ANN: PyWavelets v0.5.0 release

[Gregory Lee]

On behalf of the PyWavelets development team I am pleased to announce the
release of PyWavelets 0.5.0.


PyWavelets is a Python toolbox implementing both discrete and continuous
wavelet transforms (mathematical time-frequency transforms) with a wide
range of built-in wavelets.  C/Cython are used for the low-level routines,
enabling high performance.  Key Features of PyWavelets are:

  * 1D, 2D and nD Forward and Inverse Discrete Wavelet Transform (DWT and
IDWT)
  * 1D, 2D and nD Multilevel DWT and IDWT
  * 1D and 2D Forward and Inverse Stationary Wavelet Transform
  * 1D and 2D Wavelet Packet decomposition and reconstruction
  * 1D Continuous Wavelet Transform
  * When multiple valid implementations are available, we have chosen to
maintain consistency with MATLAB's Wavelet Toolbox.

PyWavelets 0.5.0 is the culmination of 1 year of work.  In addition to
several new features, substantial refactoring of the underlying C and
Cython code have been made.

Highlights of this release include:

- 1D continuous wavelet transforms
- new discrete wavelets added (additional Debauchies and Coiflet wavelets)
- new 'reflect' extension mode for discrete wavelet transforms
- faster performance for multilevel forward stationary wavelet transforms
(SWT)
- n-dimensional support added to forward SWT
- routines to convert multilevel DWT coefficients to and from a single array
- axis support for multilevel DWT
- substantial refactoring/reorganization of the underlying C and Cython code

Full details in the release notes at:
http://pywavelets.readthedocs.io/en/latest/release.0.5.0.html

This release requires Python 2.6, 2.7 or 3.3-3.5 and Numpy 1.9.1 or
greater. Sources can be found on https://pypi.python.org/pypi/PyWavelets
and https://github.com/PyWavelets/pywt/releases.

As always, new contributors are welcome to join us at
https://github.com/PyWavelets/pywt