a2edap/tsdat

Time series data utilities for declaratively applying standardization, Q/C, and transformations to datastreams.

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:

1. 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.

2. Open an appropriate terminal shell from your computer

   1. If you are on Linux or Mac, just open a regular terminal
   2. 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.

3. Run the following commands to create and activate your conda environment

   conda 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/ingest-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/pytest
  • black: https://github.com/psf/black
  • matplotlib guide: https://realpython.com/python-matplotlib-guide/