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:
(Please refer to the 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).
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()
Concerning this package, its use and bugs, use the issue page of the ruptures repository. For other inquiries, you can contact me here.
Installation instructions can be found here.
See the changelog for a history of notable changes to ruptures.
This project is under BSD license.
BSD 2-Clause License
Copyright (c) 2017-2022, 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.
FAQs
Change point detection for signals in Python.
We found that ruptures demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 2 open source maintainers collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Research
Security News
A look at the top trends in how threat actors are weaponizing open source packages to deliver malware and persist across the software supply chain.
Security News
ESLint now supports HTML linting with 48 new rules, expanding its language plugin system to cover more of the modern web development stack.
Security News
CISA is discontinuing official RSS support for KEV and cybersecurity alerts, shifting updates to email and social media, disrupting automation workflows.