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We are very pleased to announce the release of PyWavelets 1.1.
This release includes enhanced functionality for both the stationary wavelet
transforms (``swt``, ``swt2``, ``swtn``) as well as the continuous wavelet
transform (``cwt``). In addition, there are a handful of bug fixes as
described in more detail below.
This release has dropped Python 2.7 support and now requires Python >= 3.5.
In addition to these changes to the software itself, a paper describing
PyWavelets was recently published in The Journal of Open Source Software:
https://joss.theoj.org/papers/10.21105/joss.01237
New features
============
- All ``swt`` functions now have a new ``trim_approx`` option that can be used
to exclude the approximation coefficients from all but the final level of
decomposition. This mode makes the output of these functions consistent with
the format of the output from the corresponding ``wavedec`` functions.
- All ``swt`` functions also now have a new ``norm`` option that, when set to
``True`` and used in combination with ``trim_approx=True``, gives a partition
of variance across the transform coefficients. In other words, the sum of
the variances of all coefficients is equal to the variance of the original
data. This partitioning of variance makes the ``swt`` transform more similar
to the multiple-overlap DWT (MODWT) described in Percival and Walden's book,
"Wavelet Methods for Time Series Analysis". (476)
A demo of this new ``swt`` functionality is available at
https://github.com/PyWavelets/pywt/blob/master/demo/swt_variance.py
- The continuous wavelet transform (``cwt``) now offers an FFT-based
implementation in addition to the previous convolution based one. The new
``method`` argument can be set to either ``'conv'`` or ``'fft'`` to select
between these two implementations. (490).
- The ``cwt`` now also has ``axis`` support so that CWTs can be applied in
batch along any axis of an n-dimensional array. This enables faster batch
transformation of signals. (509)
Backwards incompatible changes
==============================
- When the input to ``cwt`` is single precision, the computations are now
performed in single precision. This was done both for efficiency and to make
``cwt`` handle dtypes consistently with the discrete transforms in
PyWavelets. This is a change from the prior behaviour of always performing
the ``cwt`` in double precision. (507)
- When using complex-valued wavelets with the ``cwt``, the output will now be
the complex conjugate of the result that was produced by PyWavelets 1.0.x.
This was done to account for a bug described below. The magnitude of the
``cwt`` coefficients will still match those from previous releases. (439)
Bugs Fixed
==========
- For a ``cwt`` with complex wavelets, the results in PyWavelets 1.0.x releases
matched the output of Matlab R2012a's ``cwt``. Howveer, older Matlab releases
like R2012a had a phase that was of opposite sign to that given in textbook
definitions of the CWT (Eq. 2 of Torrence and Compo's review article, "A
Practical Guide to Wavelet Analysis"). Consequently, the wavelet coefficients
were the complex conjugates of the expected result. This was validated by
comparing the results of a transform using ``cmor1.0-1.0`` as compared to the
``cwt`` implementation available in Matlab R2017b as well as the function
``wt.m`` from the Lancaster University Physics department's
`MODA toolbox <https://github.com/luphysics/MODA>`_. (#439)
- For some boundary modes and data sizes, round-trip ``dwt``/``idwt`` can
result in an output that has one additional coefficient. Prior to this
relese, this could cause a failure during ``WaveletPacket`` or
``WaveletPacket2D`` reconstruction. These wavelet packet transforms have now
been fixed and round-trip wavelet packet transforms always preserve the
original data shape. (448)
- All inverse transforms now handle mixed precision coefficients consistently.
Prior to this release some inverse transform raised an error upon
encountering mixed precision dtypes in the wavelet subbands. In release 1.1,
when the user-provided coefficients are a mixture of single and double
precision, all coefficients will be promoted to double precision. (450)
- A bug that caused a failure for ``iswtn`` when using user-provided ``axes``
with non-uniform shape along the transformed axes has been fixed. (462)
Other changes
=============
- The PyWavelet test suite now uses ``pytest`` rather than ``nose``. (477)
- Cython code has been updated to use ``language_level=3``. (435)
- PyWavelets has adopted the SciPy Code of Conduct. (521)