2017-08-24 16:30:13 +00:00
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#
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# spec file for package python-PyWavelets
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#
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Accepting request 773212 from home:TheBlackCat:branches:devel:languages:python:numeric
- 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.
OBS-URL: https://build.opensuse.org/request/show/773212
OBS-URL: https://build.opensuse.org/package/show/devel:languages:python:numeric/python-PyWavelets?expand=0&rev=9
2020-02-10 19:35:59 +00:00
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# Copyright (c) 2020 SUSE LLC
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2017-08-24 16:30:13 +00:00
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#
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# All modifications and additions to the file contributed by third parties
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# remain the property of their copyright owners, unless otherwise agreed
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# upon. The license for this file, and modifications and additions to the
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# file, is the same license as for the pristine package itself (unless the
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# license for the pristine package is not an Open Source License, in which
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# case the license is the MIT License). An "Open Source License" is a
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# license that conforms to the Open Source Definition (Version 1.9)
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# published by the Open Source Initiative.
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2019-01-21 09:46:33 +00:00
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# Please submit bugfixes or comments via https://bugs.opensuse.org/
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2017-08-24 16:30:13 +00:00
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#
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%{?!python_module:%define python_module() python-%{**} python3-%{**}}
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Accepting request 773212 from home:TheBlackCat:branches:devel:languages:python:numeric
- 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.
OBS-URL: https://build.opensuse.org/request/show/773212
OBS-URL: https://build.opensuse.org/package/show/devel:languages:python:numeric/python-PyWavelets?expand=0&rev=9
2020-02-10 19:35:59 +00:00
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%define skip_python2 1
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2017-08-24 16:30:13 +00:00
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Name: python-PyWavelets
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Accepting request 773212 from home:TheBlackCat:branches:devel:languages:python:numeric
- 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.
OBS-URL: https://build.opensuse.org/request/show/773212
OBS-URL: https://build.opensuse.org/package/show/devel:languages:python:numeric/python-PyWavelets?expand=0&rev=9
2020-02-10 19:35:59 +00:00
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Version: 1.1.1
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2017-08-24 16:30:13 +00:00
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Release: 0
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Summary: PyWavelets is a Python wavelet transforms module
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License: MIT
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Group: Development/Libraries/Python
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2019-07-23 02:47:36 +00:00
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URL: https://github.com/PyWavelets/pywt
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2017-08-24 16:30:13 +00:00
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Source0: https://files.pythonhosted.org/packages/source/P/PyWavelets/PyWavelets-%{version}.tar.gz
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BuildRequires: %{python_module Cython}
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BuildRequires: %{python_module devel}
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2018-07-18 20:57:36 +00:00
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BuildRequires: %{python_module numpy-devel >= 1.9.1}
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2017-08-24 16:30:13 +00:00
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BuildRequires: %{python_module setuptools}
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BuildRequires: fdupes
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BuildRequires: python-rpm-macros
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2019-01-21 09:46:33 +00:00
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BuildRequires: unzip
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2019-07-23 02:47:36 +00:00
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# SECTION test requirements
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BuildRequires: %{python_module nose}
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Accepting request 773212 from home:TheBlackCat:branches:devel:languages:python:numeric
- 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.
OBS-URL: https://build.opensuse.org/request/show/773212
OBS-URL: https://build.opensuse.org/package/show/devel:languages:python:numeric/python-PyWavelets?expand=0&rev=9
2020-02-10 19:35:59 +00:00
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BuildRequires: %{python_module pytest}
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2019-07-23 02:47:36 +00:00
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# /SECTION
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2018-07-18 20:57:36 +00:00
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Requires: python-numpy >= 1.9.1
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Accepting request 773212 from home:TheBlackCat:branches:devel:languages:python:numeric
- 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.
OBS-URL: https://build.opensuse.org/request/show/773212
OBS-URL: https://build.opensuse.org/package/show/devel:languages:python:numeric/python-PyWavelets?expand=0&rev=9
2020-02-10 19:35:59 +00:00
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Provides: python-PyWavelets-doc = %{version}
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Obsoletes: python-PyWavelets-doc < %{version}
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2017-08-24 16:30:13 +00:00
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%python_subpackages
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%description
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PyWavelets is a Python wavelet transforms module that can do:
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* 1D and 2D Forward and Inverse Discrete Wavelet Transform (DWT and IDWT)
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* 1D and 2D Stationary Wavelet Transform (Undecimated Wavelet Transform)
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* 1D and 2D Wavelet Packet decomposition and reconstruction
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* Computing Approximations of wavelet and scaling functions
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* Over seventy built-in wavelet filters and support for custom wavelets
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* Single and double precision calculations
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* Results compatibility with Matlab Wavelet Toolbox
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%prep
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%setup -q -n PyWavelets-%{version}
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2019-01-21 09:46:33 +00:00
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sed -i -e '/^#!\//, 1d' pywt/tests/*.py
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2017-10-13 12:14:51 +00:00
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# Fix wrong-script-interpreter
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2018-07-18 20:57:36 +00:00
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find demo -name '*.py' -exec sed -i "s|#!%{_bindir}/env python|#!%__python3|" {} \;
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2017-10-13 12:14:51 +00:00
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2017-08-24 16:30:13 +00:00
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# Remove unneeded shebangs
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2018-07-18 20:57:36 +00:00
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sed -i '1{\@^#!%{_bindir}/env python@d}' pywt/data/create_dat.py
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2017-08-24 16:30:13 +00:00
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# Remove unneeded executable bits
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for lib in test_concurrent test_data test_deprecations test_doc test_matlab_compatibility test_matlab_compatibility_cwt test_thresholding data/generate_matlab_data data/generate_matlab_data_cwt ; do
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chmod a-x pywt/tests/$lib.py
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done
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%build
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export CFLAGS="%{optflags} -fno-strict-aliasing"
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%python_build
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%install
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%python_install
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2017-10-13 12:14:51 +00:00
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%{python_expand pushd %{buildroot}%{$python_sitearch}
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# Fix wrong-script-interpreter
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2018-07-18 20:57:36 +00:00
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sed -i "s|#!%{_bindir}/env python|#!%__$python|" pywt/tests/*.py
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2017-10-13 12:14:51 +00:00
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# Deduplicating files can generate a RPMLINT warning for pyc mtime
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$python -m compileall -d %{$python_sitearch} pywt/tests/
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$python -O -m compileall -d %{$python_sitearch} pywt/tests/
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%fdupes .
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popd
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}
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2017-08-24 16:30:13 +00:00
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%check
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Accepting request 773212 from home:TheBlackCat:branches:devel:languages:python:numeric
- 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.
OBS-URL: https://build.opensuse.org/request/show/773212
OBS-URL: https://build.opensuse.org/package/show/devel:languages:python:numeric/python-PyWavelets?expand=0&rev=9
2020-02-10 19:35:59 +00:00
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|
|
mkdir temp
|
|
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|
|
mv pywt temp/pywt
|
|
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|
export PYTHONDONTWRITEBYTECODE=1
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|
# Accuracy is platform-dependent
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|
%pytest_arch --ignore=temp -k 'not test_accuracy_precomputed_cwt' %{buildroot}%{$python_sitearch}/pywt/
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mv temp/pywt pywt
|
2017-08-24 16:30:13 +00:00
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|
%files %{python_files}
|
2019-01-21 09:46:33 +00:00
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|
|
%doc README.rst
|
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|
%license LICENSE
|
2017-08-24 16:30:13 +00:00
|
|
|
%{python_sitearch}/pywt/
|
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|
%{python_sitearch}/PyWavelets-%{version}-py*.egg-info
|
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|
%changelog
|
