import lsst.afw.detection as afwDetection
import lsst.afw.image as afwImage
import lsst.afw.geom as afwGeom
import lsst.afw.math as afwMath
import lsst.meas.algorithms as measAlg
import lsst.meas.algorithms.utils as maUtils
import lsst.afw.display.ds9 as ds9
import lsst.afw.display.utils as displayUtils
try:
type(verbose)
except NameError:
verbose = 0
display = False
pexLog.Trace_setVerbosity("meas.algorithms.measure", verbose)
#reload(lsst.meas.algorithms.Psf); Psf = lsst.meas.algorithms.Psf
#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
class PsfShapeHistogram(object):
"""A class to represent a histogram of (Ixx, Iyy)"""
def __init__(self):
self._xSize, self._ySize = 20, 20
self._xMax, self._yMax = 15, 15
self._psfImage = afwImage.ImageF(self._xSize, self._ySize)
self._psfImage.set(0)
def getImage(self):
import lsst.afw.geom as afwGeom
import lsst.afw.geom.ellipses as afwEllipses
import lsst.afw.math as afwMath
import lsst.afw.table as afwTable
import lsst.afw.coord as afwCoord
import lsst.afw.image as afwImage
import lsst.meas.algorithms as measAlg
import lsst.meas.extensions.photometryKron as Kron
try:
type(verbose)
except NameError:
verbose = 1
display = False
ds9Frame = 0
pexLogging.Trace_setVerbosity("meas.photometry.kron", verbose)
import lsst.afw.display.ds9 as ds9
import lsst.afw.display.utils as displayUtils
#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
def makeGalaxy(width, height, flux, a, b, theta, dx=0.0, dy=0.0, xy0=None, xcen=None, ycen=None):
"""Make a fake galaxy image"""
gal = afwImage.ImageF(width, height)
if xcen is None:
xcen = 0.5*width + dx
if ycen is None:
ycen = 0.5*height + dy
I0 = flux/(2*math.pi*a*b)
import lsst.pex.exceptions as pexExceptions
import lsst.pex.logging as pexLogging
import lsst.pex.policy as pexPolicy
import lsst.afw.detection as afwDetection
import lsst.afw.math as afwMath
import lsst.afw.geom as afwGeom
import lsst.afw.table as afwTable
import lsst.afw.image as afwImage
import lsst.afw.coord as afwCoord
import lsst.meas.algorithms as measAlg
try:
type(verbose)
except NameError:
verbose = 0
pexLogging.Trace_setVerbosity("afwDetection.Measure", verbose)
try:
type(display)
except NameError:
display = False
import lsst.afw.display.ds9 as ds9
FwhmPerSigma = 2 * math.sqrt(2 * math.log(2)) # FWHM for an N(0, 1) Gaussian
#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
class MeasureSourcesTestCase(unittest.TestCase):
"""A test case for Measure"""
import lsst.afw.image as afwImage
import lsst.afw.math as afwMath
import lsst.ip.diffim as ipDiffim
import lsst.ip.diffim.diffimTools as diffimTools
import lsst.pex.logging as pexLogging
import lsst.afw.display.ds9 as ds9
import lsst.afw.display.utils as displayUtils
subBackground = True
display = True
fwhm = 6.8
warp = True
verbosity = 5
pexLogging.Trace_setVerbosity("lsst.ip.diffim", verbosity)
defDataDir = lsst.utils.getPackageDir('afwdata')
imageProcDir = lsst.utils.getPackageDir('ip_diffim')
if len(sys.argv) == 1:
defSciencePath = os.path.join(defDataDir, "DC3a-Sim", "sci", "v26-e0",
"v26-e0-c011-a10.sci")
defTemplatePath = os.path.join(defDataDir, "DC3a-Sim", "sci", "v5-e0",
"v5-e0-c011-a10.sci")
elif len(sys.argv) == 3:
defSciencePath = sys.argv[1]
defTemplatePath = sys.argv[2]
else:
sys.exit(1)
print '# Reading', fn,
loc = dafPersist.LogicalLocation(fn)
storageList = dafPersist.StorageList()
additionalData = dafBase.PropertySet()
persistence = dafPersist.Persistence.getPersistence(pexPolicy.Policy())
storageList.append(persistence.getRetrieveStorage("BoostStorage", loc))
psfptr = persistence.unsafeRetrieve("Psf", storageList, additionalData)
psf = afwDet.Psf.swigConvert(psfptr)
return psf
if __name__ == '__main__':
import lsst.ip.diffim.diffimTools as diffimTools
pexLog.Trace_setVerbosity("lsst.ip.diffim", 5)
calexpPath = sys.argv[1]
boostPath = sys.argv[2]
sigGauss = float(sys.argv[3])
calexp = afwImage.ExposureF(calexpPath)
psf = pcapsf_read_boost(boostPath)
if not calexp.hasPsf():
calexp.setPsf(psf)
# match to this
gaussPsf = measAlg.DoubleGaussianPsf(psf.getKernel().getWidth(),
psf.getKernel().getHeight(), sigGauss)
config = ipDiffim.ModelPsfMatchTask.ConfigClass()
import lsst.utils
import lsst.utils.tests as tests
import lsst.pex.config as pexConfig
import lsst.pex.logging as logging
import lsst.afw.image as afwImage
import lsst.afw.math as afwMath
import lsst.afw.geom as afwGeom
import lsst.afw.display.ds9 as ds9
import lsst.meas.algorithms as algorithms
import lsst.meas.algorithms.defects as defects
try:
type(verbose)
except NameError:
verbose = 0
logging.Trace_setVerbosity("algorithms.CR", verbose)
try:
type(display)
except NameError:
display = False
try:
afwdataDir = lsst.utils.getPackageDir('afwdata')
imageFile0 = os.path.join(afwdataDir, "CFHT", "D4", "cal-53535-i-797722_1.fits")
except Exception:
imageFile0 = None
imageFile = imageFile0
#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
#!/usr/bin/env python
import unittest
import numpy as num
import lsst.utils.tests as tests
import lsst.afw.geom as afwGeom
import lsst.afw.image as afwImage
import lsst.afw.math as afwMath
import lsst.ip.diffim as ipDiffim
import lsst.pex.logging as pexLog
import lsst.pex.config as pexConfig
#import lsst.afw.display.ds9 as ds9
pexLog.Trace_setVerbosity('lsst.ip.diffim', 5)
class DiffimTestCases(unittest.TestCase):
def setUp(self):
self.config = ipDiffim.ImagePsfMatchTask.ConfigClass()
self.config.kernel.name = "AL"
self.subconfig = self.config.kernel.active
self.policy = pexConfig.makePolicy(self.subconfig)
self.kList = ipDiffim.makeKernelBasisList(self.subconfig)
self.ksize = self.policy.get('kernelSize')
def makeSpatialKernel(self, order):
basicGaussian1 = afwMath.GaussianFunction2D(2., 2., 0.)
class ImagePsfMatchTask(PsfMatchTask):
"""!
\anchor ImagePsfMatchTask_
\brief Psf-match two MaskedImages or Exposures using the sources in the images
\section ip_diffim_imagepsfmatch_Contents Contents
- \ref ip_diffim_imagepsfmatch_Purpose
- \ref ip_diffim_imagepsfmatch_Initialize
- \ref ip_diffim_imagepsfmatch_IO
- \ref ip_diffim_imagepsfmatch_Config
- \ref ip_diffim_imagepsfmatch_Metadata
- \ref ip_diffim_imagepsfmatch_Debug
- \ref ip_diffim_imagepsfmatch_Example
#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
\section ip_diffim_imagepsfmatch_Purpose Description
Build a Psf-matching kernel using two input images, either as MaskedImages (in which case they need
to be astrometrically aligned) or Exposures (in which case astrometric alignment will happen by
default but may be turned off). This requires a list of input Sources which may be provided
by the calling Task; if not, the Task will perform a coarse source detection and selection for this purpose.
Sources are vetted for signal-to-noise and masked pixels (in both the template and science image), and
substamps around each acceptable source are extracted and used to create an instance of KernelCandidate.
Each KernelCandidate is then placed within a lsst.afw.math.SpatialCellSet, which is used by an ensemble of
lsst.afw.math.CandidateVisitor instances to build the Psf-matching kernel. These visitors include, in
the order that they are called: BuildSingleKernelVisitor, KernelSumVisitor, BuildSpatialKernelVisitor,
and AssessSpatialKernelVisitor.
Sigma clipping of KernelCandidates is performed as follows:
- BuildSingleKernelVisitor, using the substamp diffim residuals from the per-source kernel fit,
if PsfMatchConfig.singleKernelClipping is True
- KernelSumVisitor, using the mean and standard deviation of the kernel sum from all candidates,
if PsfMatchConfig.kernelSumClipping is True
- AssessSpatialKernelVisitor, using the substamp diffim ressiduals from the spatial kernel fit,
if PsfMatchConfig.spatialKernelClipping is True
The actual solving for the kernel (and differential background model) happens in
lsst.ip.diffim.PsfMatchTask._solve. This involves a loop over the SpatialCellSet that first builds the
per-candidate matching kernel for the requested number of KernelCandidates per cell
(PsfMatchConfig.nStarPerCell). The quality of this initial per-candidate difference image is examined,
using moments of the pixel residuals in the difference image normalized by the square root of the variance
(i.e. sigma); ideally this should follow a normal (0, 1) distribution, but the rejection thresholds are set
by the config (PsfMatchConfig.candidateResidualMeanMax and PsfMatchConfig.candidateResidualStdMax).
All candidates that pass this initial build are then examined en masse to find the
mean/stdev of the kernel sums across all candidates. Objects that are significantly above or below the mean,
typically due to variability or sources that are saturated in one image but not the other, are also rejected.
This threshold is defined by PsfMatchConfig.maxKsumSigma. Finally, a spatial model is built using all
currently-acceptable candidates, and the spatial model used to derive a second set of (spatial) residuals
which are again used to reject bad candidates, using the same thresholds as above.
#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
\section ip_diffim_imagepsfmatch_Initialize Task initialization
\copydoc \_\_init\_\_
#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
\section ip_diffim_imagepsfmatch_IO Invoking the Task
There is no run() method for this Task. Instead there are 4 methods that
may be used to invoke the Psf-matching. These are
\link lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.matchMaskedImages matchMaskedImages\endlink,
\link lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.subtractMaskedImages subtractMaskedImages\endlink,
\link lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.matchExposures matchExposures\endlink, and
\link lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.subtractExposures subtractExposures\endlink.
The methods that operate on lsst.afw.image.MaskedImage require that the images already be astrometrically
aligned, and are the same shape. The methods that operate on lsst.afw.image.Exposure allow for the
input images to be misregistered and potentially be different sizes; by default a
lsst.afw.math.LanczosWarpingKernel is used to perform the astrometric alignment. The methods
that "match" images return a Psf-matched image, while the methods that "subtract" images
return a Psf-matched and template subtracted image.
See each method's returned lsst.pipe.base.Struct for more details.
#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
\section ip_diffim_imagepsfmatch_Config Configuration parameters
See \ref ImagePsfMatchConfig
#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
\section ip_diffim_imagepsfmatch_Metadata Quantities set in Metadata
See \ref ip_diffim_psfmatch_Metadata "PsfMatchTask"
#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
\section ip_diffim_imagepsfmatch_Debug Debug variables
The \link lsst.pipe.base.cmdLineTask.CmdLineTask command line task\endlink interface supports a
flag \c -d/--debug to import \b debug.py from your \c PYTHONPATH. The relevant contents of debug.py
for this Task include:
\code{.py}
import sys
import lsstDebug
def DebugInfo(name):
di = lsstDebug.getInfo(name)
if name == "lsst.ip.diffim.psfMatch":
di.display = True # enable debug output
di.maskTransparency = 80 # ds9 mask transparency
di.displayCandidates = True # show all the candidates and residuals
di.displayKernelBasis = False # show kernel basis functions
di.displayKernelMosaic = True # show kernel realized across the image
di.plotKernelSpatialModel = False # show coefficients of spatial model
di.showBadCandidates = True # show the bad candidates (red) along with good (green)
elif name == "lsst.ip.diffim.imagePsfMatch":
di.display = True # enable debug output
di.maskTransparency = 30 # ds9 mask transparency
di.displayTemplate = True # show full (remapped) template
di.displaySciIm = True # show science image to match to
di.displaySpatialCells = True # show spatial cells
di.displayDiffIm = True # show difference image
di.showBadCandidates = True # show the bad candidates (red) along with good (green)
elif name == "lsst.ip.diffim.diaCatalogSourceSelector":
di.display = False # enable debug output
di.maskTransparency = 30 # ds9 mask transparency
di.displayExposure = True # show exposure with candidates indicated
di.pauseAtEnd = False # pause when done
return di
lsstDebug.Info = DebugInfo
lsstDebug.frame = 1
\endcode
Note that if you want addional logging info, you may add to your scripts:
\code{.py}
import lsst.pex.logging as pexLog
pexLog.Trace_setVerbosity('lsst.ip.diffim', 5)
\endcode
#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
\section ip_diffim_imagepsfmatch_Example A complete example of using ImagePsfMatchTask
This code is imagePsfMatchTask.py in the examples directory, and can be run as \em e.g.
\code
examples/imagePsfMatchTask.py --debug
examples/imagePsfMatchTask.py --debug --mode="matchExposures"
examples/imagePsfMatchTask.py --debug --template /path/to/templateExp.fits --science /path/to/scienceExp.fits
\endcode
\dontinclude imagePsfMatchTask.py
Create a subclass of ImagePsfMatchTask that allows us to either match exposures, or subtract exposures:
\skip MyImagePsfMatchTask
\until self.subtractExposures
And allow the user the freedom to either run the script in default mode, or point to their own images on disk.
Note that these images must be readable as an lsst.afw.image.Exposure:
\skip main
\until parse_args
We have enabled some minor display debugging in this script via the --debug option. However, if you
have an lsstDebug debug.py in your PYTHONPATH you will get additional debugging displays. The following
block checks for this script:
\skip args.debug
\until sys.stderr
\dontinclude imagePsfMatchTask.py
Finally, we call a run method that we define below. First set up a Config and modify some of the parameters.
E.g. use an "Alard-Lupton" sum-of-Gaussian basis, fit for a differential background, and use low order spatial
variation in the kernel and background:
\skip run(args)
\until spatialBgOrder
Make sure the images (if any) that were sent to the script exist on disk and are readable. If no images
are sent, make some fake data up for the sake of this example script (have a look at the code if you want
more details on generateFakeImages):
\skip requested
\until sizeCellY
Create and run the Task:
\skip Create
\until args.mode
And finally provide some optional debugging displays:
\skip args.debug
\until result.subtractedExposure
#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
"""
ConfigClass = ImagePsfMatchConfig
def __init__(self, *args, **kwargs):
"""!Create the ImagePsfMatchTask
\param *args arguments to be passed to lsst.ip.diffim.PsfMatchTask.__init__
\param **kwargs keyword arguments to be passed to lsst.ip.diffim.PsfMatchTask.__init__
Upon initialization, the kernel configuration is defined by self.config.kernel.active.
The task creates an lsst.afw.math.Warper from the subConfig self.config.kernel.active.warpingConfig.
A schema for the selection and measurement of candidate lsst.ip.diffim.KernelCandidates is
defined, and used to initize subTasks selectDetection (for candidate detection) and selectMeasurement
(for candidate measurement).
"""
PsfMatchTask.__init__(self, *args, **kwargs)
self.kConfig = self.config.kernel.active
self._warper = afwMath.Warper.fromConfig(self.kConfig.warpingConfig)
self.selectSchema = afwTable.SourceTable.makeMinimalSchema()
self.selectAlgMetadata = dafBase.PropertyList()
self.makeSubtask("selectDetection", schema=self.selectSchema)
self.makeSubtask("selectMeasurement",
schema=self.selectSchema,
algMetadata=self.selectAlgMetadata)
def getFwhmPix(self, psf):
"""!Return the FWHM in pixels of a Psf"""
sigPix = psf.computeShape().getDeterminantRadius()
return sigPix * sigma2fwhm
@pipeBase.timeMethod
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
@pipeBase.timeMethod
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,
)
@pipeBase.timeMethod
def subtractExposures(self,
templateExposure,
scienceExposure,
templateFwhmPix=None,
scienceFwhmPix=None,
candidateList=None,
doWarping=True,
convolveTemplate=True):
"""!Register, Psf-match and subtract two Exposures
Do the following, in order:
- Warp templateExposure to match scienceExposure, if their WCSs do not already match
- Determine a PSF matching kernel and differential background model
that matches templateExposure to scienceExposure
- PSF-match templateExposure to scienceExposure
- Compute subtracted exposure (see return values for equation).
@param templateExposure: exposure to PSF-match to scienceExposure
@param scienceExposure: reference Exposure
@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:
- subtractedExposure: subtracted Exposure = scienceExposure - (matchedImage + backgroundModel)
- matchedImage: templateExposure after warping to match templateExposure (if doWarping true),
and convolving with psfMatchingKernel
- psfMatchingKernel: PSF matching kernel
- backgroundModel: differential background model
- kernelCellSet: SpatialCellSet used to determine PSF matching kernel
"""
results = self.matchExposures(templateExposure=templateExposure,
scienceExposure=scienceExposure,
templateFwhmPix=templateFwhmPix,
scienceFwhmPix=scienceFwhmPix,
candidateList=candidateList,
doWarping=doWarping,
convolveTemplate=convolveTemplate)
subtractedExposure = afwImage.ExposureF(scienceExposure, True)
if convolveTemplate:
subtractedMaskedImage = subtractedExposure.getMaskedImage()
subtractedMaskedImage -= results.matchedExposure.getMaskedImage()
subtractedMaskedImage -= results.backgroundModel
else:
subtractedExposure.setMaskedImage(
results.warpedExposure.getMaskedImage())
subtractedMaskedImage = subtractedExposure.getMaskedImage()
subtractedMaskedImage -= results.matchedExposure.getMaskedImage()
subtractedMaskedImage -= results.backgroundModel
# Preserve polarity of differences
subtractedMaskedImage *= -1
# Place back on native photometric scale
subtractedMaskedImage /= results.psfMatchingKernel.computeImage(
afwImage.ImageD(results.psfMatchingKernel.getDimensions()),
False)
import lsstDebug
display = lsstDebug.Info(__name__).display
displayDiffIm = lsstDebug.Info(__name__).displayDiffIm
maskTransparency = lsstDebug.Info(__name__).maskTransparency
if not maskTransparency:
maskTransparency = 0
if display:
ds9.setMaskTransparency(maskTransparency)
if display and displayDiffIm:
ds9.mtv(templateExposure, frame=lsstDebug.frame, title="Template")
lsstDebug.frame += 1
ds9.mtv(results.matchedExposure,
frame=lsstDebug.frame,
title="Matched template")
lsstDebug.frame += 1
ds9.mtv(scienceExposure,
frame=lsstDebug.frame,
title="Science Image")
lsstDebug.frame += 1
ds9.mtv(subtractedExposure,
frame=lsstDebug.frame,
title="Difference Image")
lsstDebug.frame += 1
results.subtractedExposure = subtractedExposure
return results
@pipeBase.timeMethod
def subtractMaskedImages(self,
templateMaskedImage,
scienceMaskedImage,
candidateList,
templateFwhmPix=None,
scienceFwhmPix=None):
"""!Psf-match and subtract two MaskedImages
Do the following, in order:
- PSF-match templateMaskedImage to scienceMaskedImage
- Determine the differential background
- Return the difference: scienceMaskedImage -
((warped templateMaskedImage convolved with psfMatchingKernel) + backgroundModel)
@param templateMaskedImage: MaskedImage to PSF-match to scienceMaskedImage
@param scienceMaskedImage: reference MaskedImage
@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:
- subtractedMaskedImage = scienceMaskedImage - (matchedImage + backgroundModel)
- matchedImage: templateMaskedImage convolved with psfMatchingKernel
- psfMatchingKernel: PSF matching kernel
- backgroundModel: differential background model
- kernelCellSet: SpatialCellSet used to determine PSF matching kernel
"""
if not candidateList:
raise RuntimeError(
"Candidate list must be populated by makeCandidateList")
results = self.matchMaskedImages(
templateMaskedImage=templateMaskedImage,
scienceMaskedImage=scienceMaskedImage,
candidateList=candidateList,
templateFwhmPix=templateFwhmPix,
scienceFwhmPix=scienceFwhmPix,
)
subtractedMaskedImage = afwImage.MaskedImageF(scienceMaskedImage, True)
subtractedMaskedImage -= results.matchedImage
subtractedMaskedImage -= results.backgroundModel
results.subtractedMaskedImage = subtractedMaskedImage
import lsstDebug
display = lsstDebug.Info(__name__).display
displayDiffIm = lsstDebug.Info(__name__).displayDiffIm
maskTransparency = lsstDebug.Info(__name__).maskTransparency
if not maskTransparency:
maskTransparency = 0
if display:
ds9.setMaskTransparency(maskTransparency)
if display and displayDiffIm:
ds9.mtv(subtractedMaskedImage, frame=lsstDebug.frame)
lsstDebug.frame += 1
return results
def getSelectSources(self,
exposure,
sigma=None,
doSmooth=True,
idFactory=None):
"""!Get sources to use for Psf-matching
This method runs detection and measurement on an exposure.
The returned set of sources will be used as candidates for
Psf-matching.
@param exposure: Exposure on which to run detection/measurement
@param sigma: Detection threshold
@param doSmooth: Whether or not to smooth the Exposure with Psf before detection
@param idFactory: Factory for the generation of Source ids
@return source catalog containing candidates for the Psf-matching
"""
if idFactory:
table = afwTable.SourceTable.make(self.selectSchema, idFactory)
else:
table = afwTable.SourceTable.make(self.selectSchema)
mi = exposure.getMaskedImage()
imArr = mi.getImage().getArray()
maskArr = mi.getMask().getArray()
miArr = np.ma.masked_array(imArr, mask=maskArr)
try:
bkgd = getBackground(mi,
self.kConfig.afwBackgroundConfig).getImageF()
except Exception:
self.log.warn(
"Failed to get background model. Falling back to median background estimation"
)
bkgd = np.ma.extras.median(miArr)
#Take off background for detection
mi -= bkgd
try:
table.setMetadata(self.selectAlgMetadata)
detRet = self.selectDetection.makeSourceCatalog(table=table,
exposure=exposure,
sigma=sigma,
doSmooth=doSmooth)
selectSources = detRet.sources
self.selectMeasurement.run(measCat=selectSources,
exposure=exposure)
finally:
# Put back on the background in case it is needed down stream
mi += bkgd
del bkgd
return selectSources
def makeCandidateList(self,
templateExposure,
scienceExposure,
kernelSize,
candidateList=None):
"""!Make a list of acceptable KernelCandidates
Accept or generate a list of candidate sources for
Psf-matching, and examine the Mask planes in both of the
images for indications of bad pixels
@param templateExposure: Exposure that will be convolved
@param scienceExposure: Exposure that will be matched-to
@param kernelSize: Dimensions of the Psf-matching Kernel, used to grow detection footprints
@param candidateList: List of Sources to examine. Elements must be of type afw.table.Source
or a type that wraps a Source and has a getSource() method, such as
meas.algorithms.PsfCandidateF.
@return a list of dicts having a "source" and "footprint"
field for the Sources deemed to be appropriate for Psf
matching
"""
if candidateList is None:
candidateList = self.getSelectSources(scienceExposure)
if len(candidateList) < 1:
raise RuntimeError("No candidates in candidateList")
listTypes = set(type(x) for x in candidateList)
if len(listTypes) > 1:
raise RuntimeError("Candidate list contains mixed types: %s" %
[l for l in listTypes])
if not isinstance(candidateList[0], afwTable.SourceRecord):
try:
candidateList[0].getSource()
except Exception as e:
raise RuntimeError(
"Candidate List is of type: %s. " %
(type(candidateList[0])) +
"Can only make candidate list from list of afwTable.SourceRecords, "
+
"measAlg.PsfCandidateF or other type with a getSource() method: %s"
% (e))
candidateList = [c.getSource() for c in candidateList]
candidateList = diffimTools.sourceToFootprintList(
candidateList, templateExposure, scienceExposure, kernelSize,
self.kConfig.detectionConfig, self.log)
if len(candidateList) == 0:
raise RuntimeError(
"Cannot find any objects suitable for KernelCandidacy")
return candidateList
def _adaptCellSize(self, candidateList):
"""! NOT IMPLEMENTED YET"""
return self.kConfig.sizeCellX, self.kConfig.sizeCellY
def _buildCellSet(self, templateMaskedImage, scienceMaskedImage,
candidateList):
"""!Build a SpatialCellSet for use with the solve method
@param templateMaskedImage: MaskedImage to PSF-matched to scienceMaskedImage
@param scienceMaskedImage: reference MaskedImage
@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 kernelCellSet: a SpatialCellSet for use with self._solve
"""
if not candidateList:
raise RuntimeError(
"Candidate list must be populated by makeCandidateList")
sizeCellX, sizeCellY = self._adaptCellSize(candidateList)
# Object to store the KernelCandidates for spatial modeling
kernelCellSet = afwMath.SpatialCellSet(templateMaskedImage.getBBox(),
sizeCellX, sizeCellY)
policy = pexConfig.makePolicy(self.kConfig)
# Place candidates within the spatial grid
for cand in candidateList:
bbox = cand['footprint'].getBBox()
tmi = afwImage.MaskedImageF(templateMaskedImage, bbox)
smi = afwImage.MaskedImageF(scienceMaskedImage, bbox)
cand = diffimLib.makeKernelCandidate(cand['source'], tmi, smi,
policy)
self.log.logdebug(
"Candidate %d at %f, %f" %
(cand.getId(), cand.getXCenter(), cand.getYCenter()))
kernelCellSet.insertCandidate(cand)
return kernelCellSet
def _validateSize(self, templateMaskedImage, scienceMaskedImage):
"""!Return True if two image-like objects are the same size
"""
return templateMaskedImage.getDimensions(
) == scienceMaskedImage.getDimensions()
def _validateWcs(self, templateExposure, scienceExposure):
"""!Return True if the WCS of the two Exposures have the same origin and extent
"""
templateWcs = templateExposure.getWcs()
scienceWcs = scienceExposure.getWcs()
templateBBox = templateExposure.getBBox()
scienceBBox = scienceExposure.getBBox()
# LLC
templateOrigin = templateWcs.pixelToSky(
afwGeom.Point2D(templateBBox.getBegin()))
scienceOrigin = scienceWcs.pixelToSky(
afwGeom.Point2D(scienceBBox.getBegin()))
# URC
templateLimit = templateWcs.pixelToSky(
afwGeom.Point2D(templateBBox.getEnd()))
scienceLimit = scienceWcs.pixelToSky(
afwGeom.Point2D(scienceBBox.getEnd()))
self.log.info("Template Wcs : %f,%f -> %f,%f" %
(templateOrigin[0], templateOrigin[1], templateLimit[0],
templateLimit[1]))
self.log.info("Science Wcs : %f,%f -> %f,%f" %
(scienceOrigin[0], scienceOrigin[1], scienceLimit[0],
scienceLimit[1]))
templateBBox = afwGeom.Box2D(templateOrigin.getPosition(),
templateLimit.getPosition())
scienceBBox = afwGeom.Box2D(scienceOrigin.getPosition(),
scienceLimit.getPosition())
if not (templateBBox.overlaps(scienceBBox)):
raise RuntimeError("Input images do not overlap at all")
if ((templateOrigin.getPosition() != scienceOrigin.getPosition())
or (templateLimit.getPosition() != scienceLimit.getPosition())
or (templateExposure.getDimensions() !=
scienceExposure.getDimensions())):
return False
return True
import eups
import math, numpy
import lsst.utils.tests as tests
import lsst.pex.logging as logging
import lsst.afw.detection as afwDetection
import lsst.afw.image as afwImage
import lsst.afw.geom as afwGeom
import lsst.afw.display.ds9 as ds9
import lsst.meas.algorithms as algorithms
import lsst.meas.algorithms.defects as defects
try:
type(verbose)
except NameError:
verbose = 0
logging.Trace_setVerbosity("algorithms.Interp", verbose)
try:
type(display)
except NameError:
display = False
class interpolationTestCase(unittest.TestCase):
"""A test case for interpolation"""
def setUp(self):
self.FWHM = 5
self.psf = algorithms.DoubleGaussianPsf(15, 15, self.FWHM/(2*sqrt(2*log(2))))
maskedImageFile = os.path.join(eups.productDir("afwdata"), "CFHT", "D4", "cal-53535-i-797722_1.fits")
self.mi = afwImage.MaskedImageF(maskedImageFile)
if False: # use sub-image?
import unittest
import lsst.utils.tests as tests
import lsst.utils
import lsst.afw.geom as afwGeom
import lsst.afw.image as afwImage
import lsst.afw.math as afwMath
import lsst.ip.diffim as ipDiffim
import lsst.ip.diffim.diffimTools as diffimTools
import lsst.pex.logging as logging
import lsst.pex.config as pexConfig
import lsst.afw.display.ds9 as ds9
verbosity = 7
logging.Trace_setVerbosity('lsst.ip.diffim', verbosity)
display = True
writefits = False
# This one compares DeltaFunction kernels of different types; iterate lambdaVal for different strengths
CFHTTORUN = 'cal-53535-i-797722_1'
class DiffimTestCases(unittest.TestCase):
# D = I - (K.x.T + bg)
def setUp(self, CFHT=True):
lambdaValue = 1.0
import pdb # we may want to say pdb.set_trace()
import unittest
import numpy
import eups
import lsst.afw.image as afwImage
import lsst.afw.math as afwMath
import lsst.utils.tests as utilsTests
import lsst.pex.logging as pexLog
import lsst.pex.exceptions as pexEx
import lsst.coadd.kaiser as coaddKaiser
import lsst.afw.image.testUtils as imTestUtils
Verbosity = 0 # increase to see trace
pexLog.Trace_setVerbosity("lsst.coadd.kaiser", Verbosity)
dataDir = eups.productDir("afwdata")
if not dataDir:
raise RuntimeError("Must set up afwdata to run these tests")
InputImageNameSmall = "small_MI_img.fits"
currDir = os.path.abspath(os.path.dirname(__file__))
inFilePathSmallImage = os.path.join(dataDir, InputImageNameSmall)
#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
def refMedian(inArr):
"""Compute the median of an array of any shape.
"""
class ModelPsfMatchTask(PsfMatchTask):
"""!
\anchor ModelPsfMatchTask_
\brief Matching of two model Psfs, and application of the Psf-matching kernel to an input Exposure
\section ip_diffim_modelpsfmatch_Contents Contents
- \ref ip_diffim_modelpsfmatch_Purpose
- \ref ip_diffim_modelpsfmatch_Initialize
- \ref ip_diffim_modelpsfmatch_IO
- \ref ip_diffim_modelpsfmatch_Config
- \ref ip_diffim_modelpsfmatch_Metadata
- \ref ip_diffim_modelpsfmatch_Debug
- \ref ip_diffim_modelpsfmatch_Example
#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
\section ip_diffim_modelpsfmatch_Purpose Description
This Task differs from ImagePsfMatchTask in that it matches two Psf _models_, by realizing
them in an Exposure-sized SpatialCellSet and then inserting each Psf-image pair into KernelCandidates.
Because none of the pairs of sources that are to be matched should be invalid, all sigma clipping is
turned off in ModelPsfMatchConfig. And because there is no tracked _variance_ in the Psf images, the
debugging and logging QA info should be interpreted with caution.
One item of note is that the sizes of Psf models are fixed (e.g. its defined as a 21x21 matrix). When the
Psf-matching kernel is being solved for, the Psf "image" is convolved with each kernel basis function,
leading to a loss of information around the borders. This pixel loss will be problematic for the numerical
stability of the kernel solution if the size of the convolution kernel (set by ModelPsfMatchConfig.kernelSize)
is much bigger than: psfSize//2. Thus the sizes of Psf-model matching kernels are typically smaller
than their image-matching counterparts. If the size of the kernel is too small, the convolved stars will
look "boxy"; if the kernel is too large, the kernel solution will be "noisy". This is a trade-off that
needs careful attention for a given dataset.
The primary use case for this Task is in matching an Exposure to a constant-across-the-sky Psf model for the
purposes of image coaddition. It is important to note that in the code, the "template" Psf is the Psf
that the science image gets matched to. In this sense the order of template and science image are
reversed, compared to ImagePsfMatchTask, which operates on the template image.
#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
\section ip_diffim_modelpsfmatch_Initialize Task initialization
\copydoc \_\_init\_\_
#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
\section ip_diffim_modelpsfmatch_IO Invoking the Task
\copydoc run
#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
\section ip_diffim_modelpsfmatch_Config Configuration parameters
See \ref ModelPsfMatchConfig
#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
\section ip_diffim_modelpsfmatch_Metadata Quantities set in Metadata
See \ref ip_diffim_psfmatch_Metadata "PsfMatchTask"
#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
\section ip_diffim_modelpsfmatch_Debug Debug variables
The \link lsst.pipe.base.cmdLineTask.CmdLineTask command line task\endlink interface supports a
flag \c -d/--debug to import \b debug.py from your \c PYTHONPATH. The relevant contents of debug.py
for this Task include:
\code{.py}
import sys
import lsstDebug
def DebugInfo(name):
di = lsstDebug.getInfo(name)
if name == "lsst.ip.diffim.psfMatch":
di.display = True # global
di.maskTransparency = 80 # ds9 mask transparency
di.displayCandidates = True # show all the candidates and residuals
di.displayKernelBasis = False # show kernel basis functions
di.displayKernelMosaic = True # show kernel realized across the image
di.plotKernelSpatialModel = False # show coefficients of spatial model
di.showBadCandidates = True # show the bad candidates (red) along with good (green)
elif name == "lsst.ip.diffim.modelPsfMatch":
di.display = True # global
di.maskTransparency = 30 # ds9 mask transparency
di.displaySpatialCells = True # show spatial cells before the fit
return di
lsstDebug.Info = DebugInfo
lsstDebug.frame = 1
\endcode
Note that if you want addional logging info, you may add to your scripts:
\code{.py}
import lsst.pex.logging as pexLog
pexLog.Trace_setVerbosity('lsst.ip.diffim', 5)
\endcode
#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
\section ip_diffim_modelpsfmatch_Example A complete example of using ModelPsfMatchTask
This code is modelPsfMatchTask.py in the examples directory, and can be run as \em e.g.
\code
examples/modelPsfMatchTask.py
examples/modelPsfMatchTask.py --debug
examples/modelPsfMatchTask.py --debug --template /path/to/templateExp.fits --science /path/to/scienceExp.fits
\endcode
\dontinclude modelPsfMatchTask.py
Create a subclass of ModelPsfMatchTask that accepts two exposures. Note that the "template" exposure
contains the Psf that will get matched to, and the "science" exposure is the one that will be convolved:
\skip MyModelPsfMatchTask
\until return
And allow the user the freedom to either run the script in default mode, or point to their own images on disk.
Note that these images must be readable as an lsst.afw.image.Exposure:
\skip main
\until parse_args
We have enabled some minor display debugging in this script via the --debug option. However, if you
have an lsstDebug debug.py in your PYTHONPATH you will get additional debugging displays. The following
block checks for this script:
\skip args.debug
\until sys.stderr
\dontinclude modelPsfMatchTask.py
Finally, we call a run method that we define below. First set up a Config and modify some of the parameters.
In particular we don't want to "grow" the sizes of the kernel or KernelCandidates, since we are operating with
fixed--size images (i.e. the size of the input Psf models).
\skip run(args)
\until False
Make sure the images (if any) that were sent to the script exist on disk and are readable. If no images
are sent, make some fake data up for the sake of this example script (have a look at the code if you want
more details on generateFakeData):
\skip requested
\until sizeCellY
Display the two images if --debug:
\skip args.debug
\until Science
Create and run the Task:
\skip Create
\until result
And finally provide optional debugging display of the Psf-matched (via the Psf models) science image:
\skip args.debug
\until result.psfMatchedExposure
#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
"""
ConfigClass = ModelPsfMatchConfig
def __init__(self, *args, **kwargs):
"""!Create a ModelPsfMatchTask
\param *args arguments to be passed to lsst.ip.diffim.PsfMatchTask.__init__
\param **kwargs keyword arguments to be passed to lsst.ip.diffim.PsfMatchTask.__init__
Upon initialization, the kernel configuration is defined by self.config.kernel.active. This Task
does have a run() method, which is the default way to call the Task.
"""
PsfMatchTask.__init__(self, *args, **kwargs)
self.kConfig = self.config.kernel.active
@pipeBase.timeMethod
def run(self, exposure, referencePsfModel, kernelSum=1.0):
"""!Psf-match an exposure to a model Psf
@param exposure: Exposure to Psf-match to the reference Psf model;
it must return a valid PSF model via exposure.getPsf()
@param referencePsfModel: The Psf model to match to (an lsst.afw.detection.Psf)
@param kernelSum: A multipicative factor to apply to the kernel sum (default=1.0)
@return
- psfMatchedExposure: the Psf-matched Exposure. This has the same parent bbox, Wcs, Calib and
Filter as the input Exposure but no Psf. In theory the Psf should equal referencePsfModel but
the match is likely not exact.
- psfMatchingKernel: the spatially varying Psf-matching kernel
- kernelCellSet: SpatialCellSet used to solve for the Psf-matching kernel
Raise a RuntimeError if the Exposure does not contain a Psf model
"""
if not exposure.hasPsf():
raise RuntimeError("exposure does not contain a Psf model")
maskedImage = exposure.getMaskedImage()
self.log.log(pexLog.Log.INFO, "compute Psf-matching kernel")
kernelCellSet = self._buildCellSet(exposure, referencePsfModel)
width, height = referencePsfModel.getLocalKernel().getDimensions()
psfAttr1 = measAlg.PsfAttributes(exposure.getPsf(), width // 2,
height // 2)
psfAttr2 = measAlg.PsfAttributes(referencePsfModel, width // 2,
height // 2)
s1 = psfAttr1.computeGaussianWidth(
psfAttr1.ADAPTIVE_MOMENT) # gaussian sigma in pixels
s2 = psfAttr2.computeGaussianWidth(
psfAttr2.ADAPTIVE_MOMENT) # gaussian sigma in pixels
fwhm1 = s1 * sigma2fwhm # science Psf
fwhm2 = s2 * sigma2fwhm # template Psf
basisList = makeKernelBasisList(self.kConfig,
fwhm1,
fwhm2,
metadata=self.metadata)
spatialSolution, psfMatchingKernel, backgroundModel = self._solve(
kernelCellSet, basisList)
if psfMatchingKernel.isSpatiallyVarying():
sParameters = num.array(psfMatchingKernel.getSpatialParameters())
sParameters[0][0] = kernelSum
psfMatchingKernel.setSpatialParameters(sParameters)
else:
kParameters = num.array(psfMatchingKernel.getKernelParameters())
kParameters[0] = kernelSum
psfMatchingKernel.setKernelParameters(kParameters)
self.log.log(pexLog.Log.INFO,
"Psf-match science exposure to reference")
psfMatchedExposure = afwImage.ExposureF(exposure.getBBox(),
exposure.getWcs())
psfMatchedExposure.setFilter(exposure.getFilter())
psfMatchedExposure.setCalib(exposure.getCalib())
psfMatchedMaskedImage = psfMatchedExposure.getMaskedImage()
# Normalize the psf-matching kernel while convolving since its magnitude is meaningless
# when PSF-matching one model to another.
doNormalize = True
afwMath.convolve(psfMatchedMaskedImage, maskedImage, psfMatchingKernel,
doNormalize)
self.log.log(pexLog.Log.INFO, "done")
return pipeBase.Struct(psfMatchedExposure=psfMatchedExposure,
psfMatchingKernel=psfMatchingKernel,
kernelCellSet=kernelCellSet,
metadata=self.metadata)
def _diagnostic(self, kernelCellSet, spatialSolution, spatialKernel,
spatialBg):
"""!Print diagnostic information on spatial kernel and background fit
The debugging diagnostics are not really useful here, since the images we are matching have
no variance. Thus override the _diagnostic method to generate no logging information"""
return
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