This library provides support for computing 1D, 2D and 3D dual-tree complex wavelet transforms and their inverse in Python. Full documentation is available online.

The easiest way to install dtcwt is via easy_install or pip:

$ pip install dtcwt

If you want to check out the latest in-development version, look at the project's GitHub page. Once checked out, installation is based on setuptools and follows the usual conventions for a Python project:

$ python setup.py install

(Although the develop command may be more useful if you intend to perform any significant modification to the library.) A test suite is provided so that you may verify the code works on your system:

$ python setup.py nosetests

This will also write test-coverage information to the cover/ directory.

There is more documentation available online and you can build your own copy via the Sphinx documentation system:

$ python setup.py build_sphinx

Compiled documentation may be found in build/docs/html/.

Based on the Dual-Tree Complex Wavelet Transform Pack for MATLAB by Nick Kingsbury, Cambridge University. The original README can be found in ORIGINAL_README.txt. This file outlines the conditions of use of the original MATLAB toolbox.

• MATLAB-style functions such as dtwavexfm2 have been moved into a separate dtcwt.compat module.
• Backends moved to dtcwt.numpy and dtcwt.opencl modules.
• Removed dtcwt.base.ReconstructedSignal which was a needless wrapper around NumPy arrays.
• Rename TransformDomainSignal to Pyramid.
• Allow runtime configuration of default backend via dtcwt.push_backend function.
• Verified, thanks to @timseries, the NumPy 3D transform implementation against the MATLAB reference implementation.

• Verified the highpass re-sampling routines in dtcwt.sampling against the existing MATLAB implementation.
• Added experimental image registration routines.
• Re-organised documentation.

• Fixed regression from 0.7 where backend_opencl.dtwavexfm2 would return None, None, None.

• Fix a memory leak in OpenCL implementation where transform results were never de-allocated.