[SciPy-User] ANN: PyWavelets 1.0.0

classic Classic list List threaded Threaded
1 message Options
Reply | Threaded
Open this post in threaded view

[SciPy-User] ANN: PyWavelets 1.0.0

Gregory Lee
Hello all,

We are pleased to announce the release of PyWavelets 1.0. We view this version number as a milestone in the project's now more than a decade long history.  It reflects that PyWavelets has stabilized over the past few years, and is now a mature package which a lot of other important packages depend on. A listing of those package won't be complete, but some we are aware of are:

- `scikit-image <https://scikit-image.org>`_ - image processing in Python
- `imagehash <https://github.com/JohannesBuchner/imagehash>`_ - perceptual image hashing
- `pyradiomics <https://github.com/Radiomics/pyradiomics>`_ - extraction of Radiomics features from 2D and 3D images and binary masks
- `tomopy <https://github.com/tomopy/tomopy>`_ - Tomographic Reconstruction in Python
- `SpikeSort <https://github.com/btel/SpikeSort>`_ - Spike sorting library implemented in Python/NumPy/PyTables
- `ODL <https://github.com/odlgroup/odl>`_ - operator discretization library

This release requires Python 2.7 or >=3.5 and NumPy 1.9.1 or greater. The 1.0 release will be the last release supporting Python 2.7.  It will be a Long Term Support (LTS) release, meaning that we will backport critical bug fixes to 1.0.x for as long as Python itself does so (i.e. until 1 Jan 2020).

A summary of changes and contributors is given below. A more comprehensive list of specific issues and pull requests closed is available in the release notes at:

New features

New 1D test signals
Many common synthetic 1D test signals have been implemented in the new function ``pywt.data.demo_signals`` to encourage reproducible research. To get a list of the available signals, call ``pywt.data.demo_signals('list')``.
These signals have been validated to match the test signals of the same name from the `Wavelab <https://statweb.stanford.edu/~wavelab>`_ toolbox (with the kind permission of Dr. David Donoho).

C99 complex support
The Cython modules and underlying C library can now be built with C99 complex support when supported by the compiler. Doing so improves performance when running wavelet transforms on complex-valued data. On POSIX systems (Linux, Mac OS X), C99 complex support is enabled by default at build time.

The user can set the environment variable ``USE_C99_COMPLEX`` to 0 or 1 to manually disable or enable C99 support at compile time.

complex-valued CWT
The continuous wavelet transform, ``cwt``, now also accepts complex-valued data.

More flexible specification of some continuous wavelets
The continous wavelets ``"cmor"``, ``"shan"`` and ``"fbsp"`` now let the user specify attributes such as their center frequency and bandwidth that were previously fixed. See more on this in the section on deprecated features.

Fully Separable Discrete Wavelet Transfrom
A new variant of the multilevel n-dimensional DWT has been implemented. It is known as the fully separable wavelet transform (FSWT). The functions ``fswavedecn`` ``fswaverecn`` correspond to the forward and inverse transforms, respectively. This differs from the existing ``wavedecn`` and ``waverecn`` in dimensions >= 2 in that all levels of decomposition are performed along a
single axis prior to moving on to the next.

New thresholding methods
``pywt.threshold`` now supports non-negative Garotte thresholding (``mode='garotte'``).  There is also a new function ``pywt.threshold_firm`` that implements firm (semi-soft) thresholding. Both of the these new thresholding methods are intermediate between soft and hard thresholding.

New anti-symmetric boundary modes
Two new boundary handling modes for the discrete wavelet transforms have been implemented. These correspond to whole-sample and half-sample anti-symmetric boundary conditions (``antisymmetric`` and ``antireflect``). 

New functions to ravel and unravel wavedecn coefficients
The function ``ravel_coeffs`` can be used to ravel all coefficients from ``wavedec``, ``wavedec2`` or ``wavedecn`` into a single 1D array. Unraveling back into a list of individual n-dimensional coefficients can be performed by ``unravel_coeffs``.

New functions to determine multilevel DWT coefficient shapes and sizes
The new function ``wavedecn_size`` outputs the total number of coefficients that will be produced by a ``wavedecn`` decomposition. The function ``wavedecn_shapes`` returns full shape information for all coefficient arrays produced by ``wavedecn``. These functions provide the size/shape information without having to explicitly compute a transform.

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.


* 0-tree +
* Jacopo Antonello +
* Matthew Brett +
* Saket Choudhary +
* Michael V. DePalatis +
* Daniel Goertzen +
* Ralf Gommers
* Mark Harfouche +
* John Kirkham +
* Dawid Laszuk +
* Gregory R. Lee
* Michel Pelletier +
* Balint Reczey +
* SylvainLan +
* Daniele Tricoli
* Kai Wohlfahrt

A total of 16 people contributed to this release.
People with a "+" by their names contributed a patch for the first time.
This list of names is automatically generated, and may not be fully complete.

SciPy-User mailing list
[hidden email]