darr 0.4.0

Memory-mapped numeric arrays, based on a format that is self-explanatory and tool-independent

Darr is a Python science library to work with potentially large NumPy arrays and metadata that persist on disk, in a format that is simple, self-documented and tool-independent. The goal is to keep your data easily accessible on the short and long term, from a wide range of computing environments. Keeping data universally readable and documented is in line with good scientific practice. It not only makes it easy to share data with others, but also to look at you own data with different tools. More rationale for this approach is provided here.

Flat binary files and (JSON) text files are accompanied by a README text file that explains how the specific data and metadata are stored and how they can be read. This 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, because it always contains a clear and specific description of how to read it. No need to export anything or to provide elaborate explanation. No dependence on complicated formats or specialized tools.

Darr uses NumPy memmory-mapped arrays under the hood, which you can access directly for full NumPy compatibility and efficient out-of-core read/write access to potentially very large arrays. In addition, Darr supports the possibility to append and truncate arrays, and the use of ragged arrays (still experimental).

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.

Features

Pro’s:

• Data persists on-disk, purely based on flat binary and text files, tool independence.

• README text file with human-readable explanation of how the binary data is stored.

• README includes examples of how to read the array in a number of popular data analysis environments, such as Python (without Darr), R, Julia, Octave/Matlab, GDL/IDL, and Mathematica (see example array).

• Works with data arrays larger than RAM.

• Data read/write access is simple and powerful through NumPy indexing (see here).

• Data is easily appendable.

• Many numeric types are supported: (u)int8-(u)int64, float16-float64, complex64, complex128.

• Easy use of metadata, stored in a separate JSON text file.

• Minimal dependencies, only NumPy.

• Integrates easily with the Dask library for out-of-core computation on very large arrays.

• Supports ragged arrays (still experimental).

See the documentation for more information.