Metadata-Version: 2.1
Name: ruptures
Version: 1.1.3rc1
Summary: Change point detection for signals in Python.
Home-page: https://github.com/deepcharles/ruptures/
Author: Charles Truong, Laurent Oudre, Nicolas Vayatis
Author-email: charles@doffy.net
Maintainer: Charles Truong, Olivier Boulant
License: BSD-2-Clause
Project-URL: Documentation, https://centre-borelli.github.io/ruptures-docs/
Project-URL: Source, https://github.com/deepcharles/ruptures/
Project-URL: Bug Tracker, https://github.com/deepcharles/ruptures/issues/
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        # ruptures
        
        `ruptures` is a Python library for off-line change point detection.
        This package provides methods for the analysis and segmentation of non-stationary signals.  Implemented algorithms include exact and approximate detection for various parametric and non-parametric models.
        `ruptures` focuses on ease of use by providing a well-documented and consistent interface.
        In addition, thanks to its modular structure, different algorithms and models can be connected and extended within this package.
        
        ### How to cite
        If you use `ruptures` in a scientific publication, we would appreciate citations to the following paper:
        - C. Truong, L. Oudre, N. Vayatis. Selective review of offline change point detection methods. _Signal Processing_, 167:107299, 2020. [[journal]](https://doi.org/10.1016/j.sigpro.2019.107299) [[pdf]](http://www.laurentoudre.fr/publis/TOG-SP-19.pdf)
        
        ## Important links
        
        - Documentation: [link](https://centre-borelli.github.io/ruptures-docs/ "Link to documentation").
        - Pypi package index: [link](https://pypi.python.org/pypi/ruptures)
        
        
        # Dependencies and install
        
        Installation instructions can be found [here](https://centre-borelli.github.io/ruptures-docs/install/).
        
        # Basic usage
        
        (Please refer to the [documentation](https://centre-borelli.github.io/ruptures-docs/ "Link to documentation") for more advanced use.)
        
        The following snippet creates a noisy piecewise constant signal, performs a penalized kernel change point detection and displays the results (alternating colors mark true regimes and dashed lines mark estimated change points).
        
        ```python
        import matplotlib.pyplot as plt
        import ruptures as rpt
        
        # generate signal
        n_samples, dim, sigma = 1000, 3, 4
        n_bkps = 4  # number of breakpoints
        signal, bkps = rpt.pw_constant(n_samples, dim, n_bkps, noise_std=sigma)
        
        # detection
        algo = rpt.Pelt(model="rbf").fit(signal)
        result = algo.predict(pen=10)
        
        # display
        rpt.display(signal, bkps, result)
        plt.show()
        ```
        
        ![](./images/example_readme.png)
        
        # Changelog
        
        See the [changelog](https://github.com/deepcharles/ruptures/blob/master/CHANGELOG.md) for a history of notable changes to `ruptures`.
        
        # Thanks to all our contributors
        
        <a href="https://github.com/deepcharles/ruptures/graphs/contributors">
          <img src="https://contributors-img.web.app/image?repo=deepcharles/ruptures" />
        </a>
        
        # License
        
        This project is under BSD license.
        
        ```
        BSD 2-Clause License
        
        Copyright (c) 2017, ENS Paris-Saclay, CNRS
        All rights reserved.
        
        Redistribution and use in source and binary forms, with or without
        modification, are permitted provided that the following conditions are met:
        
        * Redistributions of source code must retain the above copyright notice, this
          list of conditions and the following disclaimer.
        
        * Redistributions in binary form must reproduce the above copyright notice,
          this list of conditions and the following disclaimer in the documentation
          and/or other materials provided with the distribution.
        
        THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
        AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
        IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
        DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
        FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
        DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
        SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
        CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
        OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
        OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
        ```
        
Keywords: change point detection,signal segmentation,computer science,machine learning,kernel methods,time series
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: display
Provides-Extra: test
Provides-Extra: dev
