Inheritance diagram for lsst.ip.diffim.snapPsfMatch.SnapPsfMatchTask:
Public Member Functions | |
| def | subtractExposures (self, templateExposure, scienceExposure, templateFwhmPix=None, scienceFwhmPix=None, candidateList=None) |
| 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) |
Public Attributes | |
| kConfig | |
| background | |
| selectSchema | |
| selectAlgMetadata | |
| useRegularization | |
| hMat | |
Static Public Attributes | |
| ConfigClass = SnapPsfMatchConfig | |
Detailed Description
Image-based Psf-matching of two subsequent snaps from the same visit
Notes
-----
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.
Task initialization
Initialization is the same as base class ImagePsfMatch.__init__,
with the difference being that the Task's
ConfigClass is SnapPsfMatchConfig.
Invoking the Task
The Task is only configured to have a subtractExposures method, which in turn calls
ImagePsfMatchTask.subtractExposures.
Configuration parameters
See SnapPsfMatchConfig, which uses either SnapPsfMatchConfigDF and SnapPsfMatchConfigAL
as its active configuration.
Debug variables
The lsst.pipe.base.cmdLineTask.CmdLineTask command line task interface supports a
flag -d/--debug to importdebug.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.log.utils as logUtils
logUtils.traceSetAt("ip.diffim", 4)
Examples
--------
This code is snapPsfMatchTask.py in the examples directory, and can be run as e.g.
.. code-block:: py
examples/snapPsfMatchTask.py
examples/snapPsfMatchTask.py --debug
examples/snapPsfMatchTask.py --debug --template /path/to/templateExp.fits
--science /path/to/scienceExp.fits
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:
.. code-block:: py
class MySnapPsfMatchTask(SnapPsfMatchTask):
def __init__(self, *args, **kwargs):
SnapPsfMatchTask.__init__(self, *args, **kwargs)
def run(self, templateExp, scienceExp):
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
.. code-block:: py
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)
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.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 choose the basis set to use:
.. code-block:: py
def run(args):
#
# Create the Config and use sum of gaussian basis
#
config = SnapPsfMatchTask.ConfigClass()
config.doWarping = True
config.kernel.name = "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):
.. 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 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.fitForBackground = True
config.kernel.active.spatialBgOrder = 0
config.kernel.active.sizeCellX = 128
config.kernel.active.sizeCellY = 128
Display the two images if -debug
.. code-block:: py
if args.debug:
afwDisplay.Display(frame=1).mtv(templateExp, title="Example script: Input Template")
afwDisplay.Display(frame=2).mtv(scienceExp, title="Example script: Input Science Image")
Create and run the Task
.. code-block:: py
# Create the Task
psfMatchTask = MySnapPsfMatchTask(config=config)
# Run the Task
result = psfMatchTask.run(templateExp, scienceExp)
And finally provide optional debugging display of the Psf-matched (via the Psf models) science image:
.. 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 88 of file snapPsfMatch.py.
Member Function Documentation
◆ getFwhmPix()
|
inherited |
Return the FWHM in pixels of a Psf.
Definition at line 333 of file imagePsfMatch.py.
◆ getSelectSources()
|
inherited |
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 749 of file imagePsfMatch.py.
◆ makeCandidateList()
|
inherited |
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 805 of file imagePsfMatch.py.
◆ matchExposures()
|
inherited |
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 342 of file imagePsfMatch.py.
◆ matchMaskedImages()
|
inherited |
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 456 of file imagePsfMatch.py.
◆ subtractExposures() [1/2]
| def lsst.ip.diffim.snapPsfMatch.SnapPsfMatchTask.subtractExposures | ( | self, | |
| templateExposure, | |||
| scienceExposure, | |||
templateFwhmPix = None, |
|||
scienceFwhmPix = None, |
|||
candidateList = None |
|||
| ) |
Definition at line 304 of file snapPsfMatch.py.
◆ subtractExposures() [2/2]
|
inherited |
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.Exposure`
Exposure to PSF-match to scienceExposure
scienceExposure : `lsst.afw.image.Exposure`
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 569 of file imagePsfMatch.py.
◆ subtractMaskedImages()
|
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 680 of file imagePsfMatch.py.
Member Data Documentation
◆ background
|
inherited |
Definition at line 326 of file imagePsfMatch.py.
◆ ConfigClass
|
static |
Definition at line 299 of file snapPsfMatch.py.
◆ hMat
|
inherited |
Definition at line 662 of file psfMatch.py.
◆ kConfig
|
inherited |
Definition at line 322 of file imagePsfMatch.py.
◆ selectAlgMetadata
|
inherited |
Definition at line 329 of file imagePsfMatch.py.
◆ selectSchema
|
inherited |
Definition at line 328 of file imagePsfMatch.py.
◆ useRegularization
|
inherited |
Definition at line 657 of file psfMatch.py.
The documentation for this class was generated from the following file:
- python/lsst/ip/diffim/snapPsfMatch.py
