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F.A.S.T. package for summarizing ROOT TTrees

Project description

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fast-carpenter

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
  • coffee: to help the developer(s) write code

Installation

Can be installed from pypi:

pip install --user fast-carpenter

or if you want to be able to edit code in this repo:

pip install --user -e git+https://gitlab.cern.ch/fast-hep/public/fast-carpenter.git#egg=fast_carpenter --src .

Note that to use this repository and the main fast_carpenter command, you normally shouldn't need to be able to edit this codebase; in most instances the full analysis should be describable with just a config file, and in some cases custom, analysis-specific stages to create more tricky variables for example.

Also note that if you install this with pip, the main executable, fast_carpenter, will only be available everywhere if include the directory ~/.local/bin in your PATH variable.

Documentation

Basic usage:

  1. Build a description of the datasets you wish to process using the fast_curator command from the fast-curator package.
  2. Write a description of what you want to do with your data (see documentation below).
  3. Run things:
fast_carpenter datasets.yaml processing.yaml

You can use the built-in help as well for more info:

fast_carpenter --help

The processing config file

... is on its way...

Example analysis

...is also on its way...

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