PyWavelets - Discrete Wavelet Transform in Python
PyWavelets is a free Open Source wavelet transform software for Python programming language. It is written in Python, Cython and C for a mix of easy and powerful high-level interface and the best performance.
PyWavelets is very easy to start with and use, and currently is capable of:
- 1D and 2D 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 compatibility with Matlab Wavelet Toolbox (tm)
PyWavelets is a package for the Python programming language. It requires:
The most recent development version can be found on GitHub at https://github.com/nigma/pywt.
Latest release, including source and binary package for Windows, is available for download from the Python Package Index.
In order to build PyWavelets from source, a working C compiler (GCC or MSVC) and a recent version of Cython is required.
- To install PyWavelets open shell prompt and type
pip install PyWaveletsor
- To build and install from source, navigate to downloaded PyWavelets source code directory and type
python setup.py install.
- The in-development version of PyWavelets can be installed with
pip install PyWavelets==devor
Prebuilt Windows binaries and source code packages are also available from Python Package Index.
Binary packages for several Linux distributors are maintained by Open Source community contributors. Query your Linux package manager tool for python-wavelets, python-pywt or similar package name.
Documentation with detailed examples and links to more resources is available online at http://www.pybytes.com/pywavelets/.
For more usage examples see the demo directory in the source package.
PyWavelets started in 2006 as an academic project for a master thesis on Analysis and Classification of Medical Signals using Wavelet Transforms and is maintained by its original developer.
All contributions including bug reports, bug fixes, new feature implementations and documentation improvements are welcome.
Go and fork on GitHub today!
Python 3 development branch is at https://github.com/nigma/pywt/tree/py-3. Check out the changelog for info. Currently the code and examples are ported to work on Python 2.7 and 3.2 from the same codebase.
To contact me directly email me at email@example.com.
PyWavelets is a free Open Source software released under the MIT license.