| Name | pyewt JSON |
| Version |
1.0.0
JSON |
| download |
| home_page | None |
| Summary | Package implementing the Empirical Wavelet Transforms |
| upload_time | 2025-08-08 01:37:42 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| 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
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 |
analysis
empirical
harmonic analysis
image processing
mathematics
signal processing
wavelet
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# 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.
Raw data
{
"_id": null,
"home_page": null,
"name": "pyewt",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.8",
"maintainer_email": null,
"keywords": "analysis, empirical, harmonic analysis, image processing, mathematics, signal processing, wavelet",
"author": null,
"author_email": "Jerome Gilles <jgilles@sdsu.edu>",
"download_url": "https://files.pythonhosted.org/packages/90/38/6b5cb602b1cca1787662454d74f63a49279babcb1383c6ee181c8b5c1d0c/pyewt-1.0.0.tar.gz",
"platform": null,
"description": "# Empirical Wavelet Transforms Package\n\nThis package is the official package that provides the different empirical wavelet transforms published by J.Gilles and his lab.\nIt does provide the same transforms as the original Matlab toolbox (https://github.com/jegilles/Empirical-Wavelets).\n\nThe source code is available at: https://github.com/jegilles/pyewt\n\nThe available transforms are:\n\n### 1D transform\n\n- original Littlewood-Paley transform\n- transform using different mother wavelets\n- tools to extract/plot the time-frequency information\n\n### 2D transform\n\n- tensor approach\n- isotropic Littlewood-Paley\n- curvelets type I, II, and III\n- Voronoi based Littlewood-Paley\n- watershed based Littlewood-Paley\n- plotting tools for both the filters and the extracted wavelet coefficients\n\n### Partition detection tools\n\n- basic 1D partitioning\n- scale-space method in both 1D and 2D\n- Voronoi and watershed partitioning\n\n# References\n\nAll papers are available in the \"Publications\" section at: https://jegilles.sdsu.edu/\n\n- J.Gilles, \"Empirical Wavelet Transform\" in IEEE Trans. Signal Processing, Vol.61, No.16, 3999--4010, August 2013.\n- 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.\n- 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.\n- J.Gilles, \"Continuous empirical wavelets systems\", Advances in Data Science and Adaptive Analysis, Vol. 12, No 03n04, 2050006, 2020.\n- 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.\n- J.Gilles, \"Empirical Voronoi wavelets\", Constructive Mathematical Analysis, Vol.5, No.4, 183--189, 2022.\n",
"bugtrack_url": null,
"license": "MIT License\n \n Copyright (c) 2024 Jerome Gilles\n \n Permission is hereby granted, free of charge, to any person obtaining a copy\n of this software and associated documentation files (the \"Software\"), to deal\n in the Software without restriction, including without limitation the rights\n to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n copies of the Software, and to permit persons to whom the Software is\n furnished to do so, subject to the following conditions:\n \n The above copyright notice and this permission notice shall be included in all\n copies or substantial portions of the Software.\n \n THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n SOFTWARE.",
"summary": "Package implementing the Empirical Wavelet Transforms",
"version": "1.0.0",
"project_urls": null,
"split_keywords": [
"analysis",
" empirical",
" harmonic analysis",
" image processing",
" mathematics",
" signal processing",
" wavelet"
],
"urls": [
{
"comment_text": null,
"digests": {
"blake2b_256": "6134df2971f13c843faaa3817ac1739b0122101f7e2d3382ce3c2744ebe97c78",
"md5": "cdb9634fbc4e899d209918bfe001f21f",
"sha256": "1c331df7eb63c27a5a001e1e6ee790021bc71de1f93ad5b96868d52a42bfa5dd"
},
"downloads": -1,
"filename": "pyewt-1.0.0-py3-none-any.whl",
"has_sig": false,
"md5_digest": "cdb9634fbc4e899d209918bfe001f21f",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.8",
"size": 53244,
"upload_time": "2025-08-08T01:37:40",
"upload_time_iso_8601": "2025-08-08T01:37:40.343566Z",
"url": "https://files.pythonhosted.org/packages/61/34/df2971f13c843faaa3817ac1739b0122101f7e2d3382ce3c2744ebe97c78/pyewt-1.0.0-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "90386b5cb602b1cca1787662454d74f63a49279babcb1383c6ee181c8b5c1d0c",
"md5": "57d4ccdc1e2a7c63e6e203c1b49a4f69",
"sha256": "1666b36dbb06c85269a1d26b247283f9b84923f8bea47d3291b2c32beb348053"
},
"downloads": -1,
"filename": "pyewt-1.0.0.tar.gz",
"has_sig": false,
"md5_digest": "57d4ccdc1e2a7c63e6e203c1b49a4f69",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.8",
"size": 8678954,
"upload_time": "2025-08-08T01:37:42",
"upload_time_iso_8601": "2025-08-08T01:37:42.292966Z",
"url": "https://files.pythonhosted.org/packages/90/38/6b5cb602b1cca1787662454d74f63a49279babcb1383c6ee181c8b5c1d0c/pyewt-1.0.0.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2025-08-08 01:37:42",
"github": false,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"lcname": "pyewt"
}
