Turns your trees into tables (ie. reads ROOT TTrees, writes summary Pandas DataFrames)

fast-carpenter can:

• 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

Powered by:

• 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.