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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.


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