This set of jupyter notebooks is designed to work with the file
    example1.fits
which is assumed to be in this directory.  You can run them by initialising the LSST code, then
    ipython jupyter &

Start by looking at
    Image Processing.ipynb
which runs through processing a single image using the LSST codebase; there are (of course) command line
scripts that wrap this up in a reasonably nice interface.  Jim Bosch will introduce you to them later.

If you want to know more about the `Exposure' object that we use to represent the image look at
    Exposures.ipynb

The deblender isn't especially interesting for this shallow data (30s on the 8.2m Subaru telescope), but if
you want to see what it does look at
    Deblender.ipynb

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The remaining notebooks illustrate ways to fiddle with configurations.

The default PSF estimation uses a not-very-good version of the PCA code that I wrote for SDSS.  Running
    PSF.ipynb
explores how well this works, and shows what happens if we switch to using PSFex as the back-end.

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The
    Tune Detection.ipynb
notebook fiddles a bit with the detection algorithms; changing the thresholds, and enabling a feature that
aggessively over-subtracts bright objects while detecting (which suppresses small things near big ones)

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For reasons that I don't understand the Kron code isn't distributed as part
of the standard LSST stack (we'll fix this), but this provides the opportunity to illustrate the LSST plugin
architecture.  To use the Kron code you'll have to clone code from github and build it:

Initialise the LSST code, then:
    git clone git@github.com:lsst/meas_extensions_photometryKron
    setup -r . -j
    scons -Q opt=3 -j 4
then restart your jupyter server.  Then run the
     Kron.ipynb
notebook.