PyCWT
=====
[](http://pycwt.readthedocs.io/en/latest/?badge=latest)
[](https://badge.fury.io/py/pycwt)
A Python module for continuous wavelet spectral analysis. It includes a
collection of routines for wavelet transform and statistical analysis via FFT
algorithm. In addition, the module also includes cross-wavelet transforms,
wavelet coherence tests and sample scripts.
Please read the documentation [here](http://pycwt.readthedocs.io/en/latest/).
This module requires ``NumPy``, ``SciPy``, ``tqdm``. In addition, you will
also need ``matplotlib`` to run the examples.
The sample scripts (`sample.py`, `sample_xwt.py`) illustrate the use of
the wavelet and inverse wavelet transforms, cross-wavelet transform and
wavelet transform coherence. Results are plotted in figures similar to the
sample images.
### How to cite
Sebastian Krieger and Nabil Freij. _PyCWT: wavelet spectral analysis in
Python_. V. 0.4.0-beta. Python. 2023. <https://github.com/regeirk/pycwt>.
Disclaimer
----------
This module is based on routines provided by C. Torrence and G. P. Compo
available at http://paos.colorado.edu/research/wavelets/, on routines
provided by A. Grinsted, J. Moore and S. Jevrejeva available at
http://noc.ac.uk/using-science/crosswavelet-wavelet-coherence, and
on routines provided by A. Brazhe available at
http://cell.biophys.msu.ru/static/swan/.
This software is released under a BSD-style open source license. Please read
the license file for further information. This routine is provided as is
without any express or implied warranties whatsoever.
Installation
------------
We recommend using PyPI to install this package.
```commandline
$ pip install pycwt
```
However, if you want to install directly from GitHub, use:
```commandline
$ pip install git+https://github.com/regeirk/pycwt
```
Acknowledgements
----------------
We would like to thank Christopher Torrence, Gilbert P. Compo, Aslak Grinsted,
John Moore, Svetlana Jevrejevaand and Alexey Brazhe for their code and also
Jack Ireland and Renaud Dussurget for their attentive eyes, feedback and
debugging.
Contributors
------------
- Sebastian Krieger
- Nabil Freij
- Ken Mankoff
- Aaron Nielsen
- Rodrigo Nemmen
- Ondrej Grover
- Joscelin Rocha Hidalgo
- Stuart Mumford
- ymarcon1
- Tariq Hassan
References
----------
1. Torrence, C. and Compo, G. P.. A Practical Guide to Wavelet
Analysis. Bulletin of the American Meteorological Society, *American
Meteorological Society*, **1998**, 79, 61-78.
2. Torrence, C. and Webster, P. J.. Interdecadal changes in the
ENSO-Monsoon system, *Journal of Climate*, **1999**, 12(8),
2679-2690.
3. Grinsted, A.; Moore, J. C. & Jevrejeva, S. Application of the cross
wavelet transform and wavelet coherence to geophysical time series.
*Nonlinear Processes in Geophysics*, **2004**, 11, 561-566.
4. Mallat, S.. A wavelet tour of signal processing: The sparse way.
*Academic Press*, **2008**, 805.
5. Addison, P. S. The illustrated wavelet transform handbook:
introductory theory and applications in science, engineering,
medicine and finance. *IOP Publishing*, **2002**.
6. Liu, Y., Liang, X. S. and Weisberg, R. H. Rectification of the bias
in the wavelet power spectrum. *Journal of Atmospheric and Oceanic
Technology*, **2007**, 24, 2093-2102.
Raw data
{
"_id": null,
"home_page": null,
"name": "pycwt",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.10",
"maintainer_email": "Sebastian Krieger <sebastian@nublia.com>",
"keywords": "data science, signal processing, spectral analysis, time series, timeseries, wavelet",
"author": "Nabil Freij, and contributors",
"author_email": "Sebastian Krieger <sebastian@nublia.com>",
"download_url": "https://files.pythonhosted.org/packages/4a/d2/3e326d8e714b21ea0fd53e8ed0af88bc3fd76744208962edf74353a0117c/pycwt-0.5.0b0.tar.gz",
"platform": null,
"description": "PyCWT\n=====\n\n[](http://pycwt.readthedocs.io/en/latest/?badge=latest)\n[](https://badge.fury.io/py/pycwt)\n\nA Python module for continuous wavelet spectral analysis. It includes a\ncollection of routines for wavelet transform and statistical analysis via FFT\nalgorithm. In addition, the module also includes cross-wavelet transforms,\nwavelet coherence tests and sample scripts.\n\nPlease read the documentation [here](http://pycwt.readthedocs.io/en/latest/).\n\nThis module requires ``NumPy``, ``SciPy``, ``tqdm``. In addition, you will \nalso need ``matplotlib`` to run the examples.\n\nThe sample scripts (`sample.py`, `sample_xwt.py`) illustrate the use of\nthe wavelet and inverse wavelet transforms, cross-wavelet transform and\nwavelet transform coherence. Results are plotted in figures similar to the\nsample images.\n\n\n### How to cite\n\nSebastian Krieger and Nabil Freij. _PyCWT: wavelet spectral analysis in \nPython_. V. 0.4.0-beta. Python. 2023. <https://github.com/regeirk/pycwt>.\n\n\nDisclaimer\n----------\n\nThis module is based on routines provided by C. Torrence and G. P. Compo\navailable at http://paos.colorado.edu/research/wavelets/, on routines\nprovided by A. Grinsted, J. Moore and S. Jevrejeva available at\nhttp://noc.ac.uk/using-science/crosswavelet-wavelet-coherence, and\non routines provided by A. Brazhe available at\nhttp://cell.biophys.msu.ru/static/swan/.\n\nThis software is released under a BSD-style open source license. Please read\nthe license file for further information. This routine is provided as is\nwithout any express or implied warranties whatsoever.\n\n\nInstallation\n------------\n\nWe recommend using PyPI to install this package.\n\n```commandline\n$ pip install pycwt\n```\n\nHowever, if you want to install directly from GitHub, use:\n\n```commandline\n$ pip install git+https://github.com/regeirk/pycwt\n```\n\n\nAcknowledgements\n----------------\n\nWe would like to thank Christopher Torrence, Gilbert P. Compo, Aslak Grinsted,\nJohn Moore, Svetlana Jevrejevaand and Alexey Brazhe for their code and also\nJack Ireland and Renaud Dussurget for their attentive eyes, feedback and\ndebugging.\n\n\nContributors\n------------\n\n- Sebastian Krieger\n- Nabil Freij\n- Ken Mankoff\n- Aaron Nielsen\n- Rodrigo Nemmen\n- Ondrej Grover\n- Joscelin Rocha Hidalgo\n- Stuart Mumford\n- ymarcon1\n- Tariq Hassan\n\n\nReferences\n----------\n\n1. Torrence, C. and Compo, G. P.. A Practical Guide to Wavelet\n Analysis. Bulletin of the American Meteorological Society, *American\n Meteorological Society*, **1998**, 79, 61-78.\n2. Torrence, C. and Webster, P. J.. Interdecadal changes in the\n ENSO-Monsoon system, *Journal of Climate*, **1999**, 12(8),\n 2679-2690.\n3. Grinsted, A.; Moore, J. C. & Jevrejeva, S. Application of the cross\n wavelet transform and wavelet coherence to geophysical time series.\n *Nonlinear Processes in Geophysics*, **2004**, 11, 561-566.\n4. Mallat, S.. A wavelet tour of signal processing: The sparse way.\n *Academic Press*, **2008**, 805.\n5. Addison, P. S. The illustrated wavelet transform handbook:\n introductory theory and applications in science, engineering,\n medicine and finance. *IOP Publishing*, **2002**.\n6. Liu, Y., Liang, X. S. and Weisberg, R. H. Rectification of the bias\n in the wavelet power spectrum. *Journal of Atmospheric and Oceanic\n Technology*, **2007**, 24, 2093-2102.\n",
"bugtrack_url": null,
"license": null,
"summary": "Continuous wavelet transform module for Python.",
"version": "0.5.0b0",
"project_urls": {
"documentation": "https://pycwt.readthedocs.io/",
"issues": "https://github.com/regeirk/pycwt/issues",
"repository": "https://github.com/regeirk/pycwt"
},
"split_keywords": [
"data science",
" signal processing",
" spectral analysis",
" time series",
" timeseries",
" wavelet"
],
"urls": [
{
"comment_text": null,
"digests": {
"blake2b_256": "c7330013e55dd51ee0ad012ddd331a7b1852d5f4dd32043a68a0813ad762f2e3",
"md5": "a4d85a1d7290c0ca20378cff8dc8174b",
"sha256": "33ae02dd89f31d472b27d3b2471532bd1383bd71dbf4374356fdb9a1608410a8"
},
"downloads": -1,
"filename": "pycwt-0.5.0b0-py3-none-any.whl",
"has_sig": false,
"md5_digest": "a4d85a1d7290c0ca20378cff8dc8174b",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.10",
"size": 755043,
"upload_time": "2025-10-28T01:13:39",
"upload_time_iso_8601": "2025-10-28T01:13:39.755087Z",
"url": "https://files.pythonhosted.org/packages/c7/33/0013e55dd51ee0ad012ddd331a7b1852d5f4dd32043a68a0813ad762f2e3/pycwt-0.5.0b0-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "4ad23e326d8e714b21ea0fd53e8ed0af88bc3fd76744208962edf74353a0117c",
"md5": "331d3e7eadee747ac97f12a315c14549",
"sha256": "ba66417d934877beb6d3a6f2f6bad6465b6f008753caceaf60a29ea8d7f098c4"
},
"downloads": -1,
"filename": "pycwt-0.5.0b0.tar.gz",
"has_sig": false,
"md5_digest": "331d3e7eadee747ac97f12a315c14549",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.10",
"size": 1183358,
"upload_time": "2025-10-28T01:13:44",
"upload_time_iso_8601": "2025-10-28T01:13:44.459518Z",
"url": "https://files.pythonhosted.org/packages/4a/d2/3e326d8e714b21ea0fd53e8ed0af88bc3fd76744208962edf74353a0117c/pycwt-0.5.0b0.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2025-10-28 01:13:44",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "regeirk",
"github_project": "pycwt",
"travis_ci": true,
"coveralls": false,
"github_actions": false,
"requirements": [
{
"name": "numpy",
"specs": []
},
{
"name": "scipy",
"specs": []
},
{
"name": "matplotlib",
"specs": []
},
{
"name": "tqdm",
"specs": []
}
],
"lcname": "pycwt"
}
JSON
