.visitCandidates(importStarVisitor, nStarPerCell)
if self.kConfig.subtractMeanForPca:
importStarVisitor.subtractMean()
imagePca.analyze()
eigenValues = imagePca.getEigenValues()
pcaBasisList = importStarVisitor.getEigenKernels()
eSum = num.sum(eigenValues)
if eSum == 0.0:
raise RuntimeError("Eigenvalues sum to zero")
for j in range(len(eigenValues)):
pexLog.Trace(
self.log.getName() + "._solve", 6, "Eigenvalue %d : %f (%f)" %
(j, eigenValues[j], eigenValues[j] / eSum))
nToUse = min(nComponents, len(eigenValues))
trimBasisList = afwMath.KernelList()
for j in range(nToUse):
# Check for NaNs?
kimage = afwImage.ImageD(pcaBasisList[j].getDimensions())
pcaBasisList[j].computeImage(kimage, False)
if not (True in num.isnan(kimage.getArray())):
trimBasisList.push_back(pcaBasisList[j])
# Put all the power in the first kernel, which will not vary spatially
spatialBasisList = diffimLib.renormalizeKernelList(trimBasisList)
# New Kernel visitor for this new basis list (no regularization explicitly)
singlekvPca = diffimLib.BuildSingleKernelVisitorF(
spatialBasisList, policy)
singlekvPca.setSkipBuilt(False)
kernelCellSet.visitCandidates(singlekvPca, nStarPerCell)
singlekvPca.setSkipBuilt(True)
nRejectedPca = singlekvPca.getNRejected()
return nRejectedPca, spatialBasisList
def assess(self, cand, kFn1, bgFn1, kFn2, bgFn2, frame0):
tmi = cand.getTemplateMaskedImage()
smi = cand.getScienceMaskedImage()
im1 = afwImage.ImageD(kFn1.getDimensions())
kFn1.computeImage(im1, False,
afwImage.indexToPosition(int(cand.getXCenter())),
afwImage.indexToPosition(int(cand.getYCenter())))
fk1 = afwMath.FixedKernel(im1)
bg1 = bgFn1(afwImage.indexToPosition(int(cand.getXCenter())),
afwImage.indexToPosition(int(cand.getYCenter())))
d1 = ipDiffim.convolveAndSubtract(tmi, smi, fk1, bg1)
####
im2 = afwImage.ImageD(kFn2.getDimensions())
kFn2.computeImage(im2, False,
afwImage.indexToPosition(int(cand.getXCenter())),
afwImage.indexToPosition(int(cand.getYCenter())))
fk2 = afwMath.FixedKernel(im2)
bg2 = bgFn2(afwImage.indexToPosition(int(cand.getXCenter())),
afwImage.indexToPosition(int(cand.getYCenter())))
d2 = ipDiffim.convolveAndSubtract(tmi, smi, fk2, bg2)
if display:
ds9.mtv(tmi, frame=frame0+0)
ds9.dot("Cand %d" % (cand.getId()), 0, 0, frame=frame0+0)
ds9.mtv(smi, frame=frame0+1)
ds9.mtv(im1, frame=frame0+2)
ds9.mtv(d1, frame=frame0+3)
ds9.mtv(im2, frame=frame0+4)
ds9.mtv(d2, frame=frame0+5)
pexLog.Trace("lsst.ip.diffim.JackknifeResampleKernel", 1,
"Full Spatial Model")
self.stats(cand.getId(), d1)
pexLog.Trace("lsst.ip.diffim.JackknifeResampleKernel", 1,
"N-1 Spatial Model")
self.stats(cand.getId(), d2)
def jackknifeResample(self, psfmatch, results):
kernel = results.psfMatchingKernel
bg = results.backgroundModel
cellSet = results.kernelCellSet
goodList = []
for cell in cellSet.getCellList():
print
for cand in cell.begin(False):
cand = ipDiffim.cast_KernelCandidateF(cand)
if cand.getStatus() == afwMath.SpatialCellCandidate.GOOD:
goodList.append(cand.getId())
else:
# This is so that UNKNOWNs are not processed
cand.setStatus(afwMath.SpatialCellCandidate.BAD)
nStarPerCell = self.config.nStarPerCell
policy = pexConfig.makePolicy(self.config)
for idx in range(len(goodList)):
cid = goodList[idx]
print # clear the screen
pexLog.Trace("lsst.ip.diffim.JackknifeResampleKernel", 1,
"Removing candidate %d" % (cid))
cand = self.setStatus(cellSet, cid, afwMath.SpatialCellCandidate.BAD)
# From _solve
regionBBox = cellSet.getBBox()
spatialkv = ipDiffim.BuildSpatialKernelVisitorF(kernel.getKernelList(), regionBBox, policy)
cellSet.visitCandidates(spatialkv, nStarPerCell)
spatialkv.solveLinearEquation()
jkKernel, jkBg = spatialkv.getSolutionPair()
#jkResults = psfmatch._solve(cellSet, kernel.getKernelList())
#jkKernel = jkResults[1]
#jkBg = jkResults[2]
# lots of windows
# self.assess(cand, kernel, bg, jkKernel, jkBg, 6*idx+1)
# only 6 windows
self.assess(cand, kernel, bg, jkKernel, jkBg, 1)
self.setStatus(cellSet, cid, afwMath.SpatialCellCandidate.GOOD)
def runTest(self, mode):
pexLog.Trace("lsst.ip.diffim.JackknifeResampleKernel", 1,
"Mode %s" % (mode))
if mode == "DF":
self.config = self.subconfigDF
elif mode == "DFr":
self.config = self.subconfigDFr
elif mode == "AL":
self.config = self.subconfigAL
else:
raise
psfmatch = ipDiffim.ImagePsfMatchTask(self.config)
results = psfmatch.run(self.templateMaskedImage,
self.scienceMaskedImage,
"subtractMaskedImages")
self.jackknifeResample(psfmatch, results)
def backgroundSubtract(config, maskedImages):
backgrounds = []
t0 = time.time()
algorithm = config.algorithm
binsize = config.binSize
undersample = config.undersampleStyle
bctrl = afwMath.BackgroundControl(algorithm)
bctrl.setUndersampleStyle(undersample)
for maskedImage in maskedImages:
bctrl.setNxSample(maskedImage.getWidth()//binsize + 1)
bctrl.setNySample(maskedImage.getHeight()//binsize + 1)
image = maskedImage.getImage()
backobj = afwMath.makeBackground(image, bctrl)
image -= backobj.getImageF()
backgrounds.append(backobj.getImageF())
del backobj
t1 = time.time()
pexLog.Trace("lsst.ip.diffim.backgroundSubtract", 1,
"Total time for background subtraction : %.2f s" % (t1-t0))
return backgrounds
def testTraceFromPython(self):
pexLog.Trace.setVerbosity("lsst.afw", 3)
pexLog.Trace("lsst.afw", 2, "Hello")
pexLog.Trace("lsst.afw", 5, "world")
def EventFromInputFileList(inputfile,
datatypePolicy,
expTime=EXP_TIME,
slewTime=SLEW_TIME,
maxvisits=-1,
rootTopicName=ROOT_EVENT_TOPIC,
hostName=EVENT_BROKER,
metadataPolicy=None):
"""
Generate events for the IPSD (and MOPS) pipeline by reading a list of visit
directories and extracting the relevant information from the FITS files
therein.
The two optional parameters are the exposure time of each exposure in a
visit (assumed constant) and the average slew time of the telescope. Both
are in seconds. The default to 15 seconds and 5 seconds respecively.
The script sends one event for the first visit exposure, waits <exp time>
sec and then sends an event for the second exposure. At that point, it
waits (<exp time> + <slew time>) sec before passing to the next visit.
The input directory list is a simple text file listing visit directories
one per line. Comments start with a '#' and are ignored. It is assumed
that the name of each directory in the file is a valid visitId. Also it
is assumed that each directory has the following structure:
visitId/
0/
raw-<visitId>-e000-c<ccdId>-a<ampId>.fits
1/
raw-<visitId>-e001-c<ccdId>-a<ampId>.fits
@param inputfile name of the directory list file.
@param datatypePolicy policy file for the input data.
@param expTime assumed exposure per visit in seconds (defaults
to 15 seconds for DC3).
@param slewTime assumed average slew time in seconds (defaults
to 5 seconds for DC3).
@param rootTopicName root name for the event's topic. The final topic
will be rootTopicName+'0' or rootTopicName+'1'
depending on whether the event refers to the
first or second image of the visit.
@param hostName hostname of the event broker.
@param metadataPolicy policy defining the event metadata types
@return None
"""
global visitCount
# Create a metadata policy object.
if metadataPolicy is None:
mpf = pexPolicy.DefaultPolicyFile("ctrl_dc3pipe",
"dc3MetadataPolicy.paf", "pipeline")
metadataPolicy = pexPolicy.Policy.createPolicy(mpf,
mpf.getRepositoryPath())
# Covenience function.
def sendEvent(f):
return(eventFromFitsfile.EventFromInputfile(f,
datatypePolicy,
metadataPolicy,
rootTopicName,
hostName))
f = open(inputfile)
for line in f:
dirName = line.strip()
if(line.startswith('#')):
continue
visitCount += 1
if maxvisits >= 0 and visitCount > maxvisits:
logger.log(logger.INFO,
"Maximum visit count reached (%s); quitting." %
maxvisits)
return
# Get the list of amp FITS files in each dir.
fileList0 = glob.glob(os.path.join(dirName, '0', '*.fits'))
fileList1 = glob.glob(os.path.join(dirName, '1', '*.fits'))
# Simple sanity check.
if(len(fileList0) != len(fileList1)):
pexLog.Trace('dc3pipe.eventfrominputfilelist', 1,
'Skipping %s: wrong file count in 0 and 1' \
%(dirName))
continue
# Now we just trust that the i-th file in 0 corresponds to the i-th file
# in 1... Fortunately, we only need to send one event per image
# directory, since all images there are one MEF split into individual
# amps.
sendEvent(fileList0[0])
# Sleep some.
time.sleep(expTime)
# Next event.
sendEvent(fileList1[0])
# Sleep expTime + slewTime.
time.sleep(expTime + slewTime)
f.close()
return
def _solve(self, kernelCellSet, basisList, returnOnExcept=False):
"""!Solve for the PSF matching kernel
@param kernelCellSet: a SpatialCellSet to use in determining the matching kernel
(typically as provided by _buildCellSet)
@param basisList: list of Kernels to be used in the decomposition of the spatially varying kernel
(typically as provided by makeKernelBasisList)
@param returnOnExcept: if True then return (None, None) if an error occurs, else raise the exception
@return
- psfMatchingKernel: PSF matching kernel
- backgroundModel: differential background model
Raise Exception if unable to determine PSF matching kernel and returnOnExcept False
"""
import lsstDebug
display = lsstDebug.Info(__name__).display
maxSpatialIterations = self.kConfig.maxSpatialIterations
nStarPerCell = self.kConfig.nStarPerCell
usePcaForSpatialKernel = self.kConfig.usePcaForSpatialKernel
# Visitor for the single kernel fit
policy = pexConfig.makePolicy(self.kConfig)
if self.useRegularization:
singlekv = diffimLib.BuildSingleKernelVisitorF(
basisList, policy, self.hMat)
else:
singlekv = diffimLib.BuildSingleKernelVisitorF(basisList, policy)
# Visitor for the kernel sum rejection
ksv = diffimLib.KernelSumVisitorF(policy)
# Main loop
t0 = time.time()
try:
totalIterations = 0
thisIteration = 0
while (thisIteration < maxSpatialIterations):
# Make sure there are no uninitialized candidates as active occupants of Cell
nRejectedSkf = -1
while (nRejectedSkf != 0):
pexLog.Trace(self.log.getName() + "._solve", 2,
"Building single kernels...")
kernelCellSet.visitCandidates(singlekv, nStarPerCell)
nRejectedSkf = singlekv.getNRejected()
pexLog.Trace(
self.log.getName() + "._solve", 2,
"Iteration %d, rejected %d candidates due to initial kernel fit"
% (thisIteration, nRejectedSkf))
# Reject outliers in kernel sum
ksv.resetKernelSum()
ksv.setMode(diffimLib.KernelSumVisitorF.AGGREGATE)
kernelCellSet.visitCandidates(ksv, nStarPerCell)
ksv.processKsumDistribution()
ksv.setMode(diffimLib.KernelSumVisitorF.REJECT)
kernelCellSet.visitCandidates(ksv, nStarPerCell)
nRejectedKsum = ksv.getNRejected()
pexLog.Trace(
self.log.getName() + "._solve", 2,
"Iteration %d, rejected %d candidates due to kernel sum" %
(thisIteration, nRejectedKsum))
# Do we jump back to the top without incrementing thisIteration?
if nRejectedKsum > 0:
totalIterations += 1
continue
# At this stage we can either apply the spatial fit to
# the kernels, or we run a PCA, use these as a *new*
# basis set with lower dimensionality, and then apply
# the spatial fit to these kernels
if (usePcaForSpatialKernel):
pexLog.Trace(self.log.getName() + "._solve", 1,
"Building Pca basis")
nRejectedPca, spatialBasisList = self._createPcaBasis(
kernelCellSet, nStarPerCell, policy)
pexLog.Trace(
self.log.getName() + "._solve", 2,
"Iteration %d, rejected %d candidates due to Pca kernel fit"
% (thisIteration, nRejectedPca))
# We don't want to continue on (yet) with the
# spatial modeling, because we have bad objects
# contributing to the Pca basis. We basically
# need to restart from the beginning of this loop,
# since the cell-mates of those objects that were
# rejected need their original Kernels built by
# singleKernelFitter.
# Don't count against thisIteration
if (nRejectedPca > 0):
totalIterations += 1
continue
else:
spatialBasisList = basisList
# We have gotten on to the spatial modeling part
regionBBox = kernelCellSet.getBBox()
spatialkv = diffimLib.BuildSpatialKernelVisitorF(
spatialBasisList, regionBBox, policy)
kernelCellSet.visitCandidates(spatialkv, nStarPerCell)
spatialkv.solveLinearEquation()
pexLog.Trace(
self.log.getName() + "._solve", 3,
"Spatial kernel built with %d candidates" %
(spatialkv.getNCandidates()))
spatialKernel, spatialBackground = spatialkv.getSolutionPair()
# Check the quality of the spatial fit (look at residuals)
assesskv = diffimLib.AssessSpatialKernelVisitorF(
spatialKernel, spatialBackground, policy)
kernelCellSet.visitCandidates(assesskv, nStarPerCell)
nRejectedSpatial = assesskv.getNRejected()
nGoodSpatial = assesskv.getNGood()
pexLog.Trace(
self.log.getName() + "._solve", 2,
"Iteration %d, rejected %d candidates due to spatial kernel fit"
% (thisIteration, nRejectedSpatial))
pexLog.Trace(self.log.getName() + "._solve", 2,
"%d candidates used in fit" % (nGoodSpatial))
# If only nGoodSpatial == 0, might be other candidates in the cells
if nGoodSpatial == 0 and nRejectedSpatial == 0:
raise RuntimeError("No kernel candidates for spatial fit")
if nRejectedSpatial == 0:
# Nothing rejected, finished with spatial fit
break
# Otherwise, iterate on...
thisIteration += 1
# Final fit if above did not converge
if (nRejectedSpatial > 0) and (thisIteration
== maxSpatialIterations):
pexLog.Trace(self.log.getName() + "._solve", 2,
"Final spatial fit")
if (usePcaForSpatialKernel):
nRejectedPca, spatialBasisList = self._createPcaBasis(
kernelCellSet, nStarPerCell, policy)
regionBBox = kernelCellSet.getBBox()
spatialkv = diffimLib.BuildSpatialKernelVisitorF(
spatialBasisList, regionBBox, policy)
kernelCellSet.visitCandidates(spatialkv, nStarPerCell)
spatialkv.solveLinearEquation()
pexLog.Trace(
self.log.getName() + "._solve", 3,
"Spatial kernel built with %d candidates" %
(spatialkv.getNCandidates()))
spatialKernel, spatialBackground = spatialkv.getSolutionPair()
spatialSolution = spatialkv.getKernelSolution()
except Exception as e:
pexLog.Trace(self.log.getName() + "._solve", 1,
"ERROR: Unable to calculate psf matching kernel")
pexLog.Trace(self.log.getName() + "._solve", 2, str(e))
raise e
t1 = time.time()
pexLog.Trace(
self.log.getName() + "._solve", 1,
"Total time to compute the spatial kernel : %.2f s" % (t1 - t0))
if display:
self._displayDebug(kernelCellSet, spatialKernel, spatialBackground)
self._diagnostic(kernelCellSet, spatialSolution, spatialKernel,
spatialBackground)
return spatialSolution, spatialKernel, spatialBackground
def matchMaskedImages(self,
templateMaskedImage,
scienceMaskedImage,
candidateList,
templateFwhmPix=None,
scienceFwhmPix=None):
"""!PSF-match a MaskedImage (templateMaskedImage) to a reference MaskedImage (scienceMaskedImage)
Do the following, in order:
- Determine a PSF matching kernel and differential background model
that matches templateMaskedImage to scienceMaskedImage
- Convolve templateMaskedImage by the PSF matching kernel
@param templateMaskedImage: masked image to PSF-match to the reference masked image;
must be warped to match the reference masked image
@param scienceMaskedImage: maskedImage whose PSF is to be matched to
@param templateFwhmPix: FWHM (in pixels) of the Psf in the template image (image to convolve)
@param scienceFwhmPix: FWHM (in pixels) of the Psf in the science image
@param candidateList: a list of footprints/maskedImages for kernel candidates;
if None then source detection is run.
- Currently supported: list of Footprints or measAlg.PsfCandidateF
@return a pipeBase.Struct containing these fields:
- psfMatchedMaskedImage: the PSF-matched masked image =
templateMaskedImage convolved with psfMatchingKernel.
This has the same xy0, dimensions and wcs as scienceMaskedImage.
- psfMatchingKernel: the PSF matching kernel
- backgroundModel: differential background model
- kernelCellSet: SpatialCellSet used to solve for the PSF matching kernel
Raise a RuntimeError if input images have different dimensions
"""
import lsstDebug
display = lsstDebug.Info(__name__).display
displayTemplate = lsstDebug.Info(__name__).displayTemplate
displaySciIm = lsstDebug.Info(__name__).displaySciIm
displaySpatialCells = lsstDebug.Info(__name__).displaySpatialCells
maskTransparency = lsstDebug.Info(__name__).maskTransparency
if not maskTransparency:
maskTransparency = 0
if display:
ds9.setMaskTransparency(maskTransparency)
if not candidateList:
raise RuntimeError(
"Candidate list must be populated by makeCandidateList")
if not self._validateSize(templateMaskedImage, scienceMaskedImage):
pexLog.Trace(self.log.getName(), 1,
"ERROR: Input images different size")
raise RuntimeError("Input images different size")
if display and displayTemplate:
ds9.mtv(templateMaskedImage,
frame=lsstDebug.frame,
title="Image to convolve")
lsstDebug.frame += 1
if display and displaySciIm:
ds9.mtv(scienceMaskedImage,
frame=lsstDebug.frame,
title="Image to not convolve")
lsstDebug.frame += 1
kernelCellSet = self._buildCellSet(templateMaskedImage,
scienceMaskedImage, candidateList)
if display and displaySpatialCells:
diUtils.showKernelSpatialCells(scienceMaskedImage,
kernelCellSet,
symb="o",
ctype=ds9.CYAN,
ctypeUnused=ds9.YELLOW,
ctypeBad=ds9.RED,
size=4,
frame=lsstDebug.frame,
title="Image to not convolve")
lsstDebug.frame += 1
if templateFwhmPix and scienceFwhmPix:
self.log.info("Matching Psf FWHM %.2f -> %.2f pix" %
(templateFwhmPix, scienceFwhmPix))
if self.kConfig.useBicForKernelBasis:
tmpKernelCellSet = self._buildCellSet(templateMaskedImage,
scienceMaskedImage,
candidateList)
nbe = diffimTools.NbasisEvaluator(self.kConfig, templateFwhmPix,
scienceFwhmPix)
bicDegrees = nbe(tmpKernelCellSet, self.log)
basisList = makeKernelBasisList(self.kConfig,
templateFwhmPix,
scienceFwhmPix,
alardDegGauss=bicDegrees[0],
metadata=self.metadata)
del tmpKernelCellSet
else:
basisList = makeKernelBasisList(self.kConfig,
templateFwhmPix,
scienceFwhmPix,
metadata=self.metadata)
spatialSolution, psfMatchingKernel, backgroundModel = self._solve(
kernelCellSet, basisList)
psfMatchedMaskedImage = afwImage.MaskedImageF(
templateMaskedImage.getBBox())
doNormalize = False
afwMath.convolve(psfMatchedMaskedImage, templateMaskedImage,
psfMatchingKernel, doNormalize)
return pipeBase.Struct(
matchedImage=psfMatchedMaskedImage,
psfMatchingKernel=psfMatchingKernel,
backgroundModel=backgroundModel,
kernelCellSet=kernelCellSet,
)
def _buildCellSet(self, exposure, referencePsfModel):
"""!Build a SpatialCellSet for use with the solve method
@param exposure: The science exposure that will be convolved; must contain a Psf
@param referencePsfModel: Psf model to match to
@return kernelCellSet: a SpatialCellSet to be used by self._solve
Raise a RuntimeError if the reference Psf model and science Psf model have different dimensions
"""
scienceBBox = exposure.getBBox()
sciencePsfModel = exposure.getPsf()
# The Psf base class does not support getKernel() in general, as there are some Psf
# classes for which this is not meaningful.
# Many Psfs we use in practice are KernelPsfs, and this algorithm will work fine for them,
# but someday it should probably be modified to support arbitrary Psfs.
referencePsfModel = measAlg.KernelPsf.swigConvert(referencePsfModel)
sciencePsfModel = measAlg.KernelPsf.swigConvert(sciencePsfModel)
if referencePsfModel is None or sciencePsfModel is None:
raise RuntimeError(
"ERROR: Psf matching is only implemented for KernelPsfs")
if (referencePsfModel.getKernel().getDimensions() !=
sciencePsfModel.getKernel().getDimensions()):
pexLog.Trace(
self.log.getName(), 1,
"ERROR: Dimensions of reference Psf and science Psf different; exiting"
)
raise RuntimeError, "ERROR: Dimensions of reference Psf and science Psf different; exiting"
psfWidth, psfHeight = referencePsfModel.getKernel().getDimensions()
maxKernelSize = min(psfWidth, psfHeight) - 1
if maxKernelSize % 2 == 0:
maxKernelSize -= 1
if self.kConfig.kernelSize > maxKernelSize:
raise ValueError, "Kernel size (%d) too big to match Psfs of size %d; reduce to at least %d" % (
self.kConfig.kernelSize, psfWidth, maxKernelSize)
# Infer spatial order of Psf model!
#
# Infer from the number of spatial parameters.
# (O + 1) * (O + 2) / 2 = N
# O^2 + 3 * O + 2 * (1 - N) = 0
#
# Roots are [-3 +/- sqrt(9 - 8 * (1 - N))] / 2
#
nParameters = sciencePsfModel.getKernel().getNSpatialParameters()
root = num.sqrt(9 - 8 * (1 - nParameters))
if (root != root // 1): # We know its an integer solution
pexLog.Trace(self.log.getName(), 3,
"Problem inferring spatial order of image's Psf")
else:
order = (root - 3) / 2
if (order != order // 1):
pexLog.Trace(self.log.getName(), 3,
"Problem inferring spatial order of image's Psf")
else:
pexLog.Trace(
self.log.getName(), 2,
"Spatial order of Psf = %d; matching kernel order = %d" %
(order, self.kConfig.spatialKernelOrder))
regionSizeX, regionSizeY = scienceBBox.getDimensions()
scienceX0, scienceY0 = scienceBBox.getMin()
sizeCellX = self.kConfig.sizeCellX
sizeCellY = self.kConfig.sizeCellY
kernelCellSet = afwMath.SpatialCellSet(
afwGeom.Box2I(afwGeom.Point2I(scienceX0, scienceY0),
afwGeom.Extent2I(regionSizeX, regionSizeY)),
sizeCellX, sizeCellY)
nCellX = regionSizeX // sizeCellX
nCellY = regionSizeY // sizeCellY
dimenR = referencePsfModel.getKernel().getDimensions()
dimenS = sciencePsfModel.getKernel().getDimensions()
policy = pexConfig.makePolicy(self.kConfig)
for row in range(nCellY):
# place at center of cell
posY = sizeCellY * row + sizeCellY // 2 + scienceY0
for col in range(nCellX):
# place at center of cell
posX = sizeCellX * col + sizeCellX // 2 + scienceX0
pexLog.Trace(
self.log.getName(), 5,
"Creating Psf candidate at %.1f %.1f" % (posX, posY))
# reference kernel image, at location of science subimage
kernelImageR = referencePsfModel.computeImage(
afwGeom.Point2D(posX, posY)).convertF()
kernelMaskR = afwImage.MaskU(dimenR)
kernelMaskR.set(0)
kernelVarR = afwImage.ImageF(dimenR)
kernelVarR.set(1.0)
referenceMI = afwImage.MaskedImageF(kernelImageR, kernelMaskR,
kernelVarR)
# kernel image we are going to convolve
kernelImageS = sciencePsfModel.computeImage(
afwGeom.Point2D(posX, posY)).convertF()
kernelMaskS = afwImage.MaskU(dimenS)
kernelMaskS.set(0)
kernelVarS = afwImage.ImageF(dimenS)
kernelVarS.set(1.0)
scienceMI = afwImage.MaskedImageF(kernelImageS, kernelMaskS,
kernelVarS)
# The image to convolve is the science image, to the reference Psf.
kc = diffimLib.makeKernelCandidate(posX, posY, scienceMI,
referenceMI, policy)
kernelCellSet.insertCandidate(kc)
import lsstDebug
display = lsstDebug.Info(__name__).display
displaySpatialCells = lsstDebug.Info(__name__).displaySpatialCells
maskTransparency = lsstDebug.Info(__name__).maskTransparency
if not maskTransparency:
maskTransparency = 0
if display:
ds9.setMaskTransparency(maskTransparency)
if display and displaySpatialCells:
diUtils.showKernelSpatialCells(exposure.getMaskedImage(),
kernelCellSet,
symb="o",
ctype=ds9.CYAN,
ctypeUnused=ds9.YELLOW,
ctypeBad=ds9.RED,
size=4,
frame=lsstDebug.frame,
title="Image to be convolved")
lsstDebug.frame += 1
return kernelCellSet
def applyVisitor(self, invert=False, xloc=397, yloc=580):
print '# %.2f %.2f' % (xloc, yloc)
#if xloc != 1312 and yloc != 160:
# return
imsize = int(3 * self.subconfig1.kernelSize)
# chop out a region around a known object
bbox = afwGeom.Box2I(
afwGeom.Point2I(xloc - imsize / 2, yloc - imsize / 2),
afwGeom.Point2I(xloc + imsize / 2, yloc + imsize / 2))
# sometimes the box goes off the image; no big deal...
try:
if invert:
tmi = afwImage.MaskedImageF(
self.scienceExposure.getMaskedImage(), bbox,
afwImage.LOCAL)
smi = afwImage.MaskedImageF(
self.templateExposure.getMaskedImage(), bbox,
afwImage.LOCAL)
else:
smi = afwImage.MaskedImageF(
self.scienceExposure.getMaskedImage(), bbox,
afwImage.LOCAL)
tmi = afwImage.MaskedImageF(
self.templateExposure.getMaskedImage(), bbox,
afwImage.LOCAL)
except Exception:
return None
# delta function kernel
logging.Trace("lsst.ip.diffim.compareLambdaTypes", 1, 'DF run')
results1 = self.apply(pexConfig.makePolicy(self.subconfig1),
self.bskv1, xloc, yloc, tmi, smi)
kSum1, bg1, dmean1, dstd1, vmean1, kImageOut1, diffIm1, kc1 = results1
res = 'DF residuals : %.3f +/- %.3f; %.2f, %.2f; %.2f %.2f, %.2f' % (
self.dStats.getMean(), self.dStats.getRms(), kSum1, bg1, dmean1,
dstd1, vmean1)
logging.Trace("lsst.ip.diffim.compareLambdaTypes", 1, res)
if display:
ds9.mtv(tmi, frame=1) # ds9 switches frame 0 and 1 for some reason
ds9.mtv(smi, frame=0)
ds9.mtv(kImageOut1, frame=2)
ds9.mtv(diffIm1, frame=3)
if writefits:
tmi.writeFits('t.fits')
smi.writeFits('s.fits')
kImageOut1.writeFits('k1.fits')
diffIm1.writeFits('d1.fits')
# regularized delta function kernel
logging.Trace("lsst.ip.diffim.compareLambdaTypes", 1, 'DFrC5 run')
results2 = self.apply(pexConfig.makePolicy(self.subconfig2),
self.bskv2, xloc, yloc, tmi, smi)
kSum2, bg2, dmean2, dstd2, vmean2, kImageOut2, diffIm2, kc2 = results2
res = 'DFrC5 residuals : %.3f +/- %.3f; %.2f, %.2f; %.2f %.2f, %.2f' % (
self.dStats.getMean(), self.dStats.getRms(), kSum2, bg2, dmean2,
dstd2, vmean2)
logging.Trace("lsst.ip.diffim.compareLambdaTypes", 1, res)
if display:
ds9.mtv(tmi, frame=4)
ds9.mtv(smi, frame=5)
ds9.mtv(kImageOut2, frame=6)
ds9.mtv(diffIm2, frame=7)
if writefits:
kImageOut2.writeFits('k2.fits')
diffIm2.writeFits('d2')
# regularized delta function kernel
logging.Trace("lsst.ip.diffim.compareLambdaTypes", 1, 'DFrC9 run')
results3 = self.apply(pexConfig.makePolicy(self.subconfig3),
self.bskv3, xloc, yloc, tmi, smi)
kSum3, bg3, dmean3, dstd3, vmean3, kImageOut3, diffIm3, kc3 = results3
res = 'DFrC9 residuals : %.3f +/- %.3f; %.2f, %.2f; %.2f %.2f, %.2f' % (
self.dStats.getMean(), self.dStats.getRms(), kSum3, bg3, dmean3,
dstd3, vmean3)
logging.Trace("lsst.ip.diffim.compareLambdaTypes", 1, res)
if display:
ds9.mtv(tmi, frame=8)
ds9.mtv(smi, frame=9)
ds9.mtv(kImageOut3, frame=10)
ds9.mtv(diffIm3, frame=11)
if writefits:
kImageOut2.writeFits('k3.fits')
diffIm2.writeFits('d3')
# regularized delta function kernel
logging.Trace("lsst.ip.diffim.compareLambdaTypes", 1, 'DFrF12 run')
results4 = self.apply(pexConfig.makePolicy(self.subconfig4),
self.bskv4, xloc, yloc, tmi, smi)
kSum4, bg4, dmean4, dstd4, vmean4, kImageOut4, diffIm4, kc4 = results4
res = 'DFrF12 residuals : %.3f +/- %.3f; %.2f, %.2f; %.2f %.2f, %.2f' % (
self.dStats.getMean(), self.dStats.getRms(), kSum4, bg4, dmean4,
dstd4, vmean4)
logging.Trace("lsst.ip.diffim.compareLambdaTypes", 1, res)
if display:
ds9.mtv(tmi, frame=12)
ds9.mtv(smi, frame=13)
ds9.mtv(kImageOut4, frame=14)
ds9.mtv(diffIm4, frame=15)
if writefits:
kImageOut2.writeFits('k4.fits')
diffIm2.writeFits('d4')
raw_input('Next: ')
def detection(differenceImageExposure,
policy,
filterId,
useLog=None,
footprintList=None):
"""Detect and measure objects in an incoming difference image Exposure
Inputs:
- differenceImageExposure: an lsst.afw.image.Exposure containing a difference MaskedImage and WCS
- policy: the policy; required elements are...?
- footprintList: a sequence of detection footprints on which to force measurement
Returns:
- an lsst.afw.detection.SourceVector
"""
if not (useLog):
useLog = ScreenLog()
useLog.setScreenVerbose(True)
logging.Trace("lsst.detection.detection", 3, "filterId = %d" % (filterId))
###########
#
# Get directives from policy
#
thresh = policy.get('thresholdSigma')
nPixMin = policy.get('numPixMinFootprint')
###########
#
# Unpack the MaskedImage from the Exposure
#
img = differenceImageExposure.getMaskedImage()
###########
#
# Crudely estimate noise from mean of variance image - should do sigma clipping
#
varImg = img.getVariance()
noise = math.sqrt(afwImage.mean_channel_value(varImg))
logging.Trace(
"lsst.detection.detection", 3,
"thresholdSigma = %r; noise = %r PixMin = %r" %
(thresh, noise, nPixMin))
LogRec(useLog, Log.INFO) \
<< "Threshold computation" \
<< DataProperty("thresholdSigma", thresh) \
<< DataProperty("noise", noise) \
<< DataProperty("threshold", thresh*noise) \
<< LogRec.endr
###########
#
# Build the DetectionSet for positive sources
#
dsPositive = det.DetectionSetF(
img, det.Threshold(thresh * noise, det.Threshold.VALUE, True), "FP+",
nPixMin)
fpVecPositive = dsPositive.getFootprints()
print "Positive detections: ", len(fpVecPositive)
LogRec(useLog, Log.INFO) \
<< "Positive detections" \
<< DataProperty("nPositive", len(fpVecPositive)) \
<< LogRec.endr
###########
#
# Build the DetectionSet for negative sources
#
dsNegative = det.DetectionSetF(
img, det.Threshold(thresh * noise, det.Threshold.VALUE, False), "FP-",
nPixMin)
fpVecNegative = dsNegative.getFootprints()
print "Negative detections: ", len(fpVecNegative)
LogRec(useLog, Log.INFO) \
<< "Negative detections" \
<< DataProperty("nNegative", len(fpVecNegative)) \
<< LogRec.endr
###########
#
# Measure the FootPrints
#
imgWCS = differenceImageExposure.getWcs()
outputDiaSources = afwDet.DiaSourceVec()
imgMeasure = det.MeasureF(img, "FP+")
id = 0
for i in range(len(fpVecPositive)):
diaPtr = afwDet.DiaSourcePtr()
diaPtr.setId(id)
diaPtr.setFilterId(filterId)
imgMeasure.measureSource(
diaPtr, fpVecPositive[i],
0.0) # NOTE explicit background of zero used for difference image
pixCoord = afwImage.Coord2D(diaPtr.getColc(), diaPtr.getRowc())
skyCoord = imgWCS.colRowToRaDec(pixCoord)
diaPtr.setRa(skyCoord.x())
diaPtr.setDec(skyCoord.y())
outputDiaSources.push_back(diaPtr.get())
id += 1
imgMeasure = det.MeasureF(img, "FP-")
for i in range(len(fpVecNegative)):
diaPtr = afwDet.DiaSourcePtr()
diaPtr.setId(id)
diaPtr.setFilterId(filterId)
imgMeasure.measureSource(
diaPtr, fpVecNegative[i],
0.0) # NOTE explicit background of zero used for difference image
pixCoord = afwImage.Coord2D(diaPtr.getColc(), diaPtr.getRowc())
skyCoord = imgWCS.colRowToRaDec(pixCoord)
diaPtr.setRa(skyCoord.x())
diaPtr.setDec(skyCoord.y())
outputDiaSources.push_back(diaPtr.get())
id += 1
###########
#
# Return the DiaSources
#
return outputDiaSources
def matchExposures(self,
templateExposure,
scienceExposure,
templateFwhmPix=None,
scienceFwhmPix=None,
candidateList=None,
doWarping=True,
convolveTemplate=True):
"""!Warp and PSF-match an exposure to the reference
Do the following, in order:
- Warp templateExposure to match scienceExposure,
if doWarping True and their WCSs do not already match
- Determine a PSF matching kernel and differential background model
that matches templateExposure to scienceExposure
- Convolve templateExposure by PSF matching kernel
@param templateExposure: Exposure to warp and PSF-match to the reference masked image
@param scienceExposure: Exposure whose WCS and PSF are to be matched to
@param templateFwhmPix: FWHM (in pixels) of the Psf in the template image (image to convolve)
@param scienceFwhmPix: FWHM (in pixels) of the Psf in the science image
@param candidateList: a list of footprints/maskedImages for kernel candidates;
if None then source detection is run.
- Currently supported: list of Footprints or measAlg.PsfCandidateF
@param doWarping: what to do if templateExposure's and scienceExposure's WCSs do not match:
- if True then warp templateExposure to match scienceExposure
- if False then raise an Exception
@param convolveTemplate: convolve the template image or the science image
- if True, templateExposure is warped if doWarping, templateExposure is convolved
- if False, templateExposure is warped if doWarping, scienceExposure is convolved
@return a pipeBase.Struct containing these fields:
- matchedImage: the PSF-matched exposure =
warped templateExposure convolved by psfMatchingKernel. This has:
- the same parent bbox, Wcs and Calib as scienceExposure
- the same filter as templateExposure
- no Psf (because the PSF-matching process does not compute one)
- psfMatchingKernel: the PSF matching kernel
- backgroundModel: differential background model
- kernelCellSet: SpatialCellSet used to solve for the PSF matching kernel
Raise a RuntimeError if doWarping is False and templateExposure's and scienceExposure's
WCSs do not match
"""
if not self._validateWcs(templateExposure, scienceExposure):
if doWarping:
self.log.info(
"Astrometrically registering template to science image")
templatePsf = templateExposure.getPsf()
templateExposure = self._warper.warpExposure(
scienceExposure.getWcs(),
templateExposure,
destBBox=scienceExposure.getBBox())
templateExposure.setPsf(templatePsf)
else:
pexLog.Trace(self.log.getName(), 1,
"ERROR: Input images not registered")
raise RuntimeError("Input images not registered")
if templateFwhmPix is None:
if not templateExposure.hasPsf():
self.log.warn("No estimate of Psf FWHM for template image")
else:
templateFwhmPix = self.getFwhmPix(templateExposure.getPsf())
if scienceFwhmPix is None:
if not scienceExposure.hasPsf():
self.log.warn("No estimate of Psf FWHM for science image")
else:
scienceFwhmPix = self.getFwhmPix(scienceExposure.getPsf())
kernelSize = makeKernelBasisList(self.kConfig, templateFwhmPix,
scienceFwhmPix)[0].getWidth()
candidateList = self.makeCandidateList(templateExposure,
scienceExposure, kernelSize,
candidateList)
if convolveTemplate:
results = self.matchMaskedImages(templateExposure.getMaskedImage(),
scienceExposure.getMaskedImage(),
candidateList,
templateFwhmPix=templateFwhmPix,
scienceFwhmPix=scienceFwhmPix)
else:
results = self.matchMaskedImages(scienceExposure.getMaskedImage(),
templateExposure.getMaskedImage(),
candidateList,
templateFwhmPix=scienceFwhmPix,
scienceFwhmPix=templateFwhmPix)
psfMatchedExposure = afwImage.makeExposure(results.matchedImage,
scienceExposure.getWcs())
psfMatchedExposure.setFilter(templateExposure.getFilter())
psfMatchedExposure.setCalib(scienceExposure.getCalib())
results.warpedExposure = templateExposure
results.matchedExposure = psfMatchedExposure
return results
def generateAlardLuptonBasisList(config,
targetFwhmPix=None,
referenceFwhmPix=None,
basisDegGauss=None,
metadata=None):
"""Generate an Alard-Lupton kernel basis based upon the Config and
the input FWHM of the science and template images"""
if config.kernelBasisSet != "alard-lupton":
raise RuntimeError(
"Cannot generate %s basis within generateAlardLuptonBasisList" %
config.kernelBasisSet)
kernelSize = config.kernelSize
fwhmScaling = config.kernelSizeFwhmScaling
basisNGauss = config.alardNGauss
basisSigmaGauss = config.alardSigGauss
basisGaussBeta = config.alardGaussBeta
basisMinSigma = config.alardMinSig
if basisDegGauss is None:
basisDegGauss = config.alardDegGauss
if len(basisDegGauss) != basisNGauss:
raise ValueError("len(basisDegGauss) != basisNGauss : %d vs %d" %
(len(basisDegGauss), basisNGauss))
if len(basisSigmaGauss) != basisNGauss:
raise ValueError("len(basisSigmaGauss) != basisNGauss : %d vs %d" %
(len(basisSigmaGauss), basisNGauss))
if (kernelSize % 2) != 1:
raise ValueError("Only odd-sized Alard-Lupton bases allowed")
if (targetFwhmPix is None) or (referenceFwhmPix is
None) or (not config.scaleByFwhm):
if metadata is not None:
metadata.add("ALBasisNGauss", basisNGauss)
metadata.add("ALBasisDegGauss", basisDegGauss)
metadata.add("ALBasisSigGauss", basisSigmaGauss)
metadata.add("ALKernelSize", kernelSize)
return diffimLib.makeAlardLuptonBasisList(kernelSize // 2, basisNGauss,
basisSigmaGauss,
basisDegGauss)
targetSigma = targetFwhmPix / sigma2fwhm
referenceSigma = referenceFwhmPix / sigma2fwhm
pexLog.Trace(
"lsst.ip.diffim.generateAlardLuptonBasisList", 2,
"Generating matching bases for sigma %.2f pix -> %.2f pix" %
(targetSigma, referenceSigma))
# Modify the size of Alard Lupton kernels based upon the images FWHM
#
# Note the operation is : template.x.kernel = science
#
# Assuming the template and science image Psfs are Gaussians with
# the Fwhm above, Fwhm_T **2 + Fwhm_K **2 = Fwhm_S **2
#
if targetSigma == referenceSigma:
# Leave defaults as-is
pass
elif referenceSigma > targetSigma:
# Normal convolution
# First Gaussian has the sigma that comes from the convolution
# of two Gaussians : Sig_S**2 = Sig_T**2 + Sig_K**2
#
# If it's larger than basisMinSigma * basisGaussBeta, make it the
# second kernel. Else make it the smallest kernel. Unless
# only 1 kernel is asked for.
kernelSigma = np.sqrt(referenceSigma**2 - targetSigma**2)
if kernelSigma < basisMinSigma:
kernelSigma = basisMinSigma
basisSigmaGauss = []
if basisNGauss == 1:
basisSigmaGauss.append(kernelSigma)
nAppended = 1
else:
if (kernelSigma / basisGaussBeta) > basisMinSigma:
basisSigmaGauss.append(kernelSigma / basisGaussBeta)
basisSigmaGauss.append(kernelSigma)
nAppended = 2
else:
basisSigmaGauss.append(kernelSigma)
nAppended = 1
# Any other Gaussians above basisNGauss=1 come from a scaling
# relationship: Sig_i+1 / Sig_i = basisGaussBeta
for i in range(nAppended, basisNGauss):
basisSigmaGauss.append(basisSigmaGauss[-1] * basisGaussBeta)
kernelSize = int(fwhmScaling * basisSigmaGauss[-1])
kernelSize += 0 if kernelSize % 2 else 1 # Make sure it's odd
kernelSize = min(config.kernelSizeMax,
max(kernelSize, config.kernelSizeMin))
else:
# Deconvolution; Define the progression of Gaussians using a
# method to derive a deconvolution sum-of-Gaussians from it's
# convolution counterpart. Only use 3 since the algorithm
# assumes 3 components.
#
# http://iopscience.iop.org/0266-5611/26/8/085002 Equation 40
# Use specializations for deconvolution
basisNGauss = config.alardNGaussDeconv
basisMinSigma = config.alardMinSigDeconv
kernelSigma = np.sqrt(targetSigma**2 - referenceSigma**2)
if kernelSigma < basisMinSigma:
kernelSigma = basisMinSigma
basisSigmaGauss = []
if (kernelSigma / basisGaussBeta) > basisMinSigma:
basisSigmaGauss.append(kernelSigma / basisGaussBeta)
basisSigmaGauss.append(kernelSigma)
nAppended = 2
else:
basisSigmaGauss.append(kernelSigma)
nAppended = 1
for i in range(nAppended, basisNGauss):
basisSigmaGauss.append(basisSigmaGauss[-1] * basisGaussBeta)
kernelSize = int(fwhmScaling * basisSigmaGauss[-1])
kernelSize += 0 if kernelSize % 2 else 1 # Make sure it's odd
kernelSize = min(config.kernelSizeMax,
max(kernelSize, config.kernelSizeMin))
# Now build a deconvolution set from these sigmas
sig0 = basisSigmaGauss[0]
sig1 = basisSigmaGauss[1]
sig2 = basisSigmaGauss[2]
basisSigmaGauss = []
for n in range(1, 3):
for j in range(n):
sigma2jn = (n - j) * sig1**2
sigma2jn += j * sig2**2
sigma2jn -= (n + 1) * sig0**2
sigmajn = np.sqrt(sigma2jn)
basisSigmaGauss.append(sigmajn)
basisSigmaGauss.sort()
basisNGauss = len(basisSigmaGauss)
basisDegGauss = [config.alardDegGaussDeconv for x in basisSigmaGauss]
if metadata is not None:
metadata.add("ALBasisNGauss", basisNGauss)
metadata.add("ALBasisDegGauss", basisDegGauss)
metadata.add("ALBasisSigGauss", basisSigmaGauss)
metadata.add("ALKernelSize", kernelSize)
return diffimLib.makeAlardLuptonBasisList(kernelSize // 2, basisNGauss,
basisSigmaGauss, basisDegGauss)
def testSource(self, source, subMi):
imArr, maArr, varArr = subMi.getArrays()
flux = source.getApFlux()
nPixels = subMi.getWidth() * subMi.getHeight()
nPos, nNeg, fPos, fNeg = self.countPolarity(maArr, imArr)
nDetPos, nDetNeg = self.countDetected(maArr)
nMasked = self.countMasked(maArr)
assert (nPixels == (nMasked + nPos + nNeg))
# 1) Too many pixels in the detection are masked
fMasked = (nMasked / nPixels)
fMaskedTol = self.config.fBadPixels
if fMasked > fMaskedTol:
pexLog.Trace(
"lsst.ip.diffim.DiaSourceAnalysis", 1,
"Candidate %d : BAD fBadPixels %.2f > %.2f" %
(source.getId(), fMasked, fMaskedTol))
return False
if flux > 0:
# positive-going source
fluxRatio = fPos / (fPos + abs(fNeg))
ngoodRatio = nPos / nPixels
maskRatio = nPos / (nPos + nMasked)
npolRatio = nPos / (nPos + nNeg)
else:
# negative-going source
fluxRatio = abs(fNeg) / (fPos + abs(fNeg))
ngoodRatio = nNeg / nPixels
maskRatio = nNeg / (nNeg + nMasked)
npolRatio = nNeg / (nNeg + nPos)
# 2) Not enough flux in unmasked correct-polarity pixels
fluxRatioTolerance = self.config.fluxPolarityRatio
if fluxRatio < fluxRatioTolerance:
pexLog.Trace(
"lsst.ip.diffim.DiaSourceAnalysis", 1,
"Candidate %d : BAD flux polarity %.2f < %.2f (pos=%.2f neg=%.2f)"
% (source.getId(), fluxRatio, fluxRatioTolerance, fPos, fNeg))
return False
# 3) Not enough unmasked pixels of correct polarity
polarityTolerance = self.config.nPolarityRatio
if npolRatio < polarityTolerance:
pexLog.Trace(
"lsst.ip.diffim.DiaSourceAnalysis", 1,
"Candidate %d : BAD polarity count %.2f < %.2f (pos=%d neg=%d)"
% (source.getId(), npolRatio, polarityTolerance, nPos, nNeg))
return False
# 4) Too many masked vs. correct polarity pixels
maskedTolerance = self.config.nMaskedRatio
if maskRatio < maskedTolerance:
pexLog.Trace(
"lsst.ip.diffim.DiaSourceAnalysis", 1,
"Candidate %d : BAD unmasked count %.2f < %.2f (pos=%d neg=%d mask=%d)"
% (source.getId(), maskRatio, maskedTolerance, nPos, nNeg,
nMasked))
return False
# 5) Too few unmasked, correct polarity pixels
ngoodTolerance = self.config.nGoodRatio
if ngoodRatio < ngoodTolerance:
pexLog.Trace(
"lsst.ip.diffim.DiaSourceAnalysis", 1,
"Candidate %d : BAD good pixel count %.2f < %.2f (pos=%d neg=%d tot=%d)"
% (source.getId(), ngoodRatio, ngoodTolerance, nPos, nNeg,
nPixels))
return False
pexLog.Trace(
"lsst.ip.diffim.DiaSourceAnalysis", 1,
"Candidate %d : OK flux=%.2f nPos=%d nNeg=%d nTot=%d nDetPos=%d nDetNeg=%d fPos=%.2f fNeg=%2f"
% (source.getId(), flux, nPos, nNeg, nPixels, nDetPos, nDetNeg,
fPos, fNeg))
return True
