Metadata-Version: 1.1
Name: dtw-python
Version: 0.5.0
Summary: A comprehensive implementation of dynamic time warping (DTW) algorithms in R. DTW computes the optimal (least cumulative distance) alignment between points of two time series. Common DTW variants covered include local (slope) and global (window) constraints, subsequence matches, arbitrary distance definitions, normalizations, minimum variance matching, and so on. Provides cumulative distances, alignments, specialized plot styles, etc.
Home-page: https://DynamicTimeWarping.github.io
Author: Toni Giorgino
Author-email: toni.giorgino@gmail.com
License: GNU General Public License v3
Description: ====================================================================
        The Comprehensive Dynamic Time Warp package (Python bindings)
        ====================================================================
        
        
        .. image:: https://img.shields.io/pypi/v/dtw.svg
                :target: https://pypi.python.org/pypi/dtw
        
        .. image:: https://img.shields.io/travis/tonigi/dtw.svg
                :target: https://travis-ci.org/tonigi/dtw
        
        .. image:: https://readthedocs.org/projects/dtw/badge/?version=latest
                :target: https://dtw.readthedocs.io/en/latest/?badge=latest
                :alt: Documentation Status
        
        
        Comprehensive implementation of Dynamic Time Warping algorithms.
        Supports arbitrary local (eg symmetric, asymmetric, slope-limited) and
        global (windowing) constraints, fast native code, several plot styles,
        and more.
        
        
        Welcome
        ~~~~~~~
        
        This package provides
        the most complete, freely-available (GPL) implementation of Dynamic Time
        Warping-type (DTW) algorithms up to date. It is a faithful Python equivalent
        of `R's DTW package <http://dtw.r-forge.r-project.org/>`__.
        
        
        The package is described in a `companion
        paper <http://www.jstatsoft.org/v31/i07/>`__, including detailed
        instructions and extensive background on things like multivariate
        matching, open-end variants for real-time use, interplay between
        recursion types and length normalization, history, etc.
        
        Description
        ~~~~~~~~~~~
        
        DTW is a family of algorithms which compute the local stretch or
        compression to apply to the time axes of two timeseries in order to
        optimally map one (query) onto the other (reference). DTW outputs the
        remaining cumulative distance between the two and, if desired, the
        mapping itself (warping function). DTW is widely used e.g. for
        classification and clustering tasks in econometrics, chemometrics and
        general timeseries mining.
        
        The R implementation in `dtw <http://www.jstatsoft.org/v31/i07/>`__
        provides:
        
        -  arbitrary windowing functions (global constraints), eg. the
           `Sakoe-Chiba
           band <http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=01163055>`__
           and the `Itakura
           parallelogram <http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1162641>`__;
        -  arbitrary transition types (also known as step patterns, slope
           constraints, local constraints, or DP-recursion rules). This includes
           dozens of well-known types:
        
           -  all step patterns classified by
              `Rabiner-Juang <http://www.worldcat.org/oclc/26674087>`__,
              `Sakoe-Chiba <http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1163055>`__,
              and `Rabiner-Myers <http://hdl.handle.net/1721.1/27909>`__;
           -  symmetric and asymmetric;
           -  Rabiner's smoothed variants;
           -  arbitrary, user-defined slope constraints
        
        -  partial matches: open-begin, open-end, substring matches
        -  proper, pattern-dependent, normalization (exact average distance per
           step)
        -  the Minimum Variance Matching (MVM) algorithm `(Latecki et
           al.) <http://dx.doi.org/10.1016/j.patcog.2007.03.004>`__
        
        Multivariate timeseries can be aligned with arbitrary local distance
        definitions, leveraging the *{proxy}dist* function. DTW itself becomes a
        distance function with the *dist* semantics.
        
        In addition to computing alignments, the package provides:
        
        -  methods for plotting alignments and warping functions in several
           classic styles (see plot gallery);
        -  graphical representation of step patterns;
        -  functions for applying a warping function, either direct or inverse;
        -  both fast native (C) and interpreted (R) cores.
        
        Documentation
        ~~~~~~~~~~~~~
        
        The best place to learn how to use the package (and a hopefully a decent
        deal of background on DTW) is the companion paper `Computing and
        Visualizing Dynamic Time Warping Alignments in R: The dtw
        Package <http://www.jstatsoft.org/v31/i07/>`__, which the Journal of
        Statistical Software makes available for free.
        
        To have a look at how the *dtw* package is used in domains ranging from
        bioinformatics to chemistry to data mining, have a look at the list of
        `citing
        papers <http://scholar.google.it/scholar?oi=bibs&hl=it&cites=5151555337428350289>`__.
        
        A link to prebuilt documentation is
        `here <http://www.rdocumentation.org/packages/dtw>`__.
        
        Citation
        ~~~~~~~~
        
        If you use *dtw*, do cite it in any publication reporting results
        obtained with this software. Please follow the directions given in
        ``citation("dtw")``, i.e. cite:
        
           Toni Giorgino (2009). *Computing and Visualizing Dynamic Time Warping
           Alignments in R: The dtw Package.* Journal of Statistical Software,
           31(7), 1-24,
           `doi:10.18637/jss.v031.i07 <http://dx.doi.org/10.18637/jss.v031.i07>`__.
        
        When using partial matching (unconstrained endpoints via the
        ``open.begin``/``open.end`` options) and/or normalization strategies,
        please also cite:
        
           Paolo Tormene, Toni Giorgino, Silvana Quaglini, Mario Stefanelli
           (2008). Matching Incomplete Time Series with Dynamic Time Warping: An
           Algorithm and an Application to Post-Stroke Rehabilitation.
           Artificial Intelligence in Medicine, 45(1), 11-34.
           `doi:10.1016/j.artmed.2008.11.007 <http://dx.doi.org/10.1016/j.artmed.2008.11.007>`__
        
        
        Credits
        -------
        
        This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.
        
        .. _Cookiecutter: https://github.com/audreyr/cookiecutter
        .. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage
        
        
        
        History
        =======
        
        0.1.0 (2019-08-22)
        ------------------
        
        * First release on PyPI.
        
Keywords: dtw
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: License :: OSI Approved :: GNU Lesser General Public License v2 or later (LGPLv2+)
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
