== e2classifyligand.py ==

I had an excellent description of this program, and how to use it, but somehow it got lost. For the moment, I'm just including some basic tips. I will have to fully document it again later.

This program is used to subdivide particles into two populations based on two different 3-D maps (or, alternatively a 3-D mask). It will also provide some statistical results indicating how
well you can discriminate between the two groups on a per-particle basis.

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The program is called e2classifyligand.py. If you run it with no options, it will give some basic help, with '-h' it will
give more help. Basic usage is :

- First run e2refine for at least a couple of cycles on your data
- Prepare 2 volume files the same size as your reconstruction. One with ligand present and one without ligand. If you lack 2 good references, you
could take the results of the single refinement from all of the data, copy it, and mask out the ligand from the copy.
- run:
e2classifyligand.py <raw particle file> <class mx> <projections> --ref1=<unliganded ref> --ref2=<liganded ref>

 * <raw particle file> is a 'set' containing the particles you need to classify, the same set you ran e2refine.py on, something like sets/set1__ctf_flip.lst
 * <class mx> is the classification matrix for these particles, something like refine_01/classify_01.hdf
 * <projections> are the projections from the same iteration, like refine_01/projections_01.hdf

That's the basic algorithm. If you want to try to put 'bad' (difficult to classify) particles into separate files, you can specify the:
 --badgroup            Split the data into 4 groups rather than 2. The extra
                       two groups contain particles more likely to be bad.
 --badqualsig=BADQUALSIG
                       When identifying 'bad' particles, particles with
                       similarities >mean+sigma*badqualsig will be considered
                       bad. Default 0.5
 --badsepsig=BADSEPSIG
                       When identifying 'bad' particles, if s1/s2 are the
                       similarities to reference 1/2, then those where
                       |s1-s2| < sigma*badsepsig will be excluded. Default
                       0.25