# Introducing pygram11

tags: python numpy cpp hep

I’m very happy to release my first open source software project: pygram11. I’ve been writing software for a while now, but mostly targeting physics-experiment-specific use cases. In that time I’ve used a lot free and open source software; it feels quite nice to potentially help contribute to the scientific computing community in the same way.

This python library aims to make generating many histograms a quick task (targeting sample sizes above about 10,000 elements), while also supporting weighted statistical uncertainties on the bin values. Fixed and variable bin width histograms can be calculated. The backend implementation is in C++ and accelerated with OpenMP, with pybind11 used to generate the Python bindings.

Pygram11 can be a near drop-in replacement for numpy.histogram and numpy.histogram2d, while reaching speeds 20x faster (for a 1D histogram of an array of length 10,000) to almost 100x faster than NumPy (for a 2D histogram of 100 million $$(x_i, y_i)$$ pairs). The APIs are quite similar (with slightly different return styles). In addition to the faster calculations, constructing the variance in each bin is a “first class citizen” in pygram11 (see my NumPy Histogram tricks for HEP post).

So, please go checkout the documentation and GitHub repository. Open issues, PRs, email me, tweet at me, or write something better (checkout some benchmarks in the documentation). To try it out, spin up a virtual environment or conda environment and install with:

pip install pygram11


or

conda install pygram11 -c conda-forge


## In action

Some fixed bin histogramming:

import numpy as np
from pygram11 import histogram, histogram2d

x = np.random.randn(100000)
y = np.random.randn(100000)
w = np.random.uniform(0.8, 1.2, 100000)

h_1d, _ = histogram(x, bins=20, range=(-4, 4))
h_2d, _ = histogram2d(x, y, bins=(20, 40),
range=((-4, 4), (-3, 3)))

h_1d, err_1d = histogram(x, bins=20, range=(-4, 4), weights=w)
h_2d, err_2d = histogram2d(x, y, bins=(20, 40),
range=((-4, 4), (-3, 3)), weights=w)


Notice the error (square-root of the variance) is the second return object (for the unweighted histogram we just throw it away with an underscore).

And some variable bin histogramming, uniform logarithmic:

import numpy as np
from pygram11 import histogram

x = np.exp(np.random.uniform(0.1, 10.0, 100000))
bins = np.logspace(0.1, 1.0, 10, endpoint=True)

h, _ = histogram(x, bins=bins)