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Getting Started
===============
This library provides support for computing 1D, 2D and 3D dual-tree complex
wavelet transforms and their inverse in Python along with some signal
processing algorithms which make use of the DTCWT.
This section will guide you through using the ``dtcwt`` library. See
:doc:`reference` for full details on the library's API.
Installation
------------
The easiest way to install ``dtcwt`` is via ``easy_install`` or ``pip``:
.. code-block:: console
$ pip install dtcwt
If you want to check out the latest in-development version, look at
`the project's GitHub page <https://github.com/rjw57/dtcwt>`_. Once checked out,
installation is based on setuptools and follows the usual conventions for a
Python project:
.. code-block:: console
$ 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:
.. code-block:: console
$ pip install -r tests/requirements.txt
$ py.test
This will also write test-coverage information to the ``cover/`` directory.
Building the documentation
``````````````````````````
There is `a pre-built <https://dtcwt.readthedocs.org/>`_ version of this
documentation available online and you can build your own copy via the Sphinx
documentation system:
.. code-block:: console
$ python setup.py build_sphinx
Compiled documentation may be found in ``build/docs/html/``.
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