There are more than enough histogramming packages for python out in the wild, and some have a lot of functionality beyond dropping data into bins.

  • This library aims to provide a way to drop data into bins quickly with a simple (and hopefully easy to install) code base.
  • A property of histograms lacking from other options is the ability to retrieve the sum of weights squared in each bin (it’s possible in NumPy, but not directly from the histogramming functions). This calculation is a “first class citizen” in pygram11.
  • Finally, I thought it would be fun to learn how to write software with OpenMP and pybind11 because I had not used either piece of software before. Hopefully having them in my tool belt will be useful for a more complex future project. The idea came to me while I was sitting waiting for \(\mathcal{O}(1000)\) histograms to be generated (one of many steps in the workflow for my thesis analysis).

Some of the other options that you might find useful:

  • numpy.histogram: versatile but slow; doesn’t handle sum of weights squared
  • fast-histogram: leverages NumPy’s C API. Very fast (fixed bin only) histogramming and easy to install; no OpenMP support or sum of weights squared.
  • physt: way more than just sorting data into bins.

Some Benchmarks

Here are a couple of benchmarks testing pygram11 (labeled pg11) against numpy.histogram (labeled np) and fast-histogram (labeled fh). These were performed on a MacBook Pro with a 2.6GHz 12 Core Intel i7 (6 hyperthreaded cores) processor.

The \(y\)-axis is the ratio of the times to complete the calculation from two different packages; the higher the ratio, the faster the denominator.

The first plot shows use of pygram11 without OpenMP acceleration. For the second plot, we turn on OpenMP acceleration.

_images/compare.png _images/compare_omp.png

Without OpenMP, fast-histogram outperforms pygram11 across the board. With OpenMP, pygram11 starts to outperform fast-histogram when the array size exceeds 10,000 entries. At 1,000,000 entries, pygram11 appears to be up to 3x faster than fast-histogram, and 100x faster than numpy. For very small arrays, the overhead to spin up the parallel loops via OpenMP is observable.

For variable width binning we just compare pygram11 to NumPy:

_images/compare_var.png _images/compare_var_omp.png

Here we see pygram11 is always useful, and OpenMP becomes helpful for arrays exceeding about 1,000 entries. For large arrays we approach speeds 40x(10x) faster than NumPy in one(two)-dimension(s) with OpenMP enabled.