LSST Applications g0da5cf3356+25b44625d0,g17e5ecfddb+50a5ac4092,g1c76d35bf8+585f0f68a2,g295839609d+8ef6456700,g2e2c1a68ba+cc1f6f037e,g38293774b4+62d12e78cb,g3b44f30a73+2891c76795,g48ccf36440+885b902d19,g4b2f1765b6+0c565e8f25,g5320a0a9f6+bd4bf1dc76,g56364267ca+403c24672b,g56b687f8c9+585f0f68a2,g5c4744a4d9+78cd207961,g5ffd174ac0+bd4bf1dc76,g6075d09f38+3075de592a,g667d525e37+cacede5508,g6f3e93b5a3+da81c812ee,g71f27ac40c+cacede5508,g7212e027e3+eb621d73aa,g774830318a+18d2b9fa6c,g7985c39107+62d12e78cb,g79ca90bc5c+fa2cc03294,g881bdbfe6c+cacede5508,g91fc1fa0cf+82a115f028,g961520b1fb+2534687f64,g96f01af41f+f2060f23b6,g9ca82378b8+cacede5508,g9d27549199+78cd207961,gb065e2a02a+ad48cbcda4,gb1df4690d6+585f0f68a2,gb35d6563ee+62d12e78cb,gbc3249ced9+bd4bf1dc76,gbec6a3398f+bd4bf1dc76,gd01420fc67+bd4bf1dc76,gd59336e7c4+c7bb92e648,gf46e8334de+81c9a61069,gfed783d017+bd4bf1dc76,v25.0.1.rc3
LSST Data Management Base Package
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Public Member Functions | |
def | __init__ (self, *args, **kwargs) |
def | getFwhmPix (self, psf, position=None) |
def | matchExposures (self, templateExposure, scienceExposure, templateFwhmPix=None, scienceFwhmPix=None, candidateList=None, doWarping=True, convolveTemplate=True) |
def | matchMaskedImages (self, templateMaskedImage, scienceMaskedImage, candidateList, templateFwhmPix=None, scienceFwhmPix=None) |
def | subtractExposures (self, templateExposure, scienceExposure, templateFwhmPix=None, scienceFwhmPix=None, candidateList=None, doWarping=True, convolveTemplate=True) |
def | subtractMaskedImages (self, templateMaskedImage, scienceMaskedImage, candidateList, templateFwhmPix=None, scienceFwhmPix=None) |
def | getSelectSources (self, exposure, sigma=None, doSmooth=True, idFactory=None) |
def | makeCandidateList (self, templateExposure, scienceExposure, kernelSize, candidateList=None) |
def | makeKernelBasisList (self, targetFwhmPix=None, referenceFwhmPix=None, basisDegGauss=None, basisSigmaGauss=None, metadata=None) |
Public Attributes | |
kConfig | |
background | |
selectSchema | |
selectAlgMetadata | |
Static Public Attributes | |
ConfigClass = ImagePsfMatchConfig | |
Psf-match two MaskedImages or Exposures using the sources in the images. Parameters ---------- args : Arguments to be passed to lsst.ip.diffim.PsfMatchTask.__init__ kwargs : Keyword arguments to be passed to lsst.ip.diffim.PsfMatchTask.__init__ Notes ----- 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). 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. 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 `~lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.matchMaskedImages`, `~lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.subtractMaskedImages`, `~lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.matchExposures`, and `~lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.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. Debug variables The ``pipetask`` command line interface supports a flag --debug to import @b debug.py from your PYTHONPATH. The relevant contents of debug.py for this Task include: .. code-block:: 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 # display 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 # display 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 # display 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: .. code-block:: py import lsst.utils.logging as logUtils logUtils.trace_set_at("lsst.ip.diffim", 4) Examples -------- A complete example of using ImagePsfMatchTask This code is imagePsfMatchTask.py in the examples directory, and can be run as e.g. .. code-block:: none 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: .. code-block:: none class MyImagePsfMatchTask(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. 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: .. code-block:: py 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: .. code-block:: py 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): .. code-block:: py # 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 FileNotFoundError("Template image %s does not exist" % (args.template)) if not os.path.isfile(args.science): raise FileNotFoundError("Science image %s does not exist" % (args.science)) try: templateExp = afwImage.ExposureF(args.template) except Exception as e: raise RuntimeError("Cannot read template image %s" % (args.template)) try: scienceExp = afwImage.ExposureF(args.science) except Exception as e: raise RuntimeError("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: .. code-block:: py # Create the Task psfMatchTask = MyImagePsfMatchTask(config=config) # Run the Task result = psfMatchTask.run(templateExp, scienceExp, args.mode) And finally provide some optional debugging displays: .. code-block:: py 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 afwDisplay.Display(frame=frame).mtv(result.matchedExposure, title="Example script: Matched Template Image") if "subtractedExposure" in result.getDict(): afwDisplay.Display(frame=frame + 1).mtv(result.subtractedExposure, title="Example script: Subtracted Image")
Definition at line 82 of file imagePsfMatch.py.
def lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.__init__ | ( | self, | |
* | args, | ||
** | kwargs | ||
) |
Create the ImagePsfMatchTask.
Reimplemented from lsst.ip.diffim.psfMatch.PsfMatchTask.
Reimplemented in lsst.ip.diffim.zogy.ZogyImagePsfMatchTask.
Definition at line 319 of file imagePsfMatch.py.
def lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.getFwhmPix | ( | self, | |
psf, | |||
position = None |
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) |
Return the FWHM in pixels of a Psf.
Definition at line 334 of file imagePsfMatch.py.
def lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.getSelectSources | ( | self, | |
exposure, | |||
sigma = None , |
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doSmooth = True , |
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idFactory = None |
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) |
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 ---------- exposure : `lsst.afw.image.Exposure` Exposure on which to run detection/measurement sigma : `float` Detection threshold doSmooth : `bool` Whether or not to smooth the Exposure with Psf before detection idFactory : Factory for the generation of Source ids Returns ------- selectSources : source catalog containing candidates for the Psf-matching
Definition at line 762 of file imagePsfMatch.py.
def lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.makeCandidateList | ( | self, | |
templateExposure, | |||
scienceExposure, | |||
kernelSize, | |||
candidateList = None |
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) |
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 ---------- templateExposure : `lsst.afw.image.Exposure` Exposure that will be convolved scienceExposure : `lsst.afw.image.Exposure` Exposure that will be matched-to kernelSize : `float` Dimensions of the Psf-matching Kernel, used to grow detection footprints candidateList : `list`, optional 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. Returns ------- candidateList : `list` of `dict` A list of dicts having a "source" and "footprint" field for the Sources deemed to be appropriate for Psf matching
Definition at line 820 of file imagePsfMatch.py.
def lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.makeKernelBasisList | ( | self, | |
targetFwhmPix = None , |
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referenceFwhmPix = None , |
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basisDegGauss = None , |
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basisSigmaGauss = None , |
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metadata = None |
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) |
Wrapper to set log messages for `lsst.ip.diffim.makeKernelBasisList`. Parameters ---------- targetFwhmPix : `float`, optional Passed on to `lsst.ip.diffim.generateAlardLuptonBasisList`. Not used for delta function basis sets. referenceFwhmPix : `float`, optional Passed on to `lsst.ip.diffim.generateAlardLuptonBasisList`. Not used for delta function basis sets. basisDegGauss : `list` of `int`, optional Passed on to `lsst.ip.diffim.generateAlardLuptonBasisList`. Not used for delta function basis sets. basisSigmaGauss : `list` of `int`, optional Passed on to `lsst.ip.diffim.generateAlardLuptonBasisList`. Not used for delta function basis sets. metadata : `lsst.daf.base.PropertySet`, optional Passed on to `lsst.ip.diffim.generateAlardLuptonBasisList`. Not used for delta function basis sets. Returns ------- basisList: `list` of `lsst.afw.math.kernel.FixedKernel` List of basis kernels.
Definition at line 876 of file imagePsfMatch.py.
def lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.matchExposures | ( | self, | |
templateExposure, | |||
scienceExposure, | |||
templateFwhmPix = None , |
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scienceFwhmPix = None , |
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candidateList = None , |
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doWarping = True , |
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convolveTemplate = True |
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) |
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 ---------- templateExposure : `lsst.afw.image.Exposure` Exposure to warp and PSF-match to the reference masked image scienceExposure : `lsst.afw.image.Exposure` Exposure whose WCS and PSF are to be matched to templateFwhmPix :`float` FWHM (in pixels) of the Psf in the template image (image to convolve) scienceFwhmPix : `float` FWHM (in pixels) of the Psf in the science image candidateList : `list`, optional a list of footprints/maskedImages for kernel candidates; if `None` then source detection is run. - Currently supported: list of Footprints or measAlg.PsfCandidateF doWarping : `bool` what to do if ``templateExposure`` and ``scienceExposure`` WCSs do not match: - if `True` then warp ``templateExposure`` to match ``scienceExposure`` - if `False` then raise an Exception convolveTemplate : `bool` Whether to 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 Returns ------- results : `lsst.pipe.base.Struct` An `lsst.pipe.base.Struct` containing these fields: - ``matchedImage`` : the PSF-matched exposure = Warped ``templateExposure`` convolved by psfMatchingKernel. This has: - the same parent bbox, Wcs and PhotoCalib 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 Raises ------ RuntimeError Raised if doWarping is False and ``templateExposure`` and ``scienceExposure`` WCSs do not match
Definition at line 343 of file imagePsfMatch.py.
def lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.matchMaskedImages | ( | self, | |
templateMaskedImage, | |||
scienceMaskedImage, | |||
candidateList, | |||
templateFwhmPix = None , |
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scienceFwhmPix = None |
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) |
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 ---------- templateMaskedImage : `lsst.afw.image.MaskedImage` masked image to PSF-match to the reference masked image; must be warped to match the reference masked image scienceMaskedImage : `lsst.afw.image.MaskedImage` maskedImage whose PSF is to be matched to templateFwhmPix : `float` FWHM (in pixels) of the Psf in the template image (image to convolve) scienceFwhmPix : `float` FWHM (in pixels) of the Psf in the science image candidateList : `list`, optional A list of footprints/maskedImages for kernel candidates; if `None` then source detection is run. - Currently supported: list of Footprints or measAlg.PsfCandidateF Returns ------- result : `callable` An `lsst.pipe.base.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 Raises ------ RuntimeError Raised if input images have different dimensions
Definition at line 461 of file imagePsfMatch.py.
def lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.subtractExposures | ( | self, | |
templateExposure, | |||
scienceExposure, | |||
templateFwhmPix = None , |
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scienceFwhmPix = None , |
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candidateList = None , |
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doWarping = True , |
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convolveTemplate = True |
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) |
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 ---------- templateExposure : `lsst.afw.image.ExposureF` Exposure to PSF-match to scienceExposure scienceExposure : `lsst.afw.image.ExposureF` Reference Exposure templateFwhmPix : `float` FWHM (in pixels) of the Psf in the template image (image to convolve) scienceFwhmPix : `float` FWHM (in pixels) of the Psf in the science image candidateList : `list`, optional A list of footprints/maskedImages for kernel candidates; if `None` then source detection is run. - Currently supported: list of Footprints or measAlg.PsfCandidateF doWarping : `bool` What to do if ``templateExposure``` and ``scienceExposure`` WCSs do not match: - if `True` then warp ``templateExposure`` to match ``scienceExposure`` - if `False` then raise an Exception convolveTemplate : `bool` 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 Returns ------- result : `lsst.pipe.base.Struct` An `lsst.pipe.base.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
Reimplemented in lsst.ip.diffim.zogy.ZogyImagePsfMatchTask, and lsst.ip.diffim.snapPsfMatch.SnapPsfMatchTask.
Definition at line 574 of file imagePsfMatch.py.
def lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.subtractMaskedImages | ( | self, | |
templateMaskedImage, | |||
scienceMaskedImage, | |||
candidateList, | |||
templateFwhmPix = None , |
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scienceFwhmPix = None |
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) |
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 ---------- templateMaskedImage : `lsst.afw.image.MaskedImage` MaskedImage to PSF-match to ``scienceMaskedImage`` scienceMaskedImage : `lsst.afw.image.MaskedImage` Reference MaskedImage templateFwhmPix : `float` FWHM (in pixels) of the Psf in the template image (image to convolve) scienceFwhmPix : `float` FWHM (in pixels) of the Psf in the science image candidateList : `list`, optional A list of footprints/maskedImages for kernel candidates; if `None` then source detection is run. - Currently supported: list of Footprints or measAlg.PsfCandidateF Returns ------- results : `lsst.pipe.base.Struct` An `lsst.pipe.base.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
Reimplemented in lsst.ip.diffim.zogy.ZogyImagePsfMatchTask.
Definition at line 692 of file imagePsfMatch.py.
lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.background |
Definition at line 327 of file imagePsfMatch.py.
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static |
Definition at line 317 of file imagePsfMatch.py.
lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.kConfig |
Definition at line 323 of file imagePsfMatch.py.
lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.selectAlgMetadata |
Definition at line 330 of file imagePsfMatch.py.
lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.selectSchema |
Definition at line 329 of file imagePsfMatch.py.