PyWavelets/pywt: PyWavelets 1.1.0

.. contents::

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

• 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)

• 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)

• 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)

• 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)