Metadata-Version: 2.4
Name: pyewt
Version: 1.0.0
Summary: Package implementing the Empirical Wavelet Transforms
Author-email: Jerome Gilles <jgilles@sdsu.edu>
License: MIT License
        
        Copyright (c) 2024 Jerome Gilles
        
        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
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        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
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        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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License-File: LICENSE
Keywords: analysis,empirical,harmonic analysis,image processing,mathematics,signal processing,wavelet
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Image Processing
Classifier: Topic :: Scientific/Engineering :: Mathematics
Requires-Python: >=3.8
Requires-Dist: connected-components-3d
Requires-Dist: matplotlib
Requires-Dist: numpy
Requires-Dist: scikit-image
Requires-Dist: scikit-learn
Requires-Dist: scipy
Description-Content-Type: text/markdown

# Empirical Wavelet Transforms Package

This package is the official package that provides the different empirical wavelet transforms published by J.Gilles and his lab.
It does provide the same transforms as the original Matlab toolbox (https://github.com/jegilles/Empirical-Wavelets).

The source code is available at: https://github.com/jegilles/pyewt

The available transforms are:

### 1D transform

- original Littlewood-Paley transform
- transform using different mother wavelets
- tools to extract/plot the time-frequency information

### 2D transform

- tensor approach
- isotropic Littlewood-Paley
- curvelets type I, II, and III
- Voronoi based Littlewood-Paley
- watershed based Littlewood-Paley
- plotting tools for both the filters and the extracted wavelet coefficients

### Partition detection tools

- basic 1D partitioning
- scale-space method in both 1D and 2D
- Voronoi and watershed partitioning

# References

All papers are available in the "Publications" section at: https://jegilles.sdsu.edu/

- J.Gilles, "Empirical Wavelet Transform" in IEEE Trans. Signal Processing, Vol.61, No.16, 3999--4010, August 2013.
- J.Gilles, G.Tran, S.Osher "2D Empirical transforms. Wavelets, Ridgelets and Curvelets Revisited" in SIAM Journal on Imaging Sciences, Vol.7, No.1, 157--186, January 2014.
- J.Gilles, K.Heal, "A parameterless scale-space approach to find meaningful modes in histograms - Application to image and spectrum segmentation". International Journal of Wavelets, Multiresolution and Information Processing, Vol.12, No.6, 1450044-1--1450044-17, December 2014.
- J.Gilles, "Continuous empirical wavelets systems", Advances in Data Science and Adaptive Analysis, Vol. 12, No 03n04, 2050006, 2020.
- B.Hurat, Z.Alvarado, J.Gilles. "The Empirical Watershed Wavelet", Journal of Imaging, Special Issue "2020 Selected Papers from Journal of Imaging Editorial Board Members", Vol.6, No.12, 140, 2020.
- J.Gilles, "Empirical Voronoi wavelets", Constructive Mathematical Analysis, Vol.5, No.4, 183--189, 2022.
