Metadata-Version: 1.0
Name: dimarray
Version: 0.1.1
Summary: array with labelled dimensions and axes, metadata and NaN handling
Home-page: https://github.com/perrette/dimarray
Author: Mahe Perrette
Author-email: mahe.perrette@pik-potsdam.de
License: BSD 3-Clause
Description: dimarray: array with labelled dimensions 
        ========================================
        
        Download dimarray on `github <https://github.com/perrette/dimarray/>`_
        or just take a look at this notebook on
        `nbviewer <http://nbviewer.ipython.org/github/perrette/dimarray/blob/master/dimarray.ipynb>`_
        
        Table of Content
        ~~~~~~~~~~~~~~~~
        
        -  `Get started <#Get-started>`_
        -  `Alternative definitions <#Alternative-definitions>`_
        -  `Indexing <#Indexing>`_
        
           -  `Basics: integer, array, slice <#Basics:-integer,-array,-slice>`_
           -  `Modify array values <#Modify-array-values>`_
           -  `take and put methods <#take-and-put-methods>`_
        
        -  `Numpy Transformations <#Numpy-Transformations>`_
        -  `Missing values <#Missing-values>`_
        -  `Modify array shape <#Modify-array-shape>`_
        
           -  `transpose <#transpose>`_
           -  `newaxis <#newaxis>`_
           -  `reshape <#reshape>`_
           -  `group and ungroup
              [Experimental] <#group-and-ungroup-[Experimental]>`_
        
        -  `Repeat and broadcast: align
           dimensions <#Repeat-and-broadcast:-align-dimensions>`_
        
           -  `repeat <#repeat>`_
           -  `broadcast <#broadcast>`_
           -  `broadcast\_arrays <#broadcast_arrays>`_
        
        -  `Reindexing: align axes <#Reindexing:-align-axes>`_
        
           -  `reindex\_axis <#reindex_axis>`_
           -  `reindex\_like <#reindex_like>`_
           -  `Interpolation <#Interpolation>`_
           -  `align\_axes <#align_axes>`_
        
        -  `Join arrays <#Join-arrays>`_
        
           -  `concatenate arrays along existing
              axis <#concatenate-arrays-along-existing-axis>`_
           -  `join arrays along new axis <#join-arrays-along-new-axis>`_
           -  `aggregate arrays of varying dimensions
              [Experimental] <#aggregate-arrays-of-varying-dimensions-[Experimental]>`_
        
        -  `Operations <#Operations>`_
        
           -  `Basic Operations <#Basic-Operations-------->`_
           -  `Operation with data alignment <#Operation-with-data-alignment->`_
        
        -  `Dataset <#Dataset>`_
        -  `NetCDF I/O <#NetCDF-I/O>`_
        -  `Experimental Features <#Experimental-Features>`_
        
           -  `Metadata <#Metadata>`_
           -  `Weighted mean <#Weighted-mean>`_
           -  `Compatibility with pandas and
              larry <#Compatibility-with-pandas-and-larry>`_
        
        -  `doctest framework <#doctest-framework>`_
        
        
        Notebook:
        ---------
        http://nbviewer.ipython.org/github/perrette/dimarray/blob/master/dimarray.ipynb
        
        
        Summary:
        --------
        
        Inspired by (but does not rely on) pandas:
        
        * behave like a numpy array (operations, transformations)
        * labelled axes, NaN handling
        * automatic axis aligment for +-/* between two DimArray objects
        * similar api (`values`, `axes`,`reindex_axis`) 
        * group/ungroup methods to flatten any subset of dimensions into a 
          GroupedAxis object, in some ways similar to pandas' MultiIndex.
        
        But generalized to any dimension and augmented with new features:
        
        * intuitive multi-dimensional slicing/reshaping/transforms by axis name
        * arithmetics between arrays of different dimensions (broadcasting)
        * can assign weights to each axis (such as based on axis spacing)
          ==> `mean`, `var`, `std` can be weighted
        * in combination to `group`, can achieve area- or volumne- weighting
        * natural netCDF I/O  via netCDF4 python module (requires HDF5, netCDF4)
        * stick to numpy's api when possible (but with enhanced capabilities):
          `reshape`, `repeat`, `transpose`, `newaxis`, `squeeze`
              
        
        Organized around a small number of classes and methods:
        
        * DimArray			: main data structure 
        * Dataset		    	: ordered dictionary of DimArray objects
        * read_nc, write_nc, summary_nc : netCDF I/O (DimArray and Dataset methods)
        * Axis, Axes, GroupedAxis   : axis and indexing (under the hood)
        
        And for things pandas does better (low-dimensional data analysis, `groupby`, 
        I/O formats, etc...), just export via to_pandas() method (up to 4-D) (only
        if pandas is installed of course - otherwise dimarray does not rely on pandas)
        
Keywords: labelled array,numpy,larry,pandas,iris
Platform: UNKNOWN
