About Tsdat
Tsdat is an open-source python framework for declaratively creating pipelines to read, standardize, and enhance time series datasets of any dimensionality for use in scalable applications and in building large data repositories.
This repository contains the core tsdat code. We invite you to explore this, especially for those willing to provide feedback or make contributions to the tsdat core (we enthusiastically welcome issues, PRs, discussions & new ideas, etc.).
Most users should start with a template repository to generate boilerplate code and configurations needed to create a tsdat data pipeline. We recommend this template to start with, as it is the most flexible and well-supported template that we offer.
Development Environment
Instructions on setting up your development environment for working on the core tsdat code are included below:
-
Fork this repository to your github account and open it on your desktop in an IDE of your choice.
We recommend using VS Code, as we've included extra settings that make it easy to start developing in a standard environment with no overhead configuration time.
-
Open an appropriate terminal shell from your computer
- If you are on Linux or Mac, just open a regular terminal
- If you are on Windows, start your Anaconda prompt if you installed Anaconda directly to Windows, OR open a WSL terminal if you installed Anaconda via WSL.
-
Run the following commands to create and activate your
condaenvironmentconda env create conda activate tsdat pip install -e ".[dev]"
Community
Tsdat is an open-source repository and we highly-value community contributions and engagement via issues, pull requests, and discussions. Please let us know if you find bugs, want to request new features, or have specific questions about the framework!
Additional resources
- Learn more about
tsdat:- GitHub: https://github.com/tsdat
- Documentation: https://tsdat.readthedocs.io
- Data standards: https://github.com/tsdat/data_standards
- Preferred template: https://github.com/tsdat/pipeline-template
- All templates: https://github.com/tsdat/template-repositories
- Docker Images: https://hub.docker.com/u/tsdat
- Learn more about
xarray:- GitHub: https://github.com/pydata/xarray
- Documentation: https://xarray.pydata.org
- Learn more about
act-atmos:- GitHub: https://github.com/arm-doe/act
- Documentation: https://arm-doe.github.io/ACT/
- Other useful tools:
- VS Code: https://code.visualstudio.com/docs
- Docker: https://docs.docker.com/get-started/
pytest: https://github.com/pytest-dev/pytestblack: https://github.com/psf/blackmatplotlibguide: https://realpython.com/python-matplotlib-guide/
2 maintainers
Metadata
2 maintainers
Release files for tsdat 0.10.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| tsdat-0.10.0.tar.gz | 79.2 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| tsdat-0.10.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 79.4 MB
Release files / tsdat-0.10.0.tar.gz
| Download URL | tsdat-0.10.0.tar.gz |
|---|---|
| Size | 79.2 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
53f8dfed0ae6b203c823d01f5c5ea28fa67a425583547f159e2e7803e5becb47
|
|
BLAKE2b-256 checksum How to use checksums |
b3aced333acb983ca9d6dcb2cbe2047be4f814f479f93a445e7745c0c381f9ab
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Aug 3, 2026.
Transparency logRelease files / tsdat-0.10.0-py3-none-any.whl
| Download URL | tsdat-0.10.0-py3-none-any.whl |
|---|---|
| Size | 201.1 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
915b9d440756692744a3db219110fcfc8049acaad60c2ac16ac547542bb96365
|
|
BLAKE2b-256 checksum How to use checksums |
9d2070ae169335e24144b78ea5021b3b7a4572543d4b6ec39c9f13c02576ba59
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Aug 3, 2026.
Transparency log
