Introduction

.. image:: https://github.com/perrette/dimarray/actions/workflows/tox.yml/badge.svg :target: https://github.com/perrette/dimarray/actions/workflows/tox.yml

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.

License

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] 6.0

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

a.ix[0, -1] 3.0

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 variable
a 1.0 2.0 3.0 b 4.0 5.0 6.0

.. _links:

Useful links

================================ ==================================== Documentation http://dimarray.readthedocs.org Code on github (bleeding edge) https://github.com/perrette/dimarray Code on pypi (releases) https://pypi.python.org/pypi/dimarray Issues Tracker https://github.com/perrette/dimarray/issues ================================ ====================================

Install

Requirements:

• python >= 3.7
• numpy (latest test with version 1.21.5)

Optional:

• 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 setup.py 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 https://github.com/Unidata/netcdf4-python/blob/master/.travis.yml):

.. code:: bash

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

• On windows binaries are available: http://www.unidata.ucar.edu/software/netcdf/docs/winbin.html

• 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 (http://unidata.github.io/netcdf4-python). Detailled information on Ubuntu: https://code.google.com/p/netcdf4-python/wiki/UbuntuInstall

Contributions

All suggestions for improvement or direct contributions are very welcome. You can open an issue on github for specific requests.