Metadata-Version: 2.1
Name: darr
Version: 0.1.10
Summary: Darr is a Python science library for storing numeric data arrays in a format that is open, simple, and self-explanatory
Home-page: https://github.com/gbeckers/darr
Author: Gabriel J.L. Beckers
Author-email: gabriel@gbeckers.nl
License: BSD-3
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Information Technology
Classifier: Intended Audience :: Science/Research
Requires: numpy
Description-Content-Type: text/markdown
Requires-Dist: numpy


Darr is a Python science library for storing and sharing numeric data arrays 
in a way that is open, simple, and self-explanatory. Save and use 
your numeric arrays and metadata with one line of code while long-term and 
tool-independent accessibility and easy shareability is ensured. In 
addition, Darr provides fast memory-mapped read/write access to such 
disk-based data and the ability to append data, , so that arrays may be
larger than available RAM.

To maximize wide readability of your data, Darr is based on a combination of
flat binary and human-readable text files. It automatically saves a
description of how the 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 array](https://github.com/gbeckers/Darr/tree/master/examplearrays/examplearray_float64.darr)).

Darr is currently pre-1.0, still undergoing significant development.

Features
--------

-   Purely based on **flat binary** and **text** files, tool independence.
-   Supports **very large data arrays** through **memory-mapped** file access.
-   Data read/write access through **NumPy indexing**
-   Data is easily **appendable**.
-   **Human-readable explanation of how the binary data is stored** is saved 
    in a README text file.
-   README also contains **examples of how to read the array** in popular 
    analysis environments such as Python (without Darr), R, Julia, 
    Octave/Matlab, GDL/IDL, Maple, and Mathematica.
-   **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 or NumExpr libraries for 
    **numeric computation on very large Darr arrays**.

See the [documentation](http://darr.readthedocs.io/) for more information.



