Bump pywavelets from 0.5.2 to 1.1.1

[dependabot-preview]

Bumps pywavelets from 0.5.2 to 1.1.1.

Release notes

Sourced from pywavelets's releases.

PyWavelets 1.1.1

This release is functionally identical to 1.1.0.

This release modified setup.py to mark the package as Python 3.5+ only so pip will not try to install 1.1.1 on older Python versions. To prevent pip from trying to install 1.1.0 on older Python, the source tarball for 1.1.0 was removed from PyPI.

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

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

... (truncated)

Commits

• 7b2f66b drop Python 3.4 from appveyor.yml, add Python 3.7
• 063a2ae set ISRELEASED to True
• 3fda66f add 1.1.1 release note
• 9cad361 fail immediately for unsupported Python
• 5c96a2f add additional classifiers to setup.py
• 7dec3d7 use python_requires to prevent pip from trying with older python
• c6c1cf1 update setup.py to ISRELEASE=False on v1.1.x branch
• 20f67ab set IS_RELEASED to True
• c5e8201 remove unmerged PR from list [ci skip] (#526)
• a4018d9 Merge pull request #523 from grlee77/rel_notes_1.1.0
• Additional commits viewable in compare view

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