PyWavelets
==========

.. contents::

What is PyWavelets
------------------

PyWavelets is a free Open Source library for wavelet transforms in Python.
Wavelets are mathematical basis functions that are localized in both time and
frequency.  Wavelet transforms are time-frequency transforms employing
wavelets.  They are similar to Fourier transforms, the difference being that
Fourier transforms are localized only in frequency instead of in time and
frequency.

The main features of PyWavelets are:

  * 1D, 2D and nD Forward and Inverse Discrete Wavelet Transform (DWT and IDWT)
  * 1D and 2D Stationary Wavelet Transform (Undecimated Wavelet Transform)
  * 1D and 2D Wavelet Packet decomposition and reconstruction
  * Approximating wavelet and scaling functions
  * Over seventy `built-in wavelet filters`_
    and custom wavelets supported
  * Single and double precision calculations
  * Results compatible with Matlab Wavelet Toolbox (TM)


Documentation
-------------

Documentation with detailed examples and links to more resources is available
online at http://pywavelets.readthedocs.org.

For more usage examples see the `demo`_ directory in the source package.


Installation
------------

PyWavelets supports `Python`_ 2.6, 2.7 or >=3.3, and is only dependent on `Numpy`_
(supported versions are currently ``>= 1.6.2``).

Binaries for Windows and OS X (wheels) on PyPi are in the works, however
currently PyWavelets has to be installed from source.  To do so, a working C
compiler (any common one will work) and a recent version of `Cython`_ is required.

Binary packages for several Linux distributions can be found, but may be out of date.
Query your Linux package manager tool for `python-pywavelets`,
`python-wavelets`, `python-pywt` or a similar package name.

- Install PyWavelets with ``pip install PyWavelets``.

- To build and install from source, navigate to the PyWavelets source
  code directory and type ``python setup.py install``.

The most recent *development* version can be found on GitHub at
https://github.com/PyWavelets/pywt.

The latest release, including source and binary packages for Windows, is
available for download from the `Python Package Index`_ or on the
`Releases Page`_.


State of development & Contributing
-----------------------------------

PyWavelets started in 2006 as an academic project for a master thesis
on `Analysis and Classification of Medical Signals using Wavelet Transforms`
and was maintained until 2012 by its `original developer`_.  In 2013
maintenance was taken over in a `new repo <https://github.com/PyWavelets/pywt>`_)
by a larger development team - a move supported by the original developer.
The repo move doesn't mean that this is a fork - the package continues to be
developed under the name "PyWavelets", and released on PyPi and Github (see
`this issue <https://github.com/nigma/pywt/issues/13>`_ for the discussion
where that was decided).

All contributions including bug reports, bug fixes, new feature implementations
and documentation improvements are welcome.  Moreover, developers with an
interest in PyWavelets are very welcome to join the development team!


Contact
-------

Use `GitHub Issues`_ or the `mailing list`_ to post your
comments or questions.


License
-------

PyWavelets is a free Open Source software released under the MIT license.



.. _built-in wavelet filters: http://wavelets.pybytes.com/
.. _Cython: http://cython.org/
.. _demo: https://github.com/PyWavelets/pywt/tree/master/demo
.. _GitHub: https://github.com/PyWavelets/pywt
.. _GitHub Issues: https://github.com/PyWavelets/pywt/issues
.. _Numpy: http://www.numpy.org
.. _original developer: http://en.ig.ma
.. _Python: http://python.org/
.. _Python Package Index: http://pypi.python.org/pypi/PyWavelets/
.. _mailing list: http://groups.google.com/group/pywavelets
.. _Releases Page: https://github.com/PyWavelets/pywt/releases
