Skip to main content

A port of the Dual-Tree Complex Wavelet Transform MATLAB toolbox.

Project description

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. Coverage License Latest Version Downloads DOI: 10.5281/zenodo.9862 Documentation Status


Ubuntu 15.10 (wily) and later

Installation can be perfomed via apt-get:

$ sudo apt-get install python-dtcwt python-dtcwt-doc

The package is also currently in Debian sid (unstable).

Other operating systems

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 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:

$ pip install -r tests/requirements.txt
$ py.test

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

Further documentation

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

$ python 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.




  • Use fixed random number generator seed when generating documentation.

  • Replace use of Lena image with mandrill.

  • Refactor test suite to use tox + py.test.

  • Documentation formatting fixes.

  • Fix unsafe use of inplace casting (3D transform).

  • Use explicit integer division to close #123.


  • Fix regression in dtcwt-based image registration.

  • Allow levels used for dtcwt-based image registration to be customised.


  • Add queue parameter to low-level OpenCL colifilt and coldfilt functions.

  • Significantly increase speed of dtcwt.registration.estimatereg function.

  • Fix bug whereby dtcwt.backend_name was not restored when using preserve_backend_stack.


  • The OpenCL 2D transform was not always using the correct queue when one was passed explicitly.


  • 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.

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

dtcwt-0.12.0.tar.gz (70.8 kB view hashes)

Uploaded source

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page