Metadata-Version: 2.1
Name: darr
Version: 0.2.2
Summary: Memory-mapped numeric arrays, based on a format that is self-explanatory and tool-independent
Home-page: https://github.com/gbeckers/darr
Author: Gabriel J.L. Beckers
Author-email: gabriel@gbeckers.nl
License: BSD-3
Project-URL: Source, https://github.com/gbeckers/darr
Description: 
        Darr is a Python science library that enables you to store and access
        disk-based numeric arrays, without depending on tool-specific data formats.
        This makes it easy to access the same data in many different languages and on
        different analysis platforms. No exporting required and, as the data is saved
        in a self-explanatory way, not much explanation required either when sharing
        or archiving your data. Tool-independent and easy access to data is in line
        with good scientific practice as it promotes wide and long-term availability,
        to others but also to yourself. More rationale for this approach is provided
        `here <https://darr.readthedocs.io/en/latest/rationale.html>`__.
        
        Darr supports efficient read/write/append access and is based on universally
        readable flat binary files and automatically generated text files, containing
        human-readable explanation of precisely how your binary data is stored. It
        also provides specific code that reads the 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_uint64.darr>`__).
        
        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 <https://darr.readthedocs.io/en/latest/tutorial.html>`__
        for a brief introduction, or the
        `documentation <http://darr.readthedocs.io/>`__ for more info.
        
        Darr is currently pre-1.0, still undergoing significant development. However
        we have been using it in practice in our lab for more than a year on both
        Linux and Windows machines. It is open source and freely available under the
        `New BSD License <https://opensource.org/licenses/BSD-3-Clause>`__ terms.
        
        Features
        --------
        
        Pro's:
        
        Data storage purely based on **flat binary** and **text** files, 
        tool independence.
        
        -  **Human-readable explanation of how the binary data is stored** is
           saved in a README text file.
        -  README includes **examples of how to read the particular array** in popular
           analysis environments such as Python (without Darr), R, Julia,
           Octave/Matlab, GDL/IDL, and Mathematica.
        -  Supports **very large data arrays**, larger than RAM, through memory-mapping.
        -  Data read/write access is simple and powerful through **NumPy indexing** 
           (see `here <https://docs.scipy.org/doc/numpy-1.13.0/reference/arrays.indexing.html>`__).
        -  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 <https://en.wikipedia.org/wiki/JSON>`__ text file.
        -  **Minimal dependencies**, only `NumPy <http://www.numpy.org/>`__.
        -  **Integrates easily** with the
           `Dask <https://dask.pydata.org/en/latest/>`__ library for
           **numeric computation on very large arrays**.
        -  Supports **ragged arrays** (still experimental).
        
        See the [documentation](http://darr.readthedocs.io/) for more information.
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
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-Python: >=3.6
Description-Content-Type: text/x-rst
