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- update to 1.7.0:
  * Python 3.13 support

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- add sle15_python_module_pythons
  + Highlights:
    * Added a test suite
- remove -O1 and --skip-build flags from the install command line
- first package

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- Update to 1.6.0
  * This release is a minor update to 1.6.x. It adds support for
    NumPy 2.0, which also keeping compatibility with NumPy
    1.22-1.26. It requires Cython 3.0; support for 0.29.x is
    dropped.
  ## Other noteworthy improvements:
  * Improved documentation for ``pywt.cwt``
  * The documentation was migrated to the PyData Sphinx Theme and
    the content organization improved
  * Releases on PyPI now include wheels for ``musllinux`` (x86-64
    and aarch64)
  * The test suite is now passing and tested in CI with
    Emscripten/Pyodide
  * Nightly builds will be available at
    https://anaconda.org/scientific-python-nightly-wheels/PyWavelets/
    going forward
- Release 1.5.0
  * This release is a minor update to 1.4.x. It adds support for
    Python 3.12 and drops support for Python 3.8. It also adds
    support for Cython 3.0.
  * PyWavelets now uses Meson as its build system, and meson-python
    as the build backend.

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update to latest version

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update to latest version

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update to latest version

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- Skip python36 build: No python36-numpy anymore in Tumbleweed
  NumPy 1.20 dropped support for Python 3.6 (NEP 29)

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- Update to version 1.1.1
  * This release is identical in functionality to 1.1.0.
    It fixes setup.py to prevent pip from trying to install from PyPI for Python < 3.5.
- Update to version 1.1.0
  + 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".
      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..
    * 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.
  + 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.
    * 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.
  + 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>`_.
    * 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.
    * 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.
    * A bug that caused a failure for ``iswtn`` when using user-provided ``axes``
      with non-uniform shape along the transformed axes has been fixed.
  + Other changes
    * The PyWavelet test suite now uses ``pytest`` rather than ``nose``.
    * Cython code has been updated to use ``language_level=3``.
    * PyWavelets has adopted the SciPy Code of Conduct.
- Drop doc subpackage.  readthedocs is changing their url structure
  too quickly to easily keep up with.

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- Update to version 1.0.3
  PyWavelets 1.0.3 is functionally equivalent to the 1.0.2 release.
  It was made to archive the JOSS paper about PyWavelets to the
  1.0.x branch and serve as a reference corresponding to the
  version that was peer reviewed.

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- Update to version 1.0.2:
  PyWavelets 1.0.2 is a bug-fix and maintenance release with no
  new features compared to 1.0.1.
  * Bugs Fixed
    - A bug in iswtn when using some combinations of
      user-specified axes was fixed.
    - A potential error related to coefficient shape mismatch
      during WaveletPacket or WaveletPacket2D reconstruction was
      fixed.
  * Other Changes
    - A deprecated import of Iterable was fixed.
    - The spelling of "Garrote" was fixed in the wavelet
      thresholding documentation. For backwards compatibility
      with 1.0.0, the incorrect ("garotte") spelling is also
      accepted for the mode parameter of pywt.threshold.
    - The spelling of "supported" was fixed in one of the
      ValueError messages that can be returned by pywt.cwt.
    - Cython language compatibility has been pinned to
      language_level = '2'. This is in contrast to the master
      branch which is now using language_level = '3'. To support
      this, the minimum supported Cython version has been raised
      to 0.23.5.

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- Update to version 1.0.1
  * Key-based assignment of coefficients to a ``FswavedecnResult`` object (i.e. via
    its __setitem__ method) has been fixed.
  * The order that the individual subband coefficients were stacked by the
    function ``pywt.ravel_coeffs`` is now guaranteed to be consistent across all
    supported Python versions. Explicit alphabetic ordering of subband coefficient
    names is used for consitent ordering regardless of Python version
- Update to version 1.0.0
  + New features
    * New 1D test signals
    * C99 complex support
    * complex-valued CWT
    * More flexible specification of some continuous wavelets
    * Fully Separable Discrete Wavelet Transfrom
    * New thresholding methods
    * New anti-symmetric boundary modes
    * New functions to ravel and unravel wavedecn coefficients
    * New functions to determine multilevel DWT coefficient shapes and sizes
  + Deprecated features
    * The continous wavelets with names ``"cmor"``, ``"shan"`` and ``"fbsp"``
      should now be modified to include formerly hard-coded attributes such as their
      center frequency and bandwidth. Use of the bare names "cmor". "shan" and
      "fbsp"  is now deprecated. For "cmor" (and "shan"), the form of the wavelet
      name is now "cmorB-C" ("shanB-C") where B and C are floats representing the
      bandwidth frequency and center frequency. For "fbsp" the form should now
      incorporate three floats as in "fbspM-B-C" where M is the spline order and B
      and C are the bandwidth and center frequencies.
  + Backwards incompatible changes
    * Python 2.6, 3.3 and 3.4 are no longer supported.
    * The order of coefficients returned by ``swt2`` and input to ``iswt2`` have been
      reversed so that the decomposition levels are now returned in descending rather
      than ascending order. This makes these 2D stationary wavelet functions
      consistent with all of the other multilevel discrete transforms in PyWavelets.
    * For ``wavedec``, ``wavedec2`` and ``wavedecn``, the ability for the user to
      specify a ``level`` that is greater than the value returned by
      ``dwt_max_level``  has been restored. A ``UserWarning`` is raised instead of a
      ``ValueError`` in this case.
  + Bugs Fixed
    * Assigning new data to the ``Node`` or ``Node2D`` no longer forces a cast to
      ``float64`` when the data is one of the other dtypes supported by the dwt
      (``float32``, ``complex64``, ``complex128``).
    * Calling ``pywt.threshold`` with ``mode='soft'`` now works properly for
      complex-valued inputs.
    * A segfault when running multiple `swt2` or `swtn` transforms concurrently has
      been fixed.
    * Several instances of deprecated numpy multi-indexing that caused warnings in
      numpy >=1.15 have been resolved.
    * The 2d inverse stationary wavelet transform, `iswt2`, now supports non-square
      inputs (an unnecessary check for square inputs was removed).
    * Wavelet packets no longer convert float32 to float64 upon assignment to nodes.
    * Doctests have been updated to also work with NumPy >= 1.14,
    * Indexing conventions have been updated to avoid FutureWarnings in NumPy 1.15.
  + Other changes
    * Python 3.7 is now officially supported.
- Remove upstream-included add_default_to_switch_statement.patch

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