Tools for scaling up

Scaling up¶

The tools described in these lessons are intended to be used within a script that is scaled up for large datasets.

You could use any of them in an ordinary GRID job (or other batch processor).

However, the Coffea project (documentation) is building a distributed ecosystem that integrates Pythonic analysis with data analysis farms. This is too large of a subject to cover here, but check out the software and join the Coffea user meetings if you’re interested.

Scikit-HEP Resources¶

• scikit-hep.org

• Uproot GitHub, documentation

• Awkward Array GitHub, documentation

• boost-histogram GitHub, documentation

• hist GitHub, documentation

• Unified Histogram Interface GitHub, documentation

• mplhep GitHub, documentation

• iminuit GitHub, documentation

• zfit GitHub, documentation

• Vector GitHub, documentation

• Particle GitHub, documentation

• hepunits GitHub

• fastjet GitHub, documentation

• pyhf GitHub, documentation

• hepstats GitHub, documentation

• cabinetry GitHub, documentation

• histoprint GitHub

• decaylanguage GitHub, documentation

• GooFit GitHub, documentation

• pyhepmc GitHub

• pylhe GitHub

and finally

• cookie GitHub, documentation, a template for making your own...