tsdat 0.2.10

A data processing framework used to convert time series data into standardized format.

Time Series Data Analytical Toolkit (TSDAT)

The Time Series Data Analytical Toolkit (TSDAT) is an open-source python framework for creating pipelines to read, standardize, and enhance time series datasets of any dimensionality for use in scalable applications and data repositories.

Important Links

• Tsdat Documentation: https://tsdat.readthedocs.io
• Template Repositories: https://github.com/tsdat/template-repositories
• Issues: https://github.com/tsdat/tsdat/issues
• Xarray Documentation: https://xarray.pydata.org

Getting Started

We recommend starting by reading the docs to get a high-level overview of tsdat.

After you have a basic understanding of the various tsdat components, we recommend checking out the available templates to see example tsdat use-cases and find a template that best suits your needs. Each template repository will come with its own instructions for getting started.

Contributing

We enthusiastically welcome contributions to any of our repositories.

If you find a bug or want to submit a feature request, please submit an issue. If you are submitting an issue for a bug, please explain the bug and provide a minimal example that reproduces the bug. Feature requests should clearly explain what the new feature is and what the benefit of this feature would be.

If you know how to fix a bug or implement a feature request and would like to contribute code to help resolve an open issue, please submit a pull request. See below for guidelines on how to get started with a pull request:

1. Fork tsdat to <your_username>/tsdat and clone it to your working area.
2. Install development requirements: pip install -r requirements-dev.txt
3. Make your changes and update tests or docs as appropriate.
4. Test your changes by running pytest, black --check ., and flake8
5. Submit a Pull Request and provide a detailed description of your work so we can review your changes as efficiently as possible.