Turns your trees into tables (ie. reads ROOT TTrees, writes summary Pandas DataFrames)
- Be controlled using YAML-based config files
- Define new variables
- Cut out events or define phase-space "regions"
- Produce histograms stored as CSV files using multiple weighting schemes
- Make use of user-defined stages to manipulate the data
- AlphaTwirl (presently): to run the dataset splitting
- Atuproot: to adapt AlphaTwirl to use uproot
- uproot: to load ROOT Trees into memory as numpy arrays
- fast-flow: to manage the processing config files
- fast-curator: to orchestrate the lists of datasets to be processed
- Espresso: to keep the developer(s) writing code
A tool from the Faster Analysis Software Taskforce: http://fast-hep.web.cern.ch/
Installation and usage
Visit the documentation for full details.