| Name | PyWavelets JSON |
| Version |
1.6.0
JSON |
| download |
| home_page | https://github.com/PyWavelets/pywt |
| Summary | PyWavelets, wavelet transform module |
| upload_time | 2024-04-01 18:09:21 |
| maintainer | None |
| docs_url | https://pythonhosted.org/PyWavelets/ |
| author | None |
| requires_python | >=3.9 |
| license | Copyright (c) 2006-2012 Filip Wasilewski <http://en.ig.ma/> Copyright (c) 2012-2020 The PyWavelets Developers <https://github.com/PyWavelets/pywt> Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
|
+---------------+-----------------+
| Service | Master branch |
+===============+=================+
| GitHub | |ghactions_ci| |
+---------------+-----------------+
| Appveyor | |appveyor_ci| |
+---------------+-----------------+
| Read the Docs | |read_the_docs| |
+---------------+-----------------+
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PyWavelets
==========
.. contents::
What is PyWavelets
------------------
PyWavelets is a free Open Source library for wavelet transforms in Python.
Wavelets are mathematical basis functions that are localized in both time and
frequency. Wavelet transforms are time-frequency transforms employing
wavelets. They are similar to Fourier transforms, the difference being that
Fourier transforms are localized only in frequency instead of in time and
frequency.
The main 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 Stationary Wavelet Transform (Undecimated Wavelet Transform)
* 1D and 2D Wavelet Packet decomposition and reconstruction
* 1D Continuous Wavelet Transform
* Computing Approximations of wavelet and scaling functions
* Over 100 `built-in wavelet filters`_ and support for custom wavelets
* Single and double precision calculations
* Real and complex calculations
* Results compatible with Matlab Wavelet Toolbox (TM)
Documentation
-------------
Documentation with detailed examples and links to more resources is available
online at http://pywavelets.readthedocs.org.
For more usage examples see the `demo`_ directory in the source package.
Installation
------------
PyWavelets supports `Python`_ >=3.9, and is only dependent on `NumPy`_
(supported versions are currently ``>= 1.14.6``). To pass all of the tests,
`Matplotlib`_ is also required. `SciPy`_ is also an optional dependency. When
present, FFT-based continuous wavelet transforms will use FFTs from SciPy
rather than NumPy.
There are binary wheels for Intel Linux, Windows and macOS / OSX on PyPi. If
you are on one of these platforms, you should get a binary (precompiled)
installation with::
pip install PyWavelets
Users of the Anaconda_ Python distribution may wish to obtain pre-built
Windows, Intel Linux or macOS / OSX binaries from the conda-forge channel.
This can be done via::
conda install -c conda-forge pywavelets
Several Linux distributions have their own packages for PyWavelets, but these
tend to be moderately out of date. Query your Linux package manager tool for
``python-pywavelets``, ``python-wavelets``, ``python-pywt`` or a similar
package name.
If you want or need to install from source, you will need a working C compiler
(any common one will work) and a recent version of `Cython`_. Navigate to the
PyWavelets source code directory (containing ``pyproject.toml``) and type::
pip install .
The most recent *development* version can be found on GitHub at
https://github.com/PyWavelets/pywt.
The latest release, including source and binary packages for Intel Linux,
macOS and Windows, is available for download from the `Python Package Index`_.
You can find source releases at the `Releases Page`_.
State of development & Contributing
-----------------------------------
PyWavelets started in 2006 as an academic project for a master thesis
on `Analysis and Classification of Medical Signals using Wavelet Transforms`
and was maintained until 2012 by its `original developer`_. In 2013
maintenance was taken over in a `new repo <https://github.com/PyWavelets/pywt>`_)
by a larger development team - a move supported by the original developer.
The repo move doesn't mean that this is a fork - the package continues to be
developed under the name "PyWavelets", and released on PyPi and Github (see
`this issue <https://github.com/nigma/pywt/issues/13>`_ for the discussion
where that was decided).
All contributions including bug reports, bug fixes, new feature implementations
and documentation improvements are welcome. Moreover, developers with an
interest in PyWavelets are very welcome to join the development team!
As of 2019, PyWavelets development is supported in part by Tidelift.
`Help support PyWavelets with the Tidelift Subscription <https://tidelift.com/subscription/pkg/pypi-pywavelets?utm_source=pypi-pywavelets&utm_medium=referral&utm_campaign=readme>`_
Contact
-------
Use `GitHub Issues`_ or the `mailing list`_ to post your comments or questions.
**Report a security vulnerability:** https://tidelift.com/security
License
-------
PyWavelets is a free Open Source software released under the MIT license.
If you wish to cite PyWavelets in a publication, please use the following
JOSS publication.
.. image:: http://joss.theoj.org/papers/10.21105/joss.01237/status.svg
:target: https://doi.org/10.21105/joss.01237
Specific releases can also be cited via Zenodo. The DOI below will correspond
to the most recent release. DOIs for past versions can be found by following
the link in the badge below to Zenodo:
.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.1407171.svg
:target: https://doi.org/10.5281/zenodo.1407171
.. _built-in wavelet filters: http://wavelets.pybytes.com/
.. _Cython: http://cython.org/
.. _demo: https://github.com/PyWavelets/pywt/tree/main/demo
.. _Anaconda: https://www.continuum.io
.. _GitHub: https://github.com/PyWavelets/pywt
.. _GitHub Issues: https://github.com/PyWavelets/pywt/issues
.. _NumPy: https://www.numpy.org
.. _SciPy: https://www.scipy.org
.. _original developer: http://en.ig.ma
.. _Python: http://python.org/
.. _Python Package Index: http://pypi.python.org/pypi/PyWavelets/
.. _mailing list: http://groups.google.com/group/pywavelets
.. _Releases Page: https://github.com/PyWavelets/pywt/releases
.. _Matplotlib: http://matplotlib.org
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"description": "+---------------+-----------------+\n| Service | Master branch |\n+===============+=================+\n| GitHub | |ghactions_ci| |\n+---------------+-----------------+\n| Appveyor | |appveyor_ci| |\n+---------------+-----------------+\n| Read the Docs | |read_the_docs| |\n+---------------+-----------------+\n\n\n.. |ghactions_ci| image:: https://github.com/PyWavelets/pywt/actions/workflows/tests.yml/badge.svg?branch=main\n :alt: Build Status\n :target: https://github.com/PyWavelets/pywt/actions/workflows/tests.yml?query=branch%3Amain\n\n.. |appveyor_ci| image:: https://ci.appveyor.com/api/projects/status/github/PyWavelets/pywt\n :align: middle\n :target: https://ci.appveyor.com/project/PyWavelets/pywt\n :alt: Appveyor Status\n\n.. |read_the_docs| image:: https://readthedocs.org/projects/pywavelets/badge/?version=latest\n :align: middle\n :target: https://pywavelets.readthedocs.io/en/latest/?badge=latest\n :alt: Documentation Status\n\n\nPyWavelets\n==========\n\n.. contents::\n\nWhat is PyWavelets\n------------------\n\nPyWavelets is a free Open Source library for wavelet transforms in Python.\nWavelets are mathematical basis functions that are localized in both time and\nfrequency. 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In 2013\nmaintenance was taken over in a `new repo <https://github.com/PyWavelets/pywt>`_)\nby a larger development team - a move supported by the original developer.\nThe repo move doesn't mean that this is a fork - the package continues to be\ndeveloped under the name \"PyWavelets\", and released on PyPi and Github (see\n`this issue <https://github.com/nigma/pywt/issues/13>`_ for the discussion\nwhere that was decided).\n\nAll contributions including bug reports, bug fixes, new feature implementations\nand documentation improvements are welcome. Moreover, developers with an\ninterest in PyWavelets are very welcome to join the development team!\n\nAs of 2019, PyWavelets development is supported in part by Tidelift.\n`Help support PyWavelets with the Tidelift Subscription <https://tidelift.com/subscription/pkg/pypi-pywavelets?utm_source=pypi-pywavelets&utm_medium=referral&utm_campaign=readme>`_\n\n\nContact\n-------\n\nUse `GitHub Issues`_ or the `mailing list`_ to post your comments or questions.\n\n**Report a security vulnerability:** https://tidelift.com/security\n\nLicense\n-------\n\nPyWavelets is a free Open Source software released under the MIT license.\n\nIf you wish to cite PyWavelets in a publication, please use the following\nJOSS publication.\n\n.. image:: http://joss.theoj.org/papers/10.21105/joss.01237/status.svg\n :target: https://doi.org/10.21105/joss.01237\n\nSpecific releases can also be cited via Zenodo. The DOI below will correspond\nto the most recent release. 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