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Wavelet transforms are time-frequency transforms employing\nwavelets.  They are similar to Fourier transforms, the difference being that\nFourier transforms are localized only in frequency instead of in time and\nfrequency.\n\nThe main features of PyWavelets are:\n\n  * 1D, 2D and nD Forward and Inverse Discrete Wavelet Transform (DWT and IDWT)\n  * 1D, 2D and nD Multilevel DWT and IDWT\n  * 1D and 2D Stationary Wavelet Transform (Undecimated Wavelet Transform)\n  * 1D and 2D Wavelet Packet decomposition and reconstruction\n  * 1D Continuous Wavelet Transform\n  * Computing Approximations of wavelet and scaling functions\n  * Over 100 `built-in wavelet filters`_ and support for custom wavelets\n  * Single and double precision calculations\n  * Real and complex calculations\n  * Results compatible with Matlab Wavelet Toolbox (TM)\n\n\nDocumentation\n-------------\n\nDocumentation with detailed examples and links to more resources is available\nonline at http://pywavelets.readthedocs.org.\n\nFor more usage examples see the `demo`_ directory in the source package.\n\n\nInstallation\n------------\n\nPyWavelets supports `Python`_ \u003e=3.10, and is only dependent on `NumPy`_\n(supported versions are currently ``\u003e= 1.23.0``). To pass all of the tests,\n`Matplotlib`_ is also required.\n\nThere are binary wheels for Intel Linux, Windows and macOS / OSX on PyPi.  If\nyou are on one of these platforms, you should get a binary (precompiled)\ninstallation with::\n\n    pip install PyWavelets\n\nUsers of the Anaconda_ Python distribution may wish to obtain pre-built\nWindows, Intel Linux or macOS / OSX binaries from the conda-forge channel.\nThis can be done via::\n\n    conda install -c conda-forge pywavelets\n\nSeveral Linux distributions have their own packages for PyWavelets, but these\ntend to be moderately out of date.  Query your Linux package manager tool for\n``python-pywavelets``, ``python-wavelets``, ``python-pywt`` or a similar\npackage name.\n\nIf you want or need to install from source, you will need a working C compiler\n(any common one will work) and a recent version of `Cython`_.  Navigate to the\nPyWavelets source code directory (containing ``pyproject.toml``) and type::\n\n    pip install .\n\nThe most recent *development* version can be found on GitHub at\nhttps://github.com/PyWavelets/pywt.\n\nThe latest release, including source and binary packages for Intel Linux,\nmacOS and Windows, is available for download from the `Python Package Index`_.\nYou can find source releases at the `Releases Page`_.\n\nState of development \u0026 Contributing\n-----------------------------------\n\nPyWavelets started in 2006 as an academic project for a master thesis\non `Analysis and Classification of Medical Signals using Wavelet Transforms`\nand was maintained until 2012 by its `original developer`_.  In 2013\nmaintenance was taken over in a `new repo \u003chttps://github.com/PyWavelets/pywt\u003e`_)\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 \u003chttps://github.com/nigma/pywt/issues/13\u003e`_ 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 \u003chttps://tidelift.com/subscription/pkg/pypi-pywavelets?utm_source=pypi-pywavelets\u0026utm_medium=referral\u0026utm_campaign=readme\u003e`_\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. DOIs for past versions can be found by following\nthe link in the badge below to Zenodo:\n\n.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.1407171.svg\n   :target: https://doi.org/10.5281/zenodo.1407171\n\n.. _built-in wavelet filters: http://wavelets.pybytes.com/\n.. _Cython: http://cython.org/\n.. _demo: https://github.com/PyWavelets/pywt/tree/main/demo\n.. _Anaconda: https://www.continuum.io\n.. _GitHub: https://github.com/PyWavelets/pywt\n.. _GitHub Issues: https://github.com/PyWavelets/pywt/issues\n.. _NumPy: https://www.numpy.org\n.. _SciPy: https://www.scipy.org\n.. _original developer: http://en.ig.ma\n.. _Python: http://python.org/\n.. _Python Package Index: http://pypi.python.org/pypi/PyWavelets/\n.. _mailing list: http://groups.google.com/group/pywavelets\n.. _Releases Page: https://github.com/PyWavelets/pywt/releases\n.. _Matplotlib: http://matplotlib.org\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpywavelets%2Fpywt","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpywavelets%2Fpywt","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpywavelets%2Fpywt/lists"}