Changelog for python311-PyWavelets-1.7.0-24.44.x86_64.rpm :

* Sat Aug 31 2024 Dirk Müller - update to 1.7.0:
* Python 3.13 support
* Tue May 07 2024 Dirk Müller - add sle15_python_module_pythons
* Sun May 05 2024 Ben Greiner - 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.
* Sat Sep 17 2022 Arun Persaud - specfile:
* update required numpy version- update to version 1.4.1:
* This patch release updates setup.py to use python_requires>=3.8 and adds 3.11 to the trove classifiers.- changes from version 1.4.0:
* adds wheels for Python 3.11 and drops support for Python 3.7.
* There is one new utility function, pywt.frequency2scale, that can be used to determine CWT scale factors corresponding to a given (normalized) frequency. It is the inverse of the existing pywt.scale2frequency.
* Sat Mar 12 2022 Arun Persaud - update to version 1.3.0:
* This release is functionally the same as 1.2.0, but we have updated the set of binary wheels provided.
* Mon Feb 07 2022 Arun Persaud - specfile:
* update copyright year- update to version 1.2.0:
* New features + There is a new series of multilevel stationary wavelet transforms (mra, mra2 and mran) suited for multiresolution analysis of 1D, 2D or nD signals, respectively. This MRA analysis is also known as the additive wavelet decomposition because the corresponding inverse functions (imra, imra2 or imran) reconstruct the original signal by simple addition of the components. These are a good alternative to the use of the existing SWT functions when it is important to have features aligned across wavelet scales (see the new demo in demo/mra_vs_swt.py). + There is now an n-dimensional implementation available for the wavelet packet transforms (see class WaveletPacketND).
* Backwards incompatible changes + The image returned by pywt.data.camera has been replaced by a similar, CC0-licensed image because the original image was determined to only be licensed for non-commercial use. Any users who still need the prior camera image for non-commercial use can find it many places online by performing a web search for \"cameraman test image\".
* Bugs Fixed + Add input length check in dwt_single for reflect modes. + Demos were updated for compatibility with recent Matplotlib versions. + Removed deprecated import from imp.
* Other changes + PyWavelets has dropped support for Python 3.5 and 3.6 in this release.
* Mon Feb 15 2021 Ben Greiner - Skip python36 build: No python36-numpy anymore in Tumbleweed NumPy 1.20 dropped support for Python 3.6 (NEP 29)
* Thu Jan 30 2020 Todd R - 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 `_.
* 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.
* Tue Jul 23 2019 Todd R - 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.
* Tue Mar 12 2019 Matej Cepl - 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.