| Name | dtw-python JSON |
| Version |
1.4.2
JSON |
| download |
| home_page | https://DynamicTimeWarping.github.io |
| 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. |
| upload_time | 2024-03-19 07:46:54 |
| maintainer | |
| docs_url | None |
| author | Toni Giorgino |
| requires_python | >=3.6 |
| license | GNU General Public License v3 |
| keywords |
dtw
timeseries
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
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.
.. image:: https://github.com/DynamicTimeWarping/dtw-python/workflows/Build%20and%20upload%20to%20PyPI/badge.svg
:target: https://github.com/DynamicTimeWarping/dtw-python/actions
.. image:: https://badge.fury.io/py/dtw-python.svg
:target: https://badge.fury.io/py/dtw-python
.. image:: https://codecov.io/gh/DynamicTimeWarping/dtw-python/branch/master/graph/badge.svg
:target: https://codecov.io/gh/DynamicTimeWarping/dtw-python
Documentation
~~~~~~~~~~~~~
Please refer to the main `DTW suite 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>`__.
**Note**: **R** is the prime environment for the DTW
suite. 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>`__
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.
Multivariate timeseries can be aligned with arbitrary local distance
definitions, leveraging the [`proxy::dist`](https://www.rdocumentation.org/packages/proxy/versions/0.4-23/topics/dist) (R) or
[`scipy.spatial.distance.cdist`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.cdist.html) (Python) functions.
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>`__
Source code
~~~~~~~~~~~
Releases (stable versions) are available in the `dtw-python project on
PyPi <https://pypi.org/project/dtw-python/>`__. Development
occurs on GitHub at <https://github.com/DynamicTimeWarping/dtw-python>.
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
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"description": "Welcome to the dtw-python package\n=================================\n\nComprehensive implementation of `Dynamic Time Warping algorithms\n<https://dynamictimewarping.github.io>`__.\n\nDTW is a family of algorithms which compute the local stretch or\ncompression to apply to the time axes of two timeseries in order to\noptimally map one (query) onto the other (reference). DTW outputs the\nremaining cumulative distance between the two and, if desired, the\nmapping itself (warping function). DTW is widely used e.g. for\nclassification and clustering tasks in econometrics, chemometrics and\ngeneral timeseries mining.\n\nThis package provides the most complete, freely-available (GPL)\nimplementation of Dynamic Time Warping-type (DTW) algorithms up to\ndate. It is a faithful Python equivalent of `R's DTW package on CRAN\n<https://cran.r-project.org/package=dtw>`__. Supports arbitrary local (e.g.\nsymmetric, asymmetric, slope-limited) and global (windowing)\nconstraints, fast native code, several plot styles, and more.\n\n\n.. image:: https://github.com/DynamicTimeWarping/dtw-python/workflows/Build%20and%20upload%20to%20PyPI/badge.svg\n :target: https://github.com/DynamicTimeWarping/dtw-python/actions\n.. image:: https://badge.fury.io/py/dtw-python.svg\n :target: https://badge.fury.io/py/dtw-python\n.. image:: https://codecov.io/gh/DynamicTimeWarping/dtw-python/branch/master/graph/badge.svg\n :target: https://codecov.io/gh/DynamicTimeWarping/dtw-python\n\n\n\nDocumentation\n~~~~~~~~~~~~~\n\nPlease refer to the main `DTW suite homepage\n<https://dynamictimewarping.github.io>`__ for the full documentation\nand background.\n\nThe best place to learn how to use the package (and a hopefully a\ndecent deal of background on DTW) is the companion paper `Computing\nand Visualizing Dynamic Time Warping Alignments in R: The dtw Package\n<http://www.jstatsoft.org/v31/i07/>`__, which the Journal of\nStatistical Software makes available for free. It includes detailed\ninstructions and extensive background on things like multivariate\nmatching, open-end variants for real-time use, interplay between\nrecursion types and length normalization, history, etc.\n\nTo have a look at how the *dtw* package is used in domains ranging from\nbioinformatics to chemistry to data mining, have a look at the list of\n`citing\npapers <http://scholar.google.it/scholar?oi=bibs&hl=it&cites=5151555337428350289>`__.\n\n**Note**: **R** is the prime environment for the DTW\nsuite. 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This includes\n dozens of well-known types:\n\n - all step patterns classified by\n `Rabiner-Juang <http://www.worldcat.org/oclc/26674087>`__,\n `Sakoe-Chiba <http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1163055>`__,\n and `Rabiner-Myers <http://hdl.handle.net/1721.1/27909>`__;\n - symmetric and asymmetric;\n - Rabiner's smoothed variants;\n - arbitrary, user-defined slope constraints\n\n- partial matches: open-begin, open-end, substring matches\n- proper, pattern-dependent, normalization (exact average distance per\n step)\n- the Minimum Variance Matching (MVM) algorithm `(Latecki et\n al.) <http://dx.doi.org/10.1016/j.patcog.2007.03.004>`__\n\nIn addition to computing alignments, the package provides:\n\n- methods for plotting alignments and warping functions in several\n classic styles (see plot gallery);\n- graphical representation of step patterns;\n- functions for applying a warping function, either direct or inverse;\n- a fast native (C) core.\n\n\nMultivariate timeseries can be aligned with arbitrary local distance\ndefinitions, leveraging the [`proxy::dist`](https://www.rdocumentation.org/packages/proxy/versions/0.4-23/topics/dist) (R) or\n[`scipy.spatial.distance.cdist`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.cdist.html) (Python) functions. \n\n\nCitation\n~~~~~~~~\n\nWhen using in academic works please cite:\n\n* 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>`__.\n\nWhen using partial matching (unconstrained endpoints via the open.begin/open.end options) and/or normalization strategies, please also cite:\n\n* 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>`__\n\n\n\nSource code\n~~~~~~~~~~~\n\nReleases (stable versions) are available in the `dtw-python project on \nPyPi <https://pypi.org/project/dtw-python/>`__. 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