A Python science library that stores NumPy arrays in a way that is self-documented and tool-independent.
copied from cf-staging / darrDarr is a Python science library for disk-based NumPy arrays that persist in a format that is simple, self-documented and tool-independent. It enables you to work efficiently with potentially very large arrays, while keeping your data easily accessible from a wide range of computing environments. Even if you don't work with very large arrays, Darr is a convenient way to store your arrays in a way that keeps them universally readable and documented, which is in line with good scientific practice. Auto-documentation includes code for reading the array in a variety of current scientific data tools such as Python, R, Julia, IDL, Matlab, Maple, and Mathematica. It is trivially easy to share your data with others or with yourself when working in different computing environments, no exporting or much explanation required.
