tsdat 0.3.2

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

About tsdat

tsdat is an open-source python framework for declaratively defining 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 if you are willing to provide feedback or even help contribute to the tsdat core (we enthusiastically welcome all contributions), however most users should start with a template repository to generate nearly all of the code and configurations you need to create a declarative data pipeline with tsdat. We recommend this template to start with, as it is the most flexible and well-supported template that we offer.

Development setup

We recommend that developers using tsdat or developers working on tsdat set up their environment according to the following guidelines:

1. Download and install VS Code. Make sure to add code to your path if prompted.

   We chose VS Code because of its clean user interface, quick startup time, extremely powerful capabilities out-of-box, and its rich library of open source extensions.

2. Clone your fork of this repository to your laptop and open it up in VS Code

3. The first time you open this project in VS Code you will be prompted to install the recommended extensions. Please do so now.

4. Windows users: We recommend using Docker to manage dependencies for this project. If you choose to use Docker follow the steps below:

   • Press F1 (or ctrl-shift-p) to bring up the command pane in VS Code
   • In the command pane, type: Remote-Containers: Open Folder in Container... and hit return
   • You will be prompted to specify which folder should be opened. Select the folder containing this README file
   • Several dialog boxes may appear while the VS Code window is refreshing. Please install the recommended extensions via the dialog box. An additional dialog box should appear asking you to reload the window so Pylance can take effect. Please do this as well.
   • After the window refreshes your development environment will be set up correctly. You may skip steps 5. and 6.

   You can find more information about VS Code and docker containers here.

5. We highly recommend using conda to manage dependencies in your development environment. Please install this using the link above if you haven't already done so. Then run the following commands to create your environment:

   $ conda create --name tsdat python=3.8
   $ conda activate tsdat
   (tsdat) $ pip install -r requirements-dev.txt
   

6. Tell VS Code to use your new conda environment:

   • Press F1 (or ctrl-shift-p) to bring up the command pane in VS Code
   • In the command pane, type: Python: Select Interpreter and hit return
   • Select the newly-created tsdat conda environment from the list. Note that you may need to refresh the list (cycle icon in the top right) for it to show up.
   • Reload the VS Code window to ensure that this setting propagates correctly. This is probably not needed, but doesn't hurt. To do this, press F1 to open the control pane again and type Developer: Reload Window.

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 the 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.

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/