Llr optimizer initial

Hi Sebastian,

This is my latest pull request, which includes the initial code for the LLR Optimizer analysis. I have also updated Steve's template.py code, by taking the approach you suggested-grab his template.py from his repo and incorporate all the suggestions that I commented on myself. I also have tried to upload all data and input files, so if there are any missing, please let me know! The only thing really missing from the optimizer analysis code right now is the implementation of a few additional systematics, as well as adding the priors on the parameters that we do not wish to constrain.
I think AeffServicePar should rather directly take "aeff_numu_CC, aeff_NC,..." as an input. Finding the proper values for these should be left to whoever creates the service. In fact, I would also suggest we make the settings.json a flat dictionary, so that we can just pass `**settings` in here.