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/
Contents:
Code reference
- fast_carpenter package
- fast_carpenter.backends package
- fast_carpenter.backends.alphatwirl module
- fast_carpenter.backends.coffea module
- fast_carpenter.bookkeeping module
- fast_carpenter.define package
- fast_carpenter.define.reductions module
- fast_carpenter.define.systematics module
- fast_carpenter.define.variables module
- fast_carpenter.event_builder module
- fast_carpenter.expressions module
- fast_carpenter.masked_tree module
- fast_carpenter.selection package
- fast_carpenter.selection.filters module
- fast_carpenter.selection.stage module
- fast_carpenter.summary package
- fast_carpenter.summary.aghast module
- fast_carpenter.summary.binned_dataframe module
- fast_carpenter.summary.binning_config module
- fast_carpenter.summary.event_level_dataframe module
- fast_carpenter.summary.import_aghast module
- fast_carpenter.tree_wrapper module
- fast_carpenter.utils module
- fast_carpenter.version module