Metadata-Version: 1.1
Name: datarray
Version: 0.1.0
Summary: NumPy arrays with named axes and named indices.
Home-page: http://github.com/bids/datarray
Author: Numpy Developers
Author-email: numpy-discussion@scipy.org
License: Simplified BSD
Download-URL: http://github.com/bids/datarray/archives/master
Description: 
        ######################################
        Datarray: Numpy arrays with named axes
        ######################################
        
        Scientists, engineers, mathematicians and statisticians don't just work with
        matrices; they often work with structured data, just like you'd find in a
        table. However, functionality for this is missing from Numpy, and there are
        efforts to create something to fill the void.  This is one of those efforts.
        
        .. warning::
        
           This code is currently experimental, and its API *will* change!  It is meant
           to be a place for the community to understand and develop the right
           semantics and have a prototype implementation that will ultimately
           (hopefully) be folded back into Numpy.
        
        Datarray provides a subclass of Numpy ndarrays that support:
        
        - individual dimensions (axes) being labeled with meaningful descriptions
        - labeled 'ticks' along each axis
        - indexing and slicing by named axis
        - indexing on any axis with the tick labels instead of only integers
        - reduction operations (like .sum, .mean, etc) support named axis arguments
          instead of only integer indices.
        
        *********
        Prior Art
        *********
        
        In no particular order:
        
        * `xray <http://xarray.pydata.org/en/stable>`_ - very close in spirit to this
          package, xray implements named ND array axes and tick labels.  It integrates
          with (and depends on) Pandas;
        
        * `pandas <http://pandas.pydata.org>`_ is based around a number of
          DataFrame-esque datatypes.
        
        * `Tabular <http://bitbucket.org/elaine/tabular/src>`_ implements a
          spreadsheet-inspired datatype, with rows/columns, csv/etc. IO, and fancy
          tabular operations.
        
        * `scikits.statsmodels <http://scikits.appspot.com/statsmodels>`_ sounded as
          though it had some features we'd like to eventually see implemented on top of
          something such as datarray, and `Skipper <http://scipystats.blogspot.com>`_
          seemed pretty interested in something like this himself.
        
        * `scikits.timeseries <http://scikits.appspot.com/timeseries>`_ also has a
          time-series-specific object that's somewhat reminiscent of labeled arrays.
        
        * `pandas <http://pandas.pydata.org>`_ is based around a number of
          DataFrame-esque datatypes.
        
        * `pydataframe <https://pypi.python.org/pypi/pydataframe>`_ is supposed to be a
          clone of R's data.frame.
        
        * `larry <http://github.com/kwgoodman/la>`_, or "labeled array," often comes up
          in discussions alongside pandas.
        
        * `divisi <http://github.com/commonsense/divisi2>`_ includes labeled sparse and
          dense arrays.
        
        * `pymvpa <https://github.com/PyMVPA/PyMVPA>`_ provides Dataset class
          encapsulating the data together with matching in length sets of attributes
          for the first two (samples and features) dimensions.  Dataset is not a
          subclass of numpy array to allow other data structures (e.g. sparse
          matrices).
        
        * `ptsa <http://git.debian.org/?p=pkg-exppsy/ptsa.git>`_ subclasses
          ndarray to provide attributes per dimensions aiming to ease slicing/indexing
          given the values of the axis attributes
        
        *************
        Project Goals
        *************
        
        1. Get something akin to this in the numpy core;
        2. Stick to basic functionality such that projects like scikits.statsmodels can
           use it as a base datatype;
        3. Make an interface that allows for simple, pretty manipulation that doesn't
           introduce confusion;
        4. Oh, and make sure that the base numpy array is still accessible.
        
        ****
        Code
        ****
        
        You can find our sources and single-click downloads:
        
        * `Main repository`_ on Github;
        * Documentation_ for the current release;
        * Download the `current trunk`_ as a tar/zip file;
        * Downloads of all `available releases`_.
        
        The latest released version is always available from `pypi
        <https://pypi.python.org/pypi/datarray>`_.
        
        *******
        Support
        *******
        
        Please put up issues on the `datarray issue tracker
        <https://github.com/bids/datarray/issues>`_.
        
        .. _main repository: http://github.com/bids/datarray
        .. _Documentation: http://bids.github.com/datarray
        .. _current trunk: http://github.com/bids/datarray/archives/master
        .. _available releases: http://github.com/bids/datarray/releases
        
Platform: OS Independent
Classifier: Development Status :: 3 - Alpha
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering
Requires: numpy (>=1.7)
