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-in, standardize, and enhance time series datasets of any dimensionality for use in scalable applications and data repositories.
- Tsdat Documentation: https://tsdat.readthedocs.io
- Xarray Documentation: https://xarray.pydata.org
- Template Repositories: https://github.com/tsdat/template-repositories
- Issues: https://github.com/tsdat/tsdat/issues
tsdat uses xarray in some capacity for nearly
all components of its data pipelines, so we highly recommend checking out their
documentation if you have not used
or if you need a refresher.
We recommend starting by reading the docs to get a
high-level overview of
tsdat and the following components:
Storageclasses for abstracting I/O
IngestPipelinebase classes for creating
QualityHandlerclasses for testing and managing data quality
Storage Configyaml configuration files
After you have a basic understanding of the various tsdat components, we recommend using the local ingest template to create a data ingestion pipeline that runs on your computer. Follow the instructions outlined there to install the dependencies and run the included example.
Alternatively, if you want to start from scratch and use
tsdat without starting
from a template, you can install
pip like so:
pip install tsdat
and then use
tsdat however you like in your project.
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:
<your_username>/tsdatand clone it to your working area.
tsdatfrom source by running
pip install -e .from the cloned repository.
- Verify that you can still reproduce the bug or that the feature has not been implemented yet.
- Make your changes. Be sure to test and to update the
docs/folder if appropriate.
- Ensure your changes on your remote fork of
tsdatand submit a PR.
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