Tsdat Pipeline Template

This repository contains a collection of one or more tsdat pipelines (as found under the pipelines folder). This enables related pipelines to be more easily maintained and run together. New pipelines can be added easily via the cookiecutter template mechanism described below.

Repository Structure

The repository is made up of the following core pieces:

• runner.py: Main entry point for running a pipeline.

• pipelines/*: Collection of custom data pipelines using tsdat.

• pipelines/example_ingest: An out-of-the-box example tsdat pipeline.

• templates/*: Template(s) used to generate new pipelines.

• shared/*: Shared configuration files that may be used across multiple pipelines.

• utils/*: Utility scripts.

Prerequisites

The following are required to develop a tsdat pipeline:

1. A GitHub account. Click here to create an account if you don't have one already

2. An Anaconda environment. We strongly recommend developing in an Anaconda Python environment to ensure that there are no library dependency issues. Click here for more information on installing Anaconda on your computer

   Windows Users - You can install Anaconda directly to your Windows box OR you can run via a linux environment using the Windows Subsystem for Linux (WSL). See this tutorial on WSL for how to set up a WSL environment and attach VS Code to it.

Creating a repository from the pipeline-template

You can create a new repository based upon the tsdat pipeline-template repository in GitHub:

1. Click this 'Use this template' link and follow the steps to copy the template repository into to your account.

   NOTE: If you are looking to get an older version of the template, you will need to select the box next to 'Include all branches' and set the branch your are interested in as your new default branch.

2. On github click the 'Code' button to get a link to your code, then run

   git clone <the link you copied>

   from the terminal on your computer where you would like to work on the code.

Setting up your Anaconda environment

1. 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.

2. Run the following commands to create and activate your conda environment, where $REPOSITORY_ROOT represents the folder where you checked out your pipeline repository:

   conda env create --file=conda-environment.yaml
   conda activate tsdat-pipelines

   If you get the following warning message when running tsdat commands in your shell:

    UserWarning: pyproj unable to set database path.

   Then run the following additional commands to permanently remove this warning message:

   conda remove --force pyproj
   pip install pyproj

Opening your repository in VS Code

1. Open the cloned repository in VS Code. (This repository contains default settings for VS Code that will make it much easier to get started quickly.)

2. Install the recommended extensions (there should be a pop-up in VS Code with recommendations).

3. Tell VS Code to use your new environment:

   • Press F1 to bring up the command pane in VS Code
   • Type Python: Select Interpreter and select it.
   • Select the newly-created ingest conda environment from the drop-down list.

     You may need to refresh the list (cycle icon in the top right) to see it.

   • Bring up the command pane and type Developer: Reload Window to reload VS Code and ensure the settings changes propagate correctly.

Adding a new pipeline

1. Ensure your development environment is set up according to the instructions above

2. Use a cookiecutter template to generate a new pipeline folder. From your top level repository folder, run:

   make cookies

   The make cookies command is a memorable shortcut for cookiecutter templates/ingest -o pipelines

   Cookiecutter will show some text in the prompts, but more information on these prompts can be found in the template README.md

3. Once cookiecutter is done you will see your new pipeline folder appear inside pipelines/. Please see the README.md file inside that folder for more information on how to configure, run, test, and debug your pipeline.

This repository supports adding as many pipelines as you want - just rinse and repeat the steps above.

Additional resources

• Learn more about tsdat:
  • GitHub: https://github.com/tsdat/tsdat
  • Documentation: https://tsdat.readthedocs.io
  • Data standards: https://github.com/tsdat/data_standards
• Learn more about xarray:
  • GitHub: https://github.com/pydata/xarray
  • Documentation: https://xarray.pydata.org
• Learn more about 'pydantic':
  • GitHub: https://github.com/samuelcolvin/pydantic/
  • Documentation: https://pydantic-docs.helpmanual.io
• 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/

References

Implementation of the mbari_wec pipeline was extracted from:

• Sandia National Laboratories. (2021). MBARI WEC 2021 deployment [data set]. Retrieved from https://dx.doi.org/10.15473/1825670.