PyWavelets 1.1.0

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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)