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 ``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
--------
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 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.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.