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numpy array with labelled dimensions and axes, dimension, NaN handling and netCDF I/O

Project description


.. image::

Numpy array with dimensions
dimarray is a package to handle numpy arrays with labelled dimensions and axes.
Inspired from pandas, it includes advanced alignment and reshaping features and
as well as missing-value (NaN) handling.

The main difference with pandas is that it is generalized to N dimensions, and behaves more closely to a numpy array.
The axes do not have fixed names ('index', 'columns', etc...) but are
given a meaningful name by the user (e.g. 'time', 'items', 'lon' ...).
This is especially useful for high dimensional problems such as sensitivity analyses.

A natural I/O format for such an array is netCDF, common in geophysics, which relies on
the netCDF4 package, and supports metadata.

dimarray is distributed under a 3-clause ("Simplified" or "New") BSD
license. Parts of basemap which have BSD compatible licenses are included.
See the LICENSE file, which is distributed with the dimarray package, for details.

Getting started

A **``DimArray``** can be defined just like a numpy array, with
additional information about its dimensions, which can be provided
via its `axes` and `dims` parameters:

>>> from dimarray import DimArray
>>> a = DimArray([[1.,2,3], [4,5,6]], axes=[['a', 'b'], [1950, 1960, 1970]], dims=['variable', 'time'])
>>> a
dimarray: 6 non-null elements (0 null)
0 / variable (2): 'a' to 'b'
1 / time (3): 1950 to 1970
array([[1., 2., 3.],
[4., 5., 6.]])

Indexing now works on axes

>>> a['b', 1970]

Or can just be done **a la numpy**, via integer index:

>>> a.ix[0, -1]

Basic numpy transformations are also in there:

>>> a.mean(axis='time')
dimarray: 2 non-null elements (0 null)
0 / variable (2): 'a' to 'b'
array([2., 5.])

Can export to `pandas` for pretty printing:

>>> a.to_pandas()
time 1950 1960 1970
a 1.0 2.0 3.0
b 4.0 5.0 6.0

.. _links:

Useful links
================================ ====================================
Code on github (bleeding edge)
Code on pypi (releases)
Mailing List
Issues Tracker
================================ ====================================



- python >= 2.7, 3
- numpy (tested with 1.7, 1.8, 1.9, 1.10.1, 1.15)


- netCDF4 (tested with 1.0.8, 1.2.1) (netCDF archiving) (see notes below)
- matplotlib 1.1 (plotting)
- pandas 0.11 (interface with pandas)

Download the latest version from github and extract from archive
Then from the dimarray repository type (possibly preceded by sudo):

.. code:: bash

python install

Alternatively, you can use pip to download and install the version from pypi (could be slightly out-of-date):

.. code:: bash

pip install dimarray

Notes on installing netCDF4
- On Ubuntu, using apt-get is the easiest way (as indicated at

.. code:: bash

sudo apt-get install libhdf5-serial-dev netcdf-bin libnetcdf-dev

- On windows binaries are available:

- From source. Installing the netCDF4 python module from source can be cumbersome, because
it depends on netCDF4 and (especially) HDF5 C libraries that need to
be compiled with specific flags (
Detailled information on Ubuntu:

All suggestions for improvement or direct contributions are very welcome.
You can ask a question or start a discussion on the mailing list
or open an `issue` on github for precise requests. See `links`_.

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