SnapPsfMatchTask¶

class lsst.ip.diffim.SnapPsfMatchTask(*args, **kwargs)¶

Bases: lsst.ip.diffim.ImagePsfMatchTask

@brief Image-based Psf-matching of two subsequent snaps from the same visit

@section ip_diffim_snappsfmatch_Contents Contents

@ref ip_diffim_snappsfmatch_Purpose

@ref ip_diffim_snappsfmatch_Initialize

@ref ip_diffim_snappsfmatch_IO

@ref ip_diffim_snappsfmatch_Config

@ref ip_diffim_snappsfmatch_Metadata

@ref ip_diffim_snappsfmatch_Debug

@ref ip_diffim_snappsfmatch_Example

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@section ip_diffim_snappsfmatch_Purpose Description

@copybrief SnapPsfMatchTask

This Task differs from ImagePsfMatchTask in that it matches two Exposures assuming that the images have been acquired very closely in time. Under this assumption, the astrometric misalignments and/or relative distortions should be within a pixel, and the Psf-shapes should be very similar. As a consequence, the default configurations for this class assume a very simple solution.

. The spatial variation in the kernel (SnapPsfMatchConfig.spatialKernelOrder) is assumed to be zero

. With no spatial variation, we turn of the spatial clipping loops (SnapPsfMatchConfig.spatialKernelClipping)

. The differential background is _not_ fit for (SnapPsfMatchConfig.fitForBackground)

. The kernel is expected to be appx. a delta function, and has a small size (SnapPsfMatchConfig.kernelSize)

The sub-configurations for the Alard-Lupton (SnapPsfMatchConfigAL) and delta-function (SnapPsfMatchConfigDF) bases also are designed to generate a small, simple kernel.

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@section ip_diffim_snappsfmatch_Initialize Task initialization

Initialization is the same as base class ImagePsfMatch.__init__, with the difference being that the Task’s ConfigClass is SnapPsfMatchConfig.

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@section ip_diffim_snappsfmatch_IO Invoking the Task

The Task is only configured to have a subtractExposures method, which in turn calls ImagePsfMatchTask.subtractExposures.

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@section ip_diffim_snappsfmatch_Config Configuration parameters

See @ref SnapPsfMatchConfig, which uses either @ref SnapPsfMatchConfigDF and @ref SnapPsfMatchConfigAL as its active configuration.

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@section ip_diffim_snappsfmatch_Metadata Quantities set in Metadata

See @ref ip_diffim_psfmatch_Metadata “PsfMatchTask”

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@section ip_diffim_snappsfmatch_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.log.utils as logUtils logUtils.traceSetAt(“ip.diffim”, 4) @endcode

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@section ip_diffim_snappsfmatch_Example A complete example of using SnapPsfMatchTask

This code is snapPsfMatchTask.py in the examples directory, and can be run as @em e.g. @code examples/snapPsfMatchTask.py examples/snapPsfMatchTask.py –debug examples/snapPsfMatchTask.py –debug –template /path/to/templateExp.fits –science /path/to/scienceExp.fits @endcode

@dontinclude snapPsfMatchTask.py First, create a subclass of SnapPsfMatchTask that accepts two exposures. Ideally these exposures would have been taken back-to-back, such that the pointing/background/Psf does not vary substantially between the two: @skip MySnapPsfMatchTask @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 snapPsfMatchTask.py Finally, we call a run method that we define below. First set up a Config and choose the basis set to use: @skip run(args) @until AL

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; as a detail of how the fake images were made, you do have to fit for a differential background): @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.subtractedExposure

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Methods Summary

subtractExposures(templateExposure, …[, …]) !Register, Psf-match and subtract two Exposures

Methods Documentation

subtractExposures(templateExposure, scienceExposure, templateFwhmPix=None, scienceFwhmPix=None, candidateList=None)¶

!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

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SnapPsfMatchTask¶