For particle physicists, ROOT is one of the most important toolkits around. It
is a huge suite of tools that predates the C++ standard library, and has almost
anything a particle physicist could want. It has driven developments in other
areas too. ROOT’s current C++ interpreter, CLING, is the most powerful C++
interpreter available and is used by the Xeus project for Jupyter. The Python
work has helped PyPy, with CPPYY also coming from ROOT. However, due to the
size, complexity, and age of some parts of ROOT, it can be a bit challenging to
install; and it is even more challenging when you want it to talk to Python. I
would like to point to the brand-new Conda-Forge ROOT package for Linux and
macOS, and point out a few other options for macOS installs. Note for Windows
users: Due to the fact that ROOT expects the type long to match the system
pointer size, 64-bit Windows cannot be supported for quite some time. While you
can use it in 32 bit form, this is generally impossible to connect to Python,
which usually will be a 64-bit build.
Illustrations and videos
These are some illustrations and videos that I have made over the years.
[Read More]Histogram Speeds in Python
Let’s compare several ways of making Histograms. I’m going to assume you would like to end up with a nice OO histogram interface, so all the 2D methods will fill a Physt histogram. We will be using a 2 x 1,000,000 element array and filling a 2D histogram, or 10,000,000 elemends in a 1D histogram. Binnings are regular.
1D 10,000,000 item histogram
| Example | KNL | MBP | X24 |
|---|---|---|---|
| NumPy: histogram | 704 ms | 147 ms | 114 ms |
| NumPy: bincount | 432 ms | 110 ms | 117 ms |
| fast-histogram | 337 ms | 45.9 ms | 45.7 ms |
| Numba | 312 ms | 58.8 ms | 60.7 ms |
2D 1,000,000 item histogram
| Example | KNL | MBP | X24 |
|---|---|---|---|
| Physt | 1.21 s | 293 ms | 246 ms |
| NumPy: histogram2d | 456 ms | 114 ms | 88.3 ms |
| NumPy: add.at | 247 ms | 62.7 ms | 49.7 ms |
| NumPy: bincount | 81.7 ms | 23.3 ms | 20.3 ms |
| fast-histogram | 53.7 ms | 10.4 ms | 7.31 ms |
| fast-hist threaded 0.5 | (6) 62.5 ms | 9.78 ms | (6) 15.4 ms |
| fast-hist threaded (m) | 62.3 ms | 4.89 ms | 3.71 ms |
| Numba | 41.8 ms | 10.2 ms | 9.73 ms |
| Numba threaded | (6) 49.2 ms | 4.23 ms | (6) 4.12 ms |
| Cython | 112 ms | 12.2 ms | 11.2 ms |
| Cython threaded | (6) 128 ms | 5.68 ms | (8) 4.89 ms |
| pybind11 sequential | 93.9 ms | 9.20 ms | 17.8 ms |
| pybind11 OpenMP atomic | 4.06 ms | 6.87 ms | 1.91 ms |
| pybind11 C++11 atomic | (32) 10.7 ms | 7.08 ms | (48) 2.65 ms |
| pybind11 C++11 merge | (32) 23.0 ms | 6.03 ms | (48) 4.79 ms |
| pybind11 OpenMP merge | 8.74 ms | 5.04 ms | 1.79 ms |
Binding Minuit2
Let’s try a non-trivial example of a binding: Minuit2 (6.14.0 standalone edition).
[Read More]Tools to Bind to Python
This was originally given as a PyHEP 2018 talk, It is designed to be interactive, and can be run in SWAN if you have a CERN account. If you want to run it manually, just download the repository: github.com/henryiii/pybindings_cc. It is easy to run in Anaconda.
[Read More]Python 3 upgrade
About ten years ago, Guido Van Rossum, the Python author and Benevolent Dictator for Life (BDFL), along with the Python community, decided to make several concurrent backward incompatible changes to Python 2.5 and release a new version, Python 3.0.
[Read More]