Fork to your directory the code here: https://github.com/michelif/HHbbgg_ETH
Clone it locally:
git clone git@github.com:[YOURNAME]/HHbbgg_ETH.git HHbbgg_ETH_devel
You should create your own branch, then do pull requests when you are done.

In order to run the notebooks on the browser you need an ssh tunnel on a machine where jupyter is running. Opening it in a screen session allows you to detach it and not redoing it every time (you will only need to ssh).

One-time initialization of Jupyter:

• Start jupyter on t3:
  ssh -L 8002:localhost:8002 t3ui02.psi.ch; //this first line has to be done every time
  screen;
  cd HHbbgg_ETH_devel;
  jupyter notebook --port 8002 --no-browser;
  ctrl-a-d //detaches screen session
  In your browser open: http://localhost:8002
  

The code is organized in notebooks, the programs you execute directly in the browser. They are in the "notebooks" folder.

• Training/notebooks
  This folder contains all the notebooks needed
  -- trainMVAHHbbgg.ipynb
  Trains a Gradient Boost Classifier to separate signal/backgrounds. All re-weighting implemented, output saved.
  -- createTrees.ipynb
  It runs on ntuples, applies a given MVA and saves a reduced ntuples which includes the MVA scores
  -- optimizeClassifier.ipynb
  Optimize parameters of the classifier. In Training/scropts/optimizeClassifier.py there is a (probably not-updated) version of this optimization code wich runs without using notebooks.

• Training/python/
  This folder contains all the tools needed by the notebooks: data format, train/test splitting, plotting... These classes have to be loaded at the beginning of your notebook.

The instructions for this part of the analysis are in the README in folder Limits/macros

The instructions for this part of the analysis are in the README in folder Limits/combine_macros