more extensive logging

@rossant @kdharris101 @marius10p

We had an interesting discussion the other day about the possibility of using all the manual decisions that people make to train an algorithm to mimic those decisions.

A first step would be to increase the logging that's done in the existing log file (or maybe make a new one that's more detailed). E.g. you'd want to include when someone selects a cluster but doesn't make a decision about it, when they review a best vs. similar comparison and then skip to the next similar, and also anything that would help clarify what they are looking at, like which views are currently visible. You'd also want to have a way to uniquely identify which dataset they're looking at, e.g. by storing md5 hash of relevant files.

A second step could involve uploading the log files to a database automatically. Uploading the dataset would probably be prohibitive, but for any sorting that's done within our lab the datasets would be available, or we could work on finding some minimum set that could be uploaded, or we could consider versions of what to upload, e.g. maybe you want to work on training simply the "good vs. mua" decision - so you just upload a few simple things like ACG, waveform amplitude, a PC projection of nearest few clusters, etc.

The end result of all this could be an algorithm that says "I think most people would call this Good with 87% confidence" or "I think most people would merge these two with 43% confidence". In the limit of good/trusted algorithm, it could apply confident operations for you.