{"id":13668628,"url":"https://github.com/DynamicsAndNeuralSystems/pycatch22","last_synced_at":"2025-04-27T01:31:30.577Z","repository":{"id":43923733,"uuid":"510580297","full_name":"DynamicsAndNeuralSystems/pycatch22","owner":"DynamicsAndNeuralSystems","description":"python implementation of catch22","archived":false,"fork":false,"pushed_at":"2024-09-04T02:58:51.000Z","size":218,"stargazers_count":81,"open_issues_count":4,"forks_count":16,"subscribers_count":3,"default_branch":"main","last_synced_at":"2025-03-29T16:03:31.242Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"https://time-series-features.gitbook.io/catch22/python","language":"C","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/DynamicsAndNeuralSystems.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"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}},"created_at":"2022-07-05T03:58:34.000Z","updated_at":"2025-03-24T08:08:32.000Z","dependencies_parsed_at":"2024-01-14T16:14:29.214Z","dependency_job_id":"57c46b79-bcd7-4c47-bf1f-c0592fcedaef","html_url":"https://github.com/DynamicsAndNeuralSystems/pycatch22","commit_stats":null,"previous_names":[],"tags_count":5,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DynamicsAndNeuralSystems%2Fpycatch22","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DynamicsAndNeuralSystems%2Fpycatch22/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DynamicsAndNeuralSystems%2Fpycatch22/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DynamicsAndNeuralSystems%2Fpycatch22/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/DynamicsAndNeuralSystems","download_url":"https://codeload.github.com/DynamicsAndNeuralSystems/pycatch22/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":251076943,"owners_count":21532603,"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":[],"created_at":"2024-08-02T08:00:44.090Z","updated_at":"2025-04-27T01:31:25.543Z","avatar_url":"https://github.com/DynamicsAndNeuralSystems.png","language":"C","funding_links":[],"categories":[":open_hands: Contributing"],"sub_categories":[],"readme":"\u003cp align=\"center\"\u003e\u003cimg src=\"img/catch22_logo_square.png\" alt=\"catch22 logo\" height=\"220\"/\u003e\u003c/p\u003e\n\n\u003ch1 align=\"center\"\u003e\u003cem\u003epycatch22\u003c/em\u003e: CAnonical Time-series CHaracteristics in python\u003c/h1\u003e\n\n\u003cp align=\"center\"\u003e\n    \u003ca href=\"https://www.gnu.org/licenses/gpl-3.0\"\u003e\u003cimg src=\"https://img.shields.io/badge/License-GPLv3-blue.svg\" height=\"20\"/\u003e\u003c/a\u003e\n \t  \u003ca href=\"https://twitter.com/compTimeSeries\"\u003e\u003cimg src=\"https://img.shields.io/twitter/url/https/twitter.com/compTimeSeries.svg?style=social\u0026label=Follow%20%40compTimeSeries\" height=\"20\"/\u003e\u003c/a\u003e\n\u003c/p\u003e\n\n\n## About\n\n[_catch22_](https://github.com/DynamicsAndNeuralSystems/catch22) is a collection of 22 time-series features coded in C that can be run from Python, as well as [R](https://github.com/hendersontrent/Rcatch22), [Matlab](https://github.com/DynamicsAndNeuralSystems/catch22), and [Julia](https://github.com/brendanjohnharris/Catch22.jl).\n\nThis package provides a python implementation as the module _pycatch22_, licensed under the [GNU GPL v3 license](http://www.gnu.org/licenses/gpl-3.0.html) (or later).\n\n### What do the features do?\n\nThis [GitBooks website](https://time-series-features.gitbook.io/catch22/feature-descriptions) is dedicated to describing the features.\nFor their implementation in code, see the [main _catch22_ repository](https://github.com/DynamicsAndNeuralSystems/catch22).\nThere is also information in the associated paper [\u0026#x1F4D7; Lubba et al. (2019).](https://doi.org/10.1007/s10618-019-00647-x).\n\n### Acknowledgement :+1:\n\nIf you use this software, please read and cite this open-access article:\n\n- \u0026#x1F4D7; Lubba et al. [_catch22_: CAnonical Time-series CHaracteristics](https://doi.org/10.1007/s10618-019-00647-x), _Data Min Knowl Disc_ __33__, 1821 (2019).\n\n## Installation\n\nUsing `pip` for [`pycatch22`](https://pypi.org/project/pycatch22/):\n\n```\npip install pycatch22\n```\n\nIf this doesn't work, make sure you are using the latest `setuptools`: `pip install setuptools --upgrade`.\n\nIf you come across errors with version resolution, you should try something like: `pip install pycatch22==0.4.2 --use-deprecated=legacy-resolver`.\n\nIt is also a [package on anaconda](https://anaconda.org/conda-forge/pycatch22) thanks to [@rpanai](https://github.com/rpanai), which you can install via `conda`:\n\n```\nconda install -c conda-forge pycatch22\n```\n\nor `mamba`:\n\n```\nmamba install -c conda-forge pycatch22\n```\n\n[A manual install (bottom of this page) is a last resort.]\n\n## Usage\n\nEach feature function can be accessed individually and takes arrays as tuple or lists (not `numpy` arrays).\nFor example, for loaded data `tsData` in Python:\n\n```python3\nimport pycatch22\ntsData = [1,2,4,3] # (or more interesting data!)\npycatch22.CO_f1ecac(tsData)\n```\n\nAll features are bundled in the method `catch22_all`, which also accepts `numpy` arrays and gives back a dictionary containing the entries `catch22_all['names']` for feature names and `catch22_all['values']` for feature outputs.\n\nUsage (computing 22 features: _catch22_):\n\n```python3\npycatch22.catch22_all(tsData)\n```\n\nUsage (computing 24 features: _catch24_ = _catch22_ + mean + standard deviation):\n\n```python3\npycatch22.catch22_all(tsData,catch24=True)\n```\n\nWe also include a 'short name' for each feature for easier reference (as outlined in the GitBook [Feature overview table](https://time-series-features.gitbook.io/catch22/feature-descriptions/feature-overview-table)).\nThese short names can be included in the output from `catch22_all()` by setting `short_names=True` as follows:\n\n```python3\npycatch22.catch22_all(tsData,catch24=True,short_names=True)\n```\n\n### Template analysis script\n\nThanks to [@jmoo2880](https://github.com/jmoo2880) for putting together a [demonstration notebook](https://github.com/jmoo2880/c22-usage-examples/) for using pycatch22 to extract features from a time-series dataset.\n\n### Usage notes\n\n- When presenting results using _catch22_, you must identify the version used to allow clear reproduction of your results. For example, `CO_f1ecac` was altered from an integer-valued output to a linearly interpolated real-valued output from v0.3.\n- __Important Note:__ _catch22_ features only evaluate _dynamical_ properties of time series and do not respond to basic differences in the location (e.g., mean) or spread (e.g., variance).\n  - From _catch22_ v0.3, If the location and spread of the raw time-series distribution may be important for your application, we suggest applying the function argument `catch24 = True` to your call to the _catch22_ function in the language of your choice.\n  This will result in 24 features being calculated: the _catch22_ features in addition to mean and standard deviation.\n\n### Manual install\n\nIf you find issues with the `pip` install, you can also install using `setuptools`:\n\n```\npython3 setup.py build\npython3 setup.py install\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FDynamicsAndNeuralSystems%2Fpycatch22","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FDynamicsAndNeuralSystems%2Fpycatch22","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FDynamicsAndNeuralSystems%2Fpycatch22/lists"}