Overview

Teaching: 10 min
Exercises: 0 min

Questions

• How do I turn working code fragments into batch jobs?

• Where can I look for more help?

Objectives

• Learn where to go next.

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…

Key Points

• See Coffea for more about scaling up your analysis.

• Pythonic high-energy physics is a broad and growing ecosystem.