Darr: A Python science library for storing and sharing numeric data arrays in a way that is open, simple, and self-explanatory

Darr is a Python science library for efficient read/write/append access to disk-persistent numeric data arrays. There are other Python libraries for this, but Darr also ensures tool-independent and long-term accessibility of your data. It saves and automatically updates a human-readable explanation of how your binary data is stored, together with code for reading the specific data in a variety of current scientific data tools such as Python, R, Julia, IDL, Matlab, Maple, and Mathematica (see example arrays).

In essence, Darr enables you to work with potentially very large data arrays in a Python/NumPy environment, and share this data as is with others who do not use Darr, or even Python, without exporting anything. It is also an easy way to make sure you can read your own data in the future when you may use different tools.

Darr currently supports numerical N-dimensional arrays, and experimentally supports numerical ragged arrays, i.e. a series of arrays in which one dimension varies in length.

See this tutorial for a brief introduction, or the documentation for more info.

Darr is currently pre-1.0, still undergoing significant development. It is open source and freely available under the New BSD License terms.