Fast histogramming in python built on pybind11 and OpenMP.
Project description
pygram11
Simple and fast histogramming in Python accelerated with OpenMP (with help from pybind11).
pygram11 provides fast functions for calculating histograms (and
their statistical uncertainties). The API is very simple;
documentation can be found here (you'll
also find some
benchmarks
there).
Note: the last version of pygram11 supporting Python 2 is 0.5.2.
Installing
pygram11 only requires NumPy. To build from source you'll need a C++ compiler with C++11 support.
From PyPI
Binary wheels are provided for Linux (starting with version 0.5.0) and macOS (starting with version 0.5.1), they can be installed from PyPI via pip.
pip install pygram11
From conda-forge
For a simple installation process 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) can 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
pip install git+https://github.com/douglasdavis/pygram11.git@master
To ensure OpenMP acceleration in a build from source, read the OpenMP
section of the docs. If you have a modern GCC verion on Linux, you
probably don't have to worry about anything. If you are on macOS,
you'll probably want to install libomp from Homebrew.
Note: For releases older than v0.5, when building from source or
PyPI, pybind11 was required to be explicitly installed before
pygram11 (because setup.py used pybind11 to determine include
directories). Starting with v0.5 pybind11 is bundled as a git
submodule for installations from source.
In Action
A histogram (with fixed bin width) of weighted data in one dimension, accelerated with OpenMP:
>>> x = np.random.randn(10000)
>>> w = np.random.uniform(0.8, 1.2, 10000)
>>> h, staterr = pygram11.histogram(x, bins=40, range=(-4, 4), weights=w, omp=True)
A histogram with fixed bin width which saves the under and overflow in
the first and last bins (using __ to catch the None returned due
to the absence of weights):
>>> x = np.random.randn(1000000)
>>> h, __ = pygram11.histogram(x, bins=20, range=(-3, 3), flow=True, omp=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, __ = 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, omp=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.
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