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