jackHI (/jak-ee/) is a software suite that contains functions to aid the creation of data-driven histograms.
Given a (one-dimensional) data set, jackHI computes a count, probability or probability density histogram based on a non-arbitrary choice of binning developed in Hogg (2008). jackHI provides standalone Python classes and functions that can be used inside your own code, as well as a CLI tool that can be used to load such data sets and compute their histogram. The software suite also provides tools to visualize the computed histograms.
Note
The CLI tool and plotting tools are under active development and are therefore not yet available (i.e. the project is still in the alpha phase).
Full documentation for jackHI can found at https://JVB11.github.io/jackHI.