Series of lectures "Big Data Python ecosystem for HEP" for the LIV.DAT STFC School on Data Intensive Science 2020 held as a virtual event on 12-16 October 2020.
A nice rendering of the notebooks can be obtained with nbviewer at https://nbviewer.jupyter.org/github/eduardo-rodrigues/2020-10-13_LIV.DAT_school/tree/master/.
Data analysis in High Energy Physics (HEP) has evolved considerably in recent years. In particular, the role of Python has been gaining much momentum, sharing at present the show with C++ as a language of choice. Several (community) domain-specific projects have seen the day, providing (HEP) data analysis packages that profit from, and talk to well with, the huge Python scientific ecosystem, which navigates around NumPy and friends. In this "Big Data Python ecosystem for HEP" session I will present and discuss a large set of this new HEP ecosystem ever more used by analysts across several experiments such as the LHC experiments but also Belle II, KM3NeT and others. Ample time will be provided to "play around" with the material, in Jupyter notebooks.
One can straightforwardly run this notebook presentation on Binder, with no installation requirements on the user side (Be aware that the environment to set up is rather large and it may take 5-10 minutes for the notebooks to be up and running online.)
Just click on
Anything can be altered along the way.
Note that the notebook uses Python 3, though most (but not all!) packages also support Python 2 for now.
Alternatively, you may want to download the repository and play with the notebooks, saving for example some of the plots they produce.
git clone https://github.com/eduardo-rodrigues/2020-10-13_LIV.DAT_school.git
cd 2020-10-13_LIV.DAT_school