Fast histogramming in python built on pybind11 and OpenMP.

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  • License: MIT License
  • Author: Doug Davis
  • Requires: Python >=3.6
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pygram11

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Simple and fast histogramming in Python accelerated with OpenMP (with help from pybind11).

pygram11 provides fast functions for calculating histograms (and the variance in each bin). The API is very simple; documentation can be found here (you'll also find some benchmarks there).

Installing

Using pygram11 only requires NumPy. To build and install from source you'll just need a compiler with support for C++11 and OpenMP.

From PyPI

Binary wheels are provided for Linux and macOS. They can be installed from PyPI via pip.

pip install pygram11

From conda-forge

For installation via the conda package manager pygram11 is part of conda-forge.

conda install pygram11 -c conda-forge

Please note that on macOS the OpenMP libraries from LLVM (libomp) and Intel (libiomp) may clash if your conda environment includes the Intel Math Kernel Library (MKL) package distributed by Anaconda. You may need to install the nomkl package to prevent the clash (Intel MKL accelerates many linear algebra operations, but does not impact pygram11):

conda install nomkl ## sometimes necessary fix (macOS only)

From Source

All you need is a C++11 compiler and OpenMP. If you are using a relatively modern GCC release on Linux then you probably don't have to worry about the OpenMP dependency. If you are on macOS, you can install libomp from Homebrew. With those dependencies met, simply run:

pip install git+https://github.com/douglasdavis/pygram11.git@master

In Action

A histogram (with fixed bin width) of weighted data in one dimension:

>>> x = np.random.randn(10000)
>>> w = np.random.uniform(0.8, 1.2, 10000)
>>> h, err = pygram11.histogram(x, bins=40, range=(-4, 4), weights=w)

A histogram with fixed bin width which saves the under and overflow in the first and last bins:

>>> x = np.random.randn(1000000)
>>> h, err = pygram11.histogram(x, bins=20, range=(-3, 3), flow=True)

A histogram in two dimensions with variable width bins:

>>> x = np.random.randn(10000)
>>> y = np.random.randn(10000)
>>> xbins = [-2.0, -1.0, -0.5, 1.5, 2.0]
>>> ybins = [-3.0, -1.5, -0.1, 0.8, 2.0]
>>> h, err = pygram11.histogram2d(x, y, bins=[xbins, ybins])

Histogramming multiple weight variations for the same data, then putting the result in a DataFrame (the input pandas DataFrame will be interpreted as a NumPy array):

>>> weights = pd.DataFrame({"weight_a" : np.abs(np.random.randn(10000)),
...                         "weight_b" : np.random.uniform(0.5, 0.8, 10000),
...                         "weight_c" : np.random.rand(10000)})
>>> data = np.random.randn(10000)
>>> count, err = pygram11.histogram(data, bins=20, range=(-3, 3), weights=weights, flow=True)
>>> count_df = pd.DataFrame(count, columns=weights.columns)
>>> err_df = pd.DataFrame(err, columns=weights.columns)

I also wrote a blog post with some simple examples.

Other Libraries

  • There is an effort to develop an object oriented histogramming library for Python called boost-histogram. This library will be feature complete w.r.t. everything a physicist needs with histograms.
  • Simple and fast histogramming in Python using the NumPy C API: fast-histogram. No weights or overflow).
  • If you want to calculate histograms on a GPU in Python, check out cupy.histogram. They only have 1D histograms (no weights or overflow).

If there is something you'd like to see in pygram11, please open an issue or pull request.

Project details

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  • License: MIT License
  • Author: Doug Davis
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