Metadata-Version: 1.1
Name: pycwt
Version: 0.3.0a22
Summary: Continuous wavelet transform module for Python.
Home-page: https://github.com/regeirk/pycwt
Author: Sebastian Krieger, Nabil Freij, Alexey Brazhe, Christopher Torrence, Gilbert P. Compo and contributors
Author-email: sebastian@nublia.com
License: BSD
Description: |ReadTheDocs| |PyPi| |Travis|
        
        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.
        
        
        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.
        
        .. code-block:: sh
        
            $ pip install pycwt
        
        Or, you can download the code and run the below line within the top level
        folder.
        
        .. code-block:: sh
        
            $ python setup.py install
        
        
        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.
        
        
        Authors
        -------
        
        Sebastian Krieger, Nabil Freij, Alexey Brazhe, Christopher Torrence,
        Gilbert P. Compo and contributors.
        
        
        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.
        
        
        .. |ReadTheDocs| image:: https://readthedocs.org/projects/pycwt/badge/?version=latest
           :target: http://pycwt.readthedocs.io/en/latest/?badge=latest
        
        .. |PyPi| image:: https://badge.fury.io/py/pycwt.svg
           :target: https://badge.fury.io/py/pycwt
        
        .. |Travis| image:: https://travis-ci.org/regeirk/pycwt.svg?branch=master
           :target: https://travis-ci.org/regeirk/pycwt
        
Keywords: wavelet,spectral analysis,signal processing,data science,timeseries
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Utilities
Classifier: Intended Audience :: Science/Research
