{"id":13415328,"url":"https://github.com/deepcharles/ruptures","last_synced_at":"2025-05-14T05:10:41.649Z","repository":{"id":37925544,"uuid":"118264731","full_name":"deepcharles/ruptures","owner":"deepcharles","description":"ruptures: change point detection in Python","archived":false,"fork":false,"pushed_at":"2025-05-06T11:33:44.000Z","size":11484,"stargazers_count":1791,"open_issues_count":30,"forks_count":167,"subscribers_count":30,"default_branch":"master","last_synced_at":"2025-05-06T12:50:25.767Z","etag":null,"topics":["change-point-detection","changepoint","python","science","scientific-computing","signal-processing"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"bsd-2-clause","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/deepcharles.png","metadata":{"files":{"readme":"README.md","changelog":"CHANGELOG.md","contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2018-01-20T17:25:20.000Z","updated_at":"2025-05-06T11:33:48.000Z","dependencies_parsed_at":"2023-11-12T09:05:54.300Z","dependency_job_id":"8f98693b-d027-455d-9a63-e0249580ded4","html_url":"https://github.com/deepcharles/ruptures","commit_stats":{"total_commits":539,"total_committers":27,"mean_commits":"19.962962962962962","dds":0.6196660482374767,"last_synced_commit":"2fba882deaa82b4723117031bd86e59f6a74a0ef"},"previous_names":[],"tags_count":18,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/deepcharles%2Fruptures","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/deepcharles%2Fruptures/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/deepcharles%2Fruptures/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/deepcharles%2Fruptures/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/deepcharles","download_url":"https://codeload.github.com/deepcharles/ruptures/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":254076850,"owners_count":22010611,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["change-point-detection","changepoint","python","science","scientific-computing","signal-processing"],"created_at":"2024-07-30T21:00:47.206Z","updated_at":"2025-05-14T05:10:41.626Z","avatar_url":"https://github.com/deepcharles.png","language":"Python","readme":"# Welcome to ruptures\n\n[![Maintenance](https://img.shields.io/badge/Maintained%3F-yes-green.svg)](https://GitHub.com/deepcharles/ruptures/graphs/commit-activity)\n[![build](https://github.com/deepcharles/ruptures/actions/workflows/run-test.yml/badge.svg)](https://github.com/deepcharles/ruptures/actions/workflows/run-test.yml)\n![python](https://img.shields.io/badge/python-3.8%20|%203.9%20|3.10%20|3.11%20|3.12-blue)\n[![PyPI version](https://badge.fury.io/py/ruptures.svg)](https://badge.fury.io/py/ruptures)\n[![Conda Version](https://img.shields.io/conda/vn/conda-forge/ruptures.svg)](https://anaconda.org/conda-forge/ruptures)\n[![docs](https://github.com/deepcharles/ruptures/actions/workflows/check-docs.yml/badge.svg)](https://github.com/deepcharles/ruptures/actions/workflows/check-docs.yml)\n![PyPI - License](https://img.shields.io/pypi/l/ruptures)\n[![Downloads](https://pepy.tech/badge/ruptures)](https://pepy.tech/project/ruptures)\n\u003ca href=\"https://github.com/psf/black\"\u003e\u003cimg alt=\"Code style: black\" src=\"https://img.shields.io/badge/code%20style-black-000000.svg\"\u003e\u003c/a\u003e\n[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/deepcharles/ruptures/master)\n[![Codecov](https://codecov.io/gh/deepcharles/ruptures/branch/master/graphs/badge.svg)](https://app.codecov.io/gh/deepcharles/ruptures/branch/master)\n\n`ruptures` is a Python library for off-line change point detection.\nThis 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.\n`ruptures` focuses on ease of use by providing a well-documented and consistent interface.\nIn addition, thanks to its modular structure, different algorithms and models can be connected and extended within this package.\n\n**How to cite.** If you use `ruptures` in a scientific publication, we would appreciate citations to the following paper:\n\n- 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)\n\n\n## Latest news\n\n- Welcome to our new PhD student, [Nicolas Cecchi](https://fr.linkedin.com/in/nicolascecchi/fr)! He will integrate new algorithms in \u003cspan style=\"color:blue\"\u003eruptures\u003c/span\u003e and create tutorials and illustrative examples.\n\n- NASA uses \u003cspan style=\"color:blue\"\u003eruptures\u003c/span\u003e! They \u003cins\u003emonitor crops\u003c/ins\u003e in California and Iran. We submitted an article, let's hope for the best!!\n  - Jalilvand, E., Kumar, S.V., Haacker, E., Truong, C., Mahanama, S., 2024, Characterizing spatiotemporal variability in irrigation extent and timing through thermal remote sensing, submitted to Remote Sensing of Environment.\n\n- We have been contacted by CHELSEA FC to monitor players using \u003cspan style=\"color:blue\"\u003eruptures\u003c/span\u003e. Stay tuned...\n\n- We use \u003cspan style=\"color:blue\"\u003eruptures\u003c/span\u003e to understand the \u003cins\u003eregulation of acetylcholine\u003c/ins\u003e, an important neurotransmitter that plays a role in muscle contraction (involved in myasthenia gravis). Check out our work at the [Journal of Physiology](https://doi.org/10.1113/JP287243)\n\n\n- They use \u003cspan style=\"color:blue\"\u003eruptures\u003c/span\u003e to detect changes in \u003cins\u003eclassroom engagement and student participation\u003c/ins\u003e in Japan. [Check out their work](https://doi.org/10.1186/s40561-024-00317-6)\n\n- \u003cspan style=\"color:blue\"\u003eruptures\u003c/span\u003e is part of a larger pipeline to \u003cins\u003eobserve Earth\u003c/ins\u003e, in particular marine biodiversity. The authors use it to find change in the phytoplankton diversity. [Check out their work](https://doi.org/10.1007/s10712-024-09859-3)\n\n\n- We work with Croatian crystallographers (hi [Zoran Štefanić](https://www.irb.hr/eng/About-RBI/People/Zoran-Stefanic)!) to [understand protein motions using angular diagrams](https://pubs.acs.org/doi/10.1021/acs.jcim.4c00650). We will keep you posted for our next joint publications.\n\n- [Charles Truong](https://charles.doffy.net) presented \u003cspan style=\"color:blue\"\u003eruptures\u003c/span\u003e at [PyConDE \u0026 PyData Berlin 2024](https://pretalx.com/pyconde-pydata-2024/speaker/BFRLAK/). [Check out the video.](https://kiwi.cmla.ens-cachan.fr/index.php/s/ss3rZwNSKwGtyQW)\n\n## Basic usage\n\n(Please refer to the [documentation](https://centre-borelli.github.io/ruptures-docs/ \"Link to documentation\") for more advanced use.)\n\nThe 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).\n\n```python\nimport matplotlib.pyplot as plt\nimport ruptures as rpt\n\n# generate signal\nn_samples, dim, sigma = 1000, 3, 4\nn_bkps = 4  # number of breakpoints\nsignal, bkps = rpt.pw_constant(n_samples, dim, n_bkps, noise_std=sigma)\n\n# detection\nalgo = rpt.Pelt(model=\"rbf\").fit(signal)\nresult = algo.predict(pen=10)\n\n# display\nrpt.display(signal, bkps, result)\nplt.show()\n```\n\n![](./images/example_readme.png)\n\n## General information\n\n#### Contact\n\nConcerning this package, its use and bugs, use the [issue page](https://github.com/deepcharles/ruptures/issues) of the [ruptures repository](https://github.com/deepcharles/ruptures). For other inquiries, you can contact me [here](https://charles.doffy.net/contact/).\n\n\n#### Important links\n\n- [Documentation](https://centre-borelli.github.io/ruptures-docs)\n- [Pypi package index](https://pypi.python.org/pypi/ruptures)\n\n#### Dependencies and install\n\nInstallation instructions can be found [here](https://centre-borelli.github.io/ruptures-docs/install/).\n\n#### Changelog\n\nSee the [changelog](https://github.com/deepcharles/ruptures/blob/master/CHANGELOG.md) for a history of notable changes to `ruptures`.\n\n## Thanks to all our contributors\n\n\u003ca href=\"https://github.com/deepcharles/ruptures/graphs/contributors\"\u003e\n  \u003cimg src=\"https://contributors-img.web.app/image?repo=deepcharles/ruptures\" /\u003e\n\u003c/a\u003e\n\n## License\n\nThis project is under BSD license.\n\n```\nBSD 2-Clause License\n\nCopyright (c) 2017-2022, ENS Paris-Saclay, CNRS\nAll rights reserved.\n\nRedistribution and use in source and binary forms, with or without\nmodification, are permitted provided that the following conditions are met:\n\n* Redistributions of source code must retain the above copyright notice, this\n  list of conditions and the following disclaimer.\n\n* Redistributions in binary form must reproduce the above copyright notice,\n  this list of conditions and the following disclaimer in the documentation\n  and/or other materials provided with the distribution.\n\nTHIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\nAND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\nIMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\nDISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE\nFOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\nDAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\nSERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\nCAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\nOR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\nOF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n```\n","funding_links":[],"categories":["异常检测包","Python","Libraries","时间序列","📦 Packages"],"sub_categories":["资源传输下载","Python"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdeepcharles%2Fruptures","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdeepcharles%2Fruptures","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdeepcharles%2Fruptures/lists"}