Scipp: Scientific data handling with labeled multi-dimensional arrays for C++ and Python

TL;DR

Scipp is a C++ and Python library that enhances multi-dimensional data handling with named axes, units, uncertainties, and support for histograms and event data, improving accuracy and usability in scientific computing.

Contribution

It combines multiple advanced features like units, uncertainties, and histograms into a single coherent package, which was not previously available together.

Findings

Enables more natural and concise data analysis workflows.

Reduces programming errors through named dimensions and units.

Provides performance benefits via a C++ core.

Abstract

Scipp is heavily inspired by the Python library xarray. It enriches raw NumPy-like multi-dimensional arrays of data by adding named dimensions and associated coordinates. Multiple arrays are combined into datasets. On top of this, scipp introduces (i) implicit handling of physical units, (ii) implicit propagation of uncertainties, (iii) support for histograms, i.e., bin-edge coordinate axes, which exceed the data's dimension extent by one, and (iv) support for event data. In conjunction these features enable a more natural and more concise user experience. The combination of named dimensions, coordinates, and units helps to drastically reduce the risk for programming errors. The core of scipp is written in C++ to open opportunities for performance improvements that a Python-based solution would not allow for. On top of the C++ core, scipp's Python components provide functionality for plotting and content representations, e.g., for use in Jupyter Notebooks. While none of scipp's concepts in isolation is novel per-se, we are not aware of any project combining all of these aspects in a single coherent software package.