Repository for developing H->gg analyses, starting from (skimmed) nanoAOD inputs. Will contain machinery for the standard tasks in developing an H->gg analysis:

• Looping over nanoAOD and making yield tables + data/MC plots
• MVA training: MVA input file prep, tools for BDT/DNN training, writing output to an ntuple for SR optimization
• Signal region optimization: optimize cut(s) on MVA(s) to maximize expected sensitivity to some observable

• For first time setup, run source setup.sh to create a virtual environment for python3 and install all necessary packages.
• After the first time, you can activate your virtual environment with source env/bin/activate

• Keep track of to-do's, problems, and planned developments in the Issues tab
• For major revisions/additions, make a pull request
• For minor changes/bug fixes, commit directly to main
• Try to somewhat loosely adhere to PEP 8 style guidlines (or at least make your code readable and add comments)
  • If you want adhere to PEP8, use flake8 to check if your code adheres to it, and black to auto-format your code to adhere to PEP8