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        "description": "# tsaug\n\n[![Build](https://travis-ci.com/arundo/tsaug.svg?token=jHLpy7XE6ox7WSacEURT&branch=master)](https://travis-ci.com/arundo/tsaug)\n[![Docs](https://readthedocs.com/projects/arundo-tsaug/badge/?version=latest)](https://arundo-tsaug.readthedocs-hosted.com/en/latest/)\n[![PyPI](https://img.shields.io/pypi/v/tsaug)](https://pypi.org/project/tsaug/)\n\n`tsaug` is a Python package for time series augmentation. It offers a set of \naugmentation methods for time series with unified APIs, as well as operators to\nconnect multiple augmentors into a pipeline.\n\nSee https://arundo-tsaug.readthedocs-hosted.com complete documentation.\n\n## Installation\n\nPrerequisites: Python 3.5 or later.\n\nIt is recommended to use **pip** for installation.\n\n```shell\npip install tsaug\n```\n\nAlternatively, you could install from source code:\n\n```shell\ngit clone https://github.com/arundo/tsaug.git\ncd tsaug/\npip install ./\n```\n\n## Examples\nPlease see [Quick Start](https://arundo-tsaug.readthedocs-hosted.com/en/latest/quickstart.html) for some examples.\n\nFor full references of implemented augmentation methods, please refer to [References](https://arundo-tsaug.readthedocs-hosted.com/en/latest/references.html).\n\n## Contributing\n\nPull requests are welcome. For major changes, please open an issue first to\ndiscuss what you would like to change.\n\nPlease make sure to update tests as appropriate.\n\n## License\n\n`tsaug` is licensed under the Apache License 2.0. See the [LICENSE](LICENSE) file for details.\n\n",
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