LSST Applications
21.0.0-172-gfb10e10a+18fedfabac,22.0.0+297cba6710,22.0.0+80564b0ff1,22.0.0+8d77f4f51a,22.0.0+a28f4c53b1,22.0.0+dcf3732eb2,22.0.1-1-g7d6de66+2a20fdde0d,22.0.1-1-g8e32f31+297cba6710,22.0.1-1-geca5380+7fa3b7d9b6,22.0.1-12-g44dc1dc+2a20fdde0d,22.0.1-15-g6a90155+515f58c32b,22.0.1-16-g9282f48+790f5f2caa,22.0.1-2-g92698f7+dcf3732eb2,22.0.1-2-ga9b0f51+7fa3b7d9b6,22.0.1-2-gd1925c9+bf4f0e694f,22.0.1-24-g1ad7a390+a9625a72a8,22.0.1-25-g5bf6245+3ad8ecd50b,22.0.1-25-gb120d7b+8b5510f75f,22.0.1-27-g97737f7+2a20fdde0d,22.0.1-32-gf62ce7b1+aa4237961e,22.0.1-4-g0b3f228+2a20fdde0d,22.0.1-4-g243d05b+871c1b8305,22.0.1-4-g3a563be+32dcf1063f,22.0.1-4-g44f2e3d+9e4ab0f4fa,22.0.1-42-gca6935d93+ba5e5ca3eb,22.0.1-5-g15c806e+85460ae5f3,22.0.1-5-g58711c4+611d128589,22.0.1-5-g75bb458+99c117b92f,22.0.1-6-g1c63a23+7fa3b7d9b6,22.0.1-6-g50866e6+84ff5a128b,22.0.1-6-g8d3140d+720564cf76,22.0.1-6-gd805d02+cc5644f571,22.0.1-8-ge5750ce+85460ae5f3,master-g6e05de7fdc+babf819c66,master-g99da0e417a+8d77f4f51a,w.2021.48
LSST Data Management Base Package
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Public Member Functions | |
def | __init__ (self, *args, **kwargs) |
def | run (self, scienceExposure, templateExposure, doWarping=True) |
def | subtractExposures (self, templateExposure, scienceExposure, *args) |
def | subtractMaskedImages (self, templateExposure, scienceExposure, *args) |
def | getFwhmPix (self, psf) |
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 | |
useRegularization | |
hMat | |
Static Public Attributes | |
ConfigClass = ZogyImagePsfMatchConfig | |
Task to perform Zogy PSF matching and image subtraction. This class inherits from ImagePsfMatchTask to contain the _warper subtask and related methods.
def lsst.ip.diffim.zogy.ZogyImagePsfMatchTask.__init__ | ( | self, | |
* | args, | ||
** | kwargs | ||
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Create the ImagePsfMatchTask.
Reimplemented from lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.
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Return the FWHM in pixels of a Psf.
Definition at line 334 of file imagePsfMatch.py.
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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 760 of file imagePsfMatch.py.
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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 818 of file imagePsfMatch.py.
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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 874 of file imagePsfMatch.py.
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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 341 of file imagePsfMatch.py.
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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 459 of file imagePsfMatch.py.
def lsst.ip.diffim.zogy.ZogyImagePsfMatchTask.run | ( | self, | |
scienceExposure, | |||
templateExposure, | |||
doWarping = True |
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Register, PSF-match, and subtract two Exposures, ``scienceExposure - templateExposure`` using the ZOGY algorithm. Parameters ---------- templateExposure : `lsst.afw.image.Exposure` exposure to be warped to scienceExposure. scienceExposure : `lsst.afw.image.Exposure` reference Exposure. doWarping : `bool` 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 Notes ----- Do the following, in order: - Warp templateExposure to match scienceExposure, if their WCSs do not already match - Compute subtracted exposure ZOGY image subtraction algorithm on the two exposures This is the new entry point of the task as of DM-25115. Returns ------- results : `lsst.pipe.base.Struct` containing these fields: - subtractedExposure: `lsst.afw.image.Exposure` The subtraction result. - warpedExposure: `lsst.afw.image.Exposure` or `None` templateExposure after warping to match scienceExposure
Definition at line 1281 of file zogy.py.
def lsst.ip.diffim.zogy.ZogyImagePsfMatchTask.subtractExposures | ( | self, | |
templateExposure, | |||
scienceExposure, | |||
* | args | ||
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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
Definition at line 572 of file imagePsfMatch.py.
def lsst.ip.diffim.zogy.ZogyImagePsfMatchTask.subtractMaskedImages | ( | self, | |
templateExposure, | |||
scienceExposure, | |||
* | args | ||
) |
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inherited |
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
Definition at line 690 of file imagePsfMatch.py.
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Definition at line 327 of file imagePsfMatch.py.
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Definition at line 661 of file psfMatch.py.
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Definition at line 323 of file imagePsfMatch.py.
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Definition at line 330 of file imagePsfMatch.py.
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Definition at line 329 of file imagePsfMatch.py.
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Definition at line 656 of file psfMatch.py.