ROOT Install Options

New Conda Forge package of ROOT for Unix and more options

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.

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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: 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
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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. The main changes were:

  • Using unicode strings as default, with the old string type becoming a full featured binary type
  • Changing several builtins, for example
    • The print statement became a function, allowing more consistent syntax and the use of the word print as a name
    • The confusing input removed, and raw_input now renamed to input
    • Simpler exec
    • Division is now split between / float division and // truncating division
  • Improved exception tracing, with chaining
  • Improved function call syntax with annotations and keyword only arguments, replacing little used tuple parameter unpacking
  • More class constructor features, such as nicer metaclass syntax, keyword arguments, __prepare__
  • Renamed standard libraries, to be more consistent
  • Removal of a lot of depreciated features, including old-style classes
  • Removal of a lot of depreciated syntax that had become learner stumbling blocks
  • Adding nonlocal variables
  • Extended tuple unpacking, like first, *rest = makes_a_tuple()
  • Removing the proliferation of .pyc files, instead using __pycache__ directories
  • Automatic selection of C-based standard library modules over pure Python ones if available
  • Unified the int and long types into one unlimited length integer type
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