{"author":"Carl H Lubba","author_email":"carl.lubba@gmx.de","bugtrack_url":null,"classifiers":["License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)","Operating System :: OS Independent","Programming Language :: Python :: 3"],"description":"

$\"catch22$

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pycatch22: CAnonical Time-series CHaracteristics in python

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\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 [📗 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- 📗 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","description_content_type":"text/markdown","docs_url":null,"download_url":null,"downloads":{"last_day":-1,"last_month":-1,"last_week":-1},"dynamic":null,"home_page":null,"keywords":null,"license":"GNU General Public License v3 (GPLv3)","license_expression":null,"license_files":null,"maintainer":"Ben D Fulcher","maintainer_email":"ben.fulcher@sydney.edu.au","name":"pycatch22","package_url":"https://pypi.org/project/pycatch22/","platform":null,"project_url":"https://pypi.org/project/pycatch22/","project_urls":{"GitHub Repository (pycatch22)":"https://github.com/DynamicsAndNeuralSystems/pycatch22","Mother Repository (catch22)":"https://github.com/DynamicsAndNeuralSystems/catch22"},"provides_extra":null,"release_url":"https://pypi.org/project/pycatch22/0.4.5/","requires_dist":null,"requires_python":null,"summary":"22 CAnonical Time-series Features","version":"0.4.5","yanked":false,"yanked_reason":null}

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