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Description:

pygram11 0.13.2

pygram11

Simple and fast histogramming in Python accelerated with
OpenMP with help from
pybind11.
pygram11 provides functions for very fast histogram calculations
(and the variance in each bin) in one and two dimensions. The API is
very simple; documentation can be found
here (you'll also find some
benchmarks
there).
Installing
From PyPI
Binary wheels are provided for Linux, macOS, and Windows. 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

From Source
You need is a C++14 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 (pygram11 does compile on Apple Silicon devices
with Python version >= 3.9 and libomp installed from Homebrew).
With those dependencies met, simply run:
git clone https://github.com/douglasdavis/pygram11.git --recurse-submodules
cd pygram11
pip install .

Or let pip handle the cloning procedure:
pip install git+https://github.com/douglasdavis/pygram11.git@main

Tests are run on Python versions >= 3.8 and binary wheels are
provided for those versions.
In Action
A histogram (with fixed bin width) of weighted data in one dimension:
>>> rng = np.random.default_rng(123)
>>> x = rng.standard_normal(10000)
>>> w = rng.uniform(0.8, 1.2, x.shape[0])
>>> 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 = rng.standard_normal(1000000)
>>> h, __ = pygram11.histogram(x, bins=20, range=(-3, 3), flow=True)

where we've used __ to catch the None returned when weights are
absent. A histogram in two dimensions with variable width bins:
>>> x = rng.standard_normal(1000)
>>> y = rng.standard_normal(1000)
>>> xbins = [-2.0, -1.0, -0.5, 1.5, 2.0, 3.1]
>>> ybins = [-3.0, -1.5, -0.1, 0.8, 2.0, 2.8]
>>> h, err = pygram11.histogram2d(x, y, bins=[xbins, ybins])

Manually controlling OpenMP acceleration with context managers:
>>> with pygram11.omp_disabled(): # disable all thresholds.
... result, _ = pygram11.histogram(x, bins=10, range=(-3, 3))
...
>>> with pygram11.omp_forced(key="thresholds.var1d"): # force a single threshold.
... result, _ = pygram11.histogram(x, bins=[-3, -2, 0, 2, 3])
...

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):
>>> N = 10000
>>> weights = pd.DataFrame({"weight_a": np.abs(rng.standard_normal(N)),
... "weight_b": rng.uniform(0.5, 0.8, N),
... "weight_c": rng.uniform(0.0, 1.0, N)})
>>> data = rng.standard_normal(N)
>>> 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

boost-histogram
provides Pythonic object oriented histograms.
Simple and fast histogramming in Python using the NumPy C API:
fast-histogram (no
variance or overflow support).
To calculate histograms in Python on a GPU, see
cupy.histogram.

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