lsst.ip.diffim  15.0-10-g9f34280+2
Public Member Functions | Public Attributes | Static Public Attributes | List of all members
lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask Class Reference

Psf-match two MaskedImages or Exposures using the sources in the images. More...

Inheritance diagram for lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask:
lsst.ip.diffim.psfMatch.PsfMatchTask lsst.ip.diffim.snapPsfMatch.SnapPsfMatchTask lsst.ip.diffim.zogy.ZogyImagePsfMatchTask

Public Member Functions

def __init__ (self, args, kwargs)
 Create the ImagePsfMatchTask. More...
 
def getFwhmPix (self, psf)
 Return the FWHM in pixels of a Psf. More...
 
def matchExposures (self, templateExposure, scienceExposure, templateFwhmPix=None, scienceFwhmPix=None, candidateList=None, doWarping=True, convolveTemplate=True)
 Warp and PSF-match an exposure to the reference. More...
 
def matchMaskedImages (self, templateMaskedImage, scienceMaskedImage, candidateList, templateFwhmPix=None, scienceFwhmPix=None)
 PSF-match a MaskedImage (templateMaskedImage) to a reference MaskedImage (scienceMaskedImage) More...
 
def subtractExposures (self, templateExposure, scienceExposure, templateFwhmPix=None, scienceFwhmPix=None, candidateList=None, doWarping=True, convolveTemplate=True)
 Register, Psf-match and subtract two Exposures. More...
 
def subtractMaskedImages (self, templateMaskedImage, scienceMaskedImage, candidateList, templateFwhmPix=None, scienceFwhmPix=None)
 Psf-match and subtract two MaskedImages. More...
 
def getSelectSources (self, exposure, sigma=None, doSmooth=True, idFactory=None)
 Get sources to use for Psf-matching. More...
 
def makeCandidateList (self, templateExposure, scienceExposure, kernelSize, candidateList=None)
 Make a list of acceptable KernelCandidates. More...
 

Public Attributes

 kConfig
 
 background
 
 selectSchema
 
 selectAlgMetadata
 
 useRegularization
 
 hMat
 

Static Public Attributes

 ConfigClass = ImagePsfMatchConfig
 

Detailed Description

Psf-match two MaskedImages or Exposures using the sources in the images.

Contents

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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:

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.

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Task initialization

Create the ImagePsfMatchTask.

Parameters
*argsarguments to be passed to lsst.ip.diffim.PsfMatchTask.__init__
**kwargskeyword 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).

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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 matchMaskedImages, subtractMaskedImages, matchExposures, and subtractExposures.

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.

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Configuration parameters

See ImagePsfMatchConfig

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Quantities set in Metadata

See PsfMatchTask

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Debug variables

The command line task interface supports a flag -d/–debug to import debug.py from your PYTHONPATH. The relevant contents of debug.py for this Task include:

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

Note that if you want addional logging info, you may add to your scripts:

import lsst.log.utils as logUtils
logUtils.traceSetAt("ip.diffim", 4)

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A complete example of using ImagePsfMatchTask

This code is imagePsfMatchTask.py in the examples directory, and can be run as e.g.

examples/imagePsfMatchTask.py --debug
examples/imagePsfMatchTask.py --debug --mode="matchExposures"
examples/imagePsfMatchTask.py --debug --template /path/to/templateExp.fits --science /path/to/scienceExp.fits
Create a subclass of ImagePsfMatchTask that allows us to either match exposures, or subtract exposures:
class MyImagePsfMatchTask(ImagePsfMatchTask):
"""An override for ImagePsfMatchTask"""
def __init__(self, *args, **kwargs):
ImagePsfMatchTask.__init__(self, *args, **kwargs)
def run(self, templateExp, scienceExp, mode):
if mode == "matchExposures":
return self.matchExposures(templateExp, scienceExp)
elif mode == "subtractExposures":
return self.subtractExposures(templateExp, scienceExp)

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:

if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description="Demonstrate the use of ImagePsfMatchTask")
parser.add_argument("--debug", "-d", action="store_true", help="Load debug.py?", default=False)
parser.add_argument("--template", "-t", help="Template Exposure to use", default=None)
parser.add_argument("--science", "-s", help="Science Exposure to use", default=None)
parser.add_argument("--mode", choices=["matchExposures", "subtractExposures"],
default="subtractExposures", help="Which method of ImagePsfMatchTask to invoke")
args = parser.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:

if args.debug:
try:
import debug
# Since I am displaying 2 images here, set the starting frame number for the LSST debug LSST
debug.lsstDebug.frame = 3
except ImportError as e:
print(e, file=sys.stderr)

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:
def run(args):
#
# Create the Config and use sum of gaussian basis
#
config = ImagePsfMatchTask.ConfigClass()
config.kernel.name = "AL"
config.kernel.active.fitForBackground = True
config.kernel.active.spatialKernelOrder = 1
config.kernel.active.spatialBgOrder = 0

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):

# Run the requested method of the Task
if args.template is not None and args.science is not None:
if not os.path.isfile(args.template):
raise Exception("Template image %s does not exist" % (args.template))
if not os.path.isfile(args.science):
raise Exception("Science image %s does not exist" % (args.science))
try:
templateExp = afwImage.ExposureF(args.template)
except Exception as e:
raise Exception("Cannot read template image %s" % (args.template))
try:
scienceExp = afwImage.ExposureF(args.science)
except Exception as e:
raise Exception("Cannot read science image %s" % (args.science))
else:
templateExp, scienceExp = generateFakeImages()
config.kernel.active.sizeCellX = 128
config.kernel.active.sizeCellY = 128

Create and run the Task:

# Create the Task
psfMatchTask = MyImagePsfMatchTask(config=config)
# Run the Task
result = psfMatchTask.run(templateExp, scienceExp, args.mode)

And finally provide some optional debugging displays:

if args.debug:
# See if the LSST debug has incremented the frame number; if not start with frame 3
try:
frame = debug.lsstDebug.frame + 1
except Exception:
frame = 3
ds9.mtv(result.matchedExposure, frame=frame, title="Example script: Matched Template Image")
if "subtractedExposure" in result.getDict():
ds9.mtv(result.subtractedExposure, frame=frame+1, title="Example script: Subtracted Image")
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Definition at line 85 of file imagePsfMatch.py.

Constructor & Destructor Documentation

◆ __init__()

def lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.__init__ (   self,
  args,
  kwargs 
)

Create the ImagePsfMatchTask.

Parameters
*argsarguments to be passed to lsst.ip.diffim.PsfMatchTask.__init__
**kwargskeyword 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).

Definition at line 273 of file imagePsfMatch.py.

Member Function Documentation

◆ getFwhmPix()

def lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.getFwhmPix (   self,
  psf 
)

Return the FWHM in pixels of a Psf.

Definition at line 297 of file imagePsfMatch.py.

◆ getSelectSources()

def lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.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.

Parameters
exposureExposure on which to run detection/measurement
sigmaDetection threshold
doSmoothWhether or not to smooth the Exposure with Psf before detection
idFactoryFactory for the generation of Source ids
Returns
source catalog containing candidates for the Psf-matching

Definition at line 621 of file imagePsfMatch.py.

◆ makeCandidateList()

def lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.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

Parameters
templateExposureExposure that will be convolved
scienceExposureExposure that will be matched-to
kernelSizeDimensions of the Psf-matching Kernel, used to grow detection footprints
candidateListList 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.
Returns
a list of dicts having a "source" and "footprint" field for the Sources deemed to be appropriate for Psf matching

Definition at line 669 of file imagePsfMatch.py.

◆ matchExposures()

def lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.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
Parameters
templateExposureExposure to warp and PSF-match to the reference masked image
scienceExposureExposure whose WCS and PSF are to be matched to
templateFwhmPixFWHM (in pixels) of the Psf in the template image (image to convolve)
scienceFwhmPixFWHM (in pixels) of the Psf in the science image
candidateLista list of footprints/maskedImages for kernel candidates; if None then source detection is run.
  • Currently supported: list of Footprints or measAlg.PsfCandidateF
doWarpingwhat 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
convolveTemplateconvolve 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
Returns
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

Definition at line 305 of file imagePsfMatch.py.

◆ matchMaskedImages()

def lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.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
Parameters
templateMaskedImagemasked image to PSF-match to the reference masked image; must be warped to match the reference masked image
scienceMaskedImagemaskedImage whose PSF is to be matched to
templateFwhmPixFWHM (in pixels) of the Psf in the template image (image to convolve)
scienceFwhmPixFWHM (in pixels) of the Psf in the science image
candidateLista list of footprints/maskedImages for kernel candidates; if None then source detection is run.
  • Currently supported: list of Footprints or measAlg.PsfCandidateF
Returns
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

Definition at line 389 of file imagePsfMatch.py.

◆ subtractExposures()

def lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.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).
Parameters
templateExposureexposure to PSF-match to scienceExposure
scienceExposurereference Exposure
templateFwhmPixFWHM (in pixels) of the Psf in the template image (image to convolve)
scienceFwhmPixFWHM (in pixels) of the Psf in the science image
candidateLista list of footprints/maskedImages for kernel candidates; if None then source detection is run.
  • Currently supported: list of Footprints or measAlg.PsfCandidateF
doWarpingwhat 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
convolveTemplateconvolve 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
Returns
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

Definition at line 484 of file imagePsfMatch.py.

◆ subtractMaskedImages()

def lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.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)
Parameters
templateMaskedImageMaskedImage to PSF-match to scienceMaskedImage
scienceMaskedImagereference MaskedImage
templateFwhmPixFWHM (in pixels) of the Psf in the template image (image to convolve)
scienceFwhmPixFWHM (in pixels) of the Psf in the science image
candidateLista list of footprints/maskedImages for kernel candidates; if None then source detection is run.
  • Currently supported: list of Footprints or measAlg.PsfCandidateF
Returns
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

Definition at line 567 of file imagePsfMatch.py.

Member Data Documentation

◆ background

lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.background

Definition at line 290 of file imagePsfMatch.py.

◆ ConfigClass

lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.ConfigClass = ImagePsfMatchConfig
static

Definition at line 271 of file imagePsfMatch.py.

◆ hMat

lsst.ip.diffim.psfMatch.PsfMatchTask.hMat
inherited

Definition at line 702 of file psfMatch.py.

◆ kConfig

lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.kConfig

Definition at line 286 of file imagePsfMatch.py.

◆ selectAlgMetadata

lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.selectAlgMetadata

Definition at line 293 of file imagePsfMatch.py.

◆ selectSchema

lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.selectSchema

Definition at line 292 of file imagePsfMatch.py.

◆ useRegularization

lsst.ip.diffim.psfMatch.PsfMatchTask.useRegularization
inherited

Definition at line 697 of file psfMatch.py.


The documentation for this class was generated from the following file: