Metadata-Version: 2.1
Name: darr
Version: 0.1.7
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 numeric data arrays in a way
that is open, simple, and self-explanatory. It enables fast memory-mapped
read/write access to such disk-based data, the ability to append data, and
the flexible use of metadata. It is primarily designed for scientific use
cases. 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.

To avoid dependency on specific tools, Darr is based on a combination of
flat binary and human-readable text files. It automatically saves a clear
text 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, Matlab and Mathematica.


Features
--------

-   **Transparent data format** based on **flat binary** and **text** files.
-   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, 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.



