We are very pleased to announce the release of PyWavelets 1.3. This release is functionally the same as 1.2.0, but we have updated the set of binary wheels provided. Specifically we have added aarch64 wheels for Python 3.7 that were missing in 1.2.0 and have updated the versions of manylinux used for the linux wheels in a manner consistent with NumPy and SciPy. We have also dropped musllinux wheels which were present for some architectures in 1.2.0. We may add them again in the future if they are adopted by NumPy and there is a demand for this architecture.

full release notes

We are very pleased to announce the release of PyWavelets 1.2.

This release has new discrete wavelet transforms features incleading a series of multiresolution analysis functions (details below).

PyWavelets has dropped support for Python 3.5 and 3.6 and now supports Python 3.7-3.10.

We also now provide aarch64 linux wheels as well as universal2 and arm64 wheels that are compatible with Apple's M1 processors.

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• The image returned by pywt.data.camera has been replaced by a similar, CC0-licensed image because the original image was determined to only be licensed for non-commercial use.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.

Authors

• ElConno +
• Ralf Gommers
• Gregory R. Lee
• Jakub Mandula +
• nperraud +
• ossdev07 +

A total of 6 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.

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
  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)

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.

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.

PyWavelets 1.0.1 is a bug-fix release with no new features compared to 1.0.0.

Bugs Fixed

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.

PyWavelets 1.0.0 Release Notes

We are very 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 - image processing in Python
• imagehash - perceptual image hashing
• pyradiomics - extraction of Radiomics features from 2D and 3D images and binary masks
• tomopy - Tomographic Reconstruction in Python
• SpikeSort - Spike sorting library implemented in Python/NumPy/PyTables
• 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).

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 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.

Authors

• 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.

PyWavelets 0.5.2 Release Notes

PyWavelets 0.5.2 is a bug-fix release with no new features compared to 0.5.1.

Bugs Fixed

The pywt.data.nino data reader is now compatible with numpy 1.12. (#273)

The wp_scalogram.py demo is now compatibile with matplotlib 2.0. (#276)

Fixed a sporadic segmentation fault affecting stationary wavelet transforms of
multi-dimensional data. (#289)

idwtn now treats coefficients set to None to be treated as zeros (#291).
This makes the behavior consistent with its docstring as well as idwt2.
Previously this raised an error.

The tests are now included when installing from wheels or when running
python setup.py install. (#292)

A bug leading to a potential RuntimeError was fixed in waverec.
This bug only affected transforms where the data was >1D and the transformed
axis was not the first axis of the array. (#294).

Authors

• Ralf Gommers
• Gregory R. Lee

PyWavelets 0.5.1 Release Notes

PyWavelets 0.5.1 is a bug-fix release with no new features compared to 0.5.0

Bugs Fixed

In release 0.5.0 the wrong edge mode was used for the following three
deprecated modes: ppd, sp1, and per. All deprecated edge mode
names are now correctly converted to the corresponding new names.

One-dimensional discrete wavelet transforms did not properly respect the
axis argument for complex-valued data. Prior to this release, the last
axis was always transformed for arrays with complex dtype. This fix affects
dwt, idwt, wavedec, waverec.

PyWavelets 0.5.0 Release Notes

PyWavelets is a Python toolbox implementing both discrete and continuous
wavelet transforms (mathematical time-frequency transforms) with a wide range
of built-in wavelets. C/Cython are used for the low-level routines, enabling
high performance. Key Features of PyWavelets are:

• 1D, 2D and nD Forward and Inverse Discrete Wavelet Transform (DWT and IDWT)
• 1D, 2D and nD Multilevel DWT and IDWT
• 1D and 2D Forward and Inverse Stationary Wavelet Transform
• 1D and 2D Wavelet Packet decomposition and reconstruction
• 1D Continuous Wavelet Transform
• When multiple valid implementations are available, we have chosen to maintain consistency with |MATLAB|'s Wavelet Toolbox.

PyWavelets 0.5.0 is the culmination of 1 year of work. In addition to
several new features, substantial refactoring of the underlying C and Cython
code have been made.

This release requires Python 2.6, 2.7 or 3.3-3.5 and NumPy 1.9.1 or greater.
This will be the final release supporting Python 2.6 and 3.3.

Highlights of this release include:

• 1D continuous wavelet transforms
• new discrete wavelets added (additional Debauchies and Coiflet wavelets)
• new 'reflect' extension mode for discrete wavelet transforms
• faster performance for multilevel forward stationary wavelet transforms (SWT)
• n-dimensional support added to forward SWT
• routines to convert multilevel DWT coefficients to and from a single array
• axis support for multilevel DWT
• substantial refactoring/reorganization of the underlying C and Cython code

New features

1D Continous Wavelet Transforms

A wide range of continous wavelets are now available. These include the
following:

• Gaussian wavelets (gaus1...gaus8)
• Mexican hat wavelet (mexh)
• Morlet wavelet (morl)
• Complex Gaussian wavelets (cgau1...cgau8)
• Shannon wavelet (shan)
• Frequency B-Spline wavelet (fbsp)
• Complex Morlet wavelet (cmor)

Also, see the new CWT-related demo: demo/cwt_analysis.py

New discrete wavelets

Additional Debauchies wavelets (db20...db38) and Coiflets
(coif6...coif17) have been added.

New extension mode: reflect

Discrete wavelet transforms support a new extension mode, reflect. This
mode pads an array symmetrically, but without repeating the edge value. As an
example::

 pad      array      pad
4 3 2 | 1 2 3 4 5 | 4 3 2

This differs from symmetric, which repeats the values at the boundaries::

 pad      array      pad
3 2 1 | 1 2 3 4 5 | 5 4 3

Multilevel DWT Coefficient Handling

New routines to convert the coefficients returned by multilevel DWT routines
to and from a single n-dimensional array have been added.
pywt.coeffs_to_array concatenates the output of wavedec, wavedec2
or wavedecn into a single numpy array. pywt.array_to_coeffs can be
used to transform back from a single coefficient array to a format appropriate
for waverec, waverec2 or waverecn.

More C function calls release the GIL

Cython code calling the wavelet filtering routines (DWT and SWT) now releases
the global interpreter lock (GIL) where possible. A potential use case is in
speeding up the batch computation of several large DWTs using multi-threading
(e.g. via concurrent.futures).

Multilevel wavelet transforms along specific axes

The axis specific transform support introduced in the prior release was
extended to the multilevel DWT transforms. All wavedec* and waverec*
routines have a new axis (1D) or axes (2D, nD) keyword argument. If
unspecified the default behaviour is to transform all axes of the input.

Faster multilevel stationary wavelet transforms

Stationary wavelet transforms are now faster when the number of levels is
greater than one. The improvement can be very large (multiple orders of
magnitude) for transforms with a large number of levels.

Deprecated features

Backwards incompatible changes

A FutureWarning was added to swt2 and iswt2 to warn about a pending
backwards incompatible change to the order of the coefficients in the list
returned by these routines. The actual change will not occur until the next
release. Transform coefficients will be returned in descending rather than
ascending order. This change is being made for consistency with all other
existing multi-level transforms in PyWavelets.

Bugs Fixed

demo/image_blender.py was updated to support the new api of Pillow 3.x

A bug related to size of assumed size_t on some platforms/compilers
(e.g. Windows with mingw64) was fixed.

Fix to memory leak in (i)dwt_axis

Fix to a performance regression in idwt and iswt that was introduced
in v0.4.0.

Fixed a bug in dwtn and idwtn for data with complex dtype when
axes != None.

Other changes

The minimum supported numpy version has been increased to 1.9.1.

Test coverage (including for the Cython and C code) via
Codecov <https://codecov.io/>_ was added and the overall test coverage has
been improved.

A substantial overhaul of the C extension code has been performed. Custom
templating is no longer used. The intention is to make this code easier to
maintain and expand in the future.

The Cython code has been split out into a multiple files to hopefully make
relevant portions of the wrappers easier to find for future developers.

setup.py now relies on setuptools in all cases (rather than distutils).

Authors

• Jonathan Dan +
• Ralf Gommers
• David Menéndez Hurtado
• Gregory R. Lee
• Holger Nahrstaedt +
• Daniel M. Pelt +
• Alexandre Saint +
• Scott Sievert +
• Kai Wohlfahrt
• Frank Yu +

A total of 10 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.

Issues closed in this release: see here

Pull requests merged in this release: see here

PyWavelets 0.4.0 Release Notes

PyWavelets 0.4.0 is the culmination of 6 months of work. In addition to
several new features, some changes and deprecations have been made to streamline
the API.

This release requires Python 2.6, 2.7 or 3.3-3.5 and NumPy 1.6.2 or greater.

Highlights of this release include:

• 1D and 2D inverse stationary wavelet transforms
• Substantially faster 2D and nD discrete wavelet transforms
• Complex number support
• nD versions of the multilevel DWT and IDWT

New features

1D and 2D inverse stationary wavelet transforms

1D (iswt) and 2D (iswt2) inverse stationary wavelet transforms were
added. These currently only support even length inputs.

Faster 2D and nD wavelet transforms

The multidimensional DWT and IDWT code was refactored and is now an order of
magnitude faster than in previous releases. The following functions benefit:
dwt2, idwt2, dwtn, idwtn.

Complex floating point support

64 and 128-bit complex data types are now supported by all wavelet transforms.

nD implementation of the multilevel DWT and IDWT

The existing 1D and 2D multilevel transforms were supplemented with an nD
implementation.

Wavelet transforms can be applied along a specific axis/axes

All wavelet transform functions now support explicit specification of the axis
or axes upon which to perform the transform.

Example Datasets

Two additional 2D grayscale images were added (camera, ascent). The
previously existing 1D ECG data (ecg) and the 2D aerial image (aero)
used in the demos can also now be imported via functions defined in
pywt.data (e.g. camera = pywt.data.camera())

Deprecated features

A number of functions have been renamed, the old names are deprecated and will
be removed in a future release:

• intwave, renamed to integrate_wavelet
• centrfrq, renamed to central_frequency
• scal2frq, renamed to scale2frequency
• orthfilt, renamed to orthogonal_filter_bank

Integration of general signals (i.e. not wavelets) with integrate_wavelet
is deprecated.

The MODES object and its attributes are deprecated. The new name is
Modes, and the attribute names are expanded:

• zpd, renamed to zero
• cpd, renamed to constant
• sp1, renamed to smooth
• sym, renamed to symmetric
• ppd, renamed to periodic
• per, renamed to periodization

Backwards incompatible changes

idwt no longer takes a correct_size parameter. As a consequence,
idwt2 inputs must match exactly in length. For multilevel transforms, where
arrays differing in size by one element may be produced, use the waverec
functions from the multilevel module instead.

Bugs Fixed

float32 inputs were not always respected. All transforms now return float32
outputs when called using float32 inputs.

Incorrect detail coefficients were returned by downcoef when level > 1.

Other changes

Much of the API documentation is now autogenerated from the corresponding
function docstrings. The numpydoc sphinx extension is now needed to build the
documentation.

Authors

• Thomas Arildsen +
• François Boulogne
• Ralf Gommers
• Gregory R. Lee
• Michael Marino +
• Aaron O'Leary +
• Daniele Tricoli +
• Kai Wohlfahrt

A total of 8 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.

Issues closed in this release: see here

Pull requests merged in this release: see here

PyWavelets 0.3.0 Release Notes

PyWavelets 0.3.0 is the first release of the package in 3 years. It is the
result of a significant effort of a growing development team to modernize the
package, to provide Python 3.x support and to make a start with providing new
features as well as improved performance. A 0.4.0 release will follow
shortly, and will contain more significant new features as well as
changes/deprecations to streamline the API.

This release requires Python 2.6, 2.7 or 3.3-3.5 and NumPy 1.6.2 or greater.

Highlights of this release include:

• Support for Python 3.x (>=3.3)
• Added a test suite (based on nose, coverage up to 61% so far)
• Maintenance work: C style complying to the Numpy style guide, improved
  templating system, more complete docstrings, pep8/pyflakes compliance, and
  more.

New features

Test suite

The test suite can be run with nosetests pywt or with::

>>> import pywt
>>> pywt.test()

n-D Inverse Discrete Wavelet Transform

The function pywt.idwtn, which provides n-dimensional inverse DWT, has been
added. It complements idwt, idwt2 and dwtn.

Thresholding

The function pywt.threshold has been added. It unifies the four thresholding
functions that are still provided in the pywt.thresholding namespace.

Backwards incompatible changes

None in this release.

Other changes

Development has moved to a new repo <https://github.com/PyWavelets/pywt>_.
Everyone with an interest in wavelets is welcome to contribute!

Building wheels, building with python setup.py develop and many other
standard ways to build and install PyWavelets are supported now.

Authors

• Ankit Agrawal +
• François Boulogne +
• Ralf Gommers +
• David Menéndez Hurtado +
• Gregory R. Lee +
• David McInnis +
• Helder Oliveira +
• Filip Wasilewski
• Kai Wohlfahrt +

A total of 9 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.

Issues closed in this release: see here

Pull requests merged in this release: see here