Metadata-Version: 1.1
Name: dtw-python
Version: 1.0.0
Summary: A comprehensive implementation of dynamic time warping (DTW) algorithms. 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: Welcome to the dtw-python package
        =================================
        
        Comprehensive implementation of `Dynamic Time Warping algorithms
        <https://dynamictimewarping.github.io>`__.
        
        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.
        
        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 on CRAN
        <https://cran.r-project.org/package=dtw>`__.  Supports arbitrary local (e.g.
        symmetric, asymmetric, slope-limited) and global (windowing)
        constraints, fast native code, several plot styles, and more.
        
        
        
        Documentation
        ~~~~~~~~~~~~~
        
        Please refer to the main `DTW project homepage
        <https://dynamictimewarping.github.io>`__ for the full documentation
        and background.
        
        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.  It includes 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.
        
        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>`__.
        
        Links to prebuilt documentation are available
        `for R <http://www.rdocumentation.org/packages/dtw>`__
        and
        `Python <https://dynamictimewarping.github.io/py-api/html/>`__.
        
        **Note**: **R** is the preferred environment for the DTW
        project. Python's docstrings and the API below are generated
        automatically for the sake of consistency and maintainability, and may
        not be as pretty.
        
        
        Features
        ~~~~~~~~
        
        The implementation 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;
        -  a fast native (C) core.
        
        
           
        Citation
        ~~~~~~~~
        
        When using in academic works please cite:
        
        * T. Giorgino. Computing and Visualizing Dynamic Time Warping Alignments in R: The dtw Package. J. Stat. Soft., 31 (2009) `doi:10.18637/jss.v031.i07 <https://www.jstatsoft.org/article/view/v031i07>`__.
        
        When using partial matching (unconstrained endpoints via the open.begin/open.end options) and/or normalization strategies, please also cite:
        
        * P. Tormene, T. Giorgino, S. Quaglini, M. 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>`__
        
          
        License
        ~~~~~~~
        
        This program is free software: you can redistribute it and/or modify
        it under the terms of the GNU General Public License as published by
        the Free Software Foundation, either version 3 of the License, or
        (at your option) any later version.
        
        This program is distributed in the hope that it will be useful,
        but WITHOUT ANY WARRANTY; without even the implied warranty of
        MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
        GNU General Public License for more details.
        
        You should have received a copy of the GNU General Public License
        along with this program.  If not, see <http://www.gnu.org/licenses/>.
        
        
        
        
        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
        
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
