AMS 2022 Student Conference Python Workshop

Let's talk MetPy!

Here you will find a collection of notebooks we will be demonstrating and working through together for this year's Student Conference Python Workshop. The focus will be on some of Unidata's Python tools, including MetPy and Siphon, and the workshop is primarily designed to introduce beginners to the capability and convenience Python can provide for your work in meteorology.

↪️ Pre-workshop materials

On Sunday, we will be working from workshop.ipynb live and synchronously. Before the workshop, go to the asynchronous pre-workshop from Unidata eLearning. As a minimum requirement, this will make sure your environment is ready for Sunday's activities. We also offer optional additional practice covering the core packages and concepts necessary to complete this workshop. The pre-workshop can help you decide if this practice is necessary for you.

⌨️ Getting set up

For this workshop, we have two separate ways you can participate, work ahead, and follow along. If you've registered to attend, you will be given access to Unidata's Science Gateway to do your work on NSF's Jetstream Cloud. If you prefer or require doing the work on your own computer, you are welcome to do so as well!

☁️ Using Science Gateway

If you've registered for this workshop, you can do all of this work on our very own gateway to the NSF Jetstream Cloud! You should have received instructions from AMS on how to access Science Gateway, including a link to a form. Once you use this form to provide us with your GitHub username (create one here if needed), we will approve your access within 1-2 days or by the workshop. After you are given access, you can sign in to Science Gateway with your provided GitHub username at pyaos-workshop.unidata.ucar.edu.

When you first sign in, it may take a few seconds for your personal workspace to populate and your coding environment to be fully set up. From here you will discover a Jupyter Lab interface pre-populated with these materials and a few tools to enable you to update the materials if needed. Once you are given access, you will be able to download materials and notebooks from your workspace if you'd like, up until a brief time after the end of the workshop.

💻 Using your computer

Note that we at Unidata are not able to plan for any hardware limitations your personal computer might have, and we will not have time during the workshop to diagnose issues on personal computers. Please plan to use Science Gateway if this is a concern for you. We will be using and supporting Conda for installing and managing a Python environment from your computer's command line. Please have this environment prepared ahead of time if you'll be using your own computer.

1. Install Miniconda if you don't already have command-line access to conda.
2. Get a copy of this code! You have a few options from the green button above,
   a. git clone https://github.com/Unidata/pyaos-ams-2022.git from your command line, within some directory on your computer. Install git if necessary. If you're comfortable with git, we recommend this approach as it will let you keep this code regularly up to date.
   b. Open with GitHub Desktop if you have and prefer this graphically-focused software.
   c. Download ZIP if you prefer to get a single snapshot of the code right here and now.
3. Wherever you have this code saved, set up your Python environment with conda env create -f environment.yml from your command line.
4. Give this some time. Once it's done, activate this new environment with conda activate pyaos-ams-2022. Always do this before starting on work for this workshop!
5. Launch Jupyter and get to coding with jupyter lab. Don't forget to activate your environment first!

💬 Acknowledgements

The JupyterHub for this workshop is part of the National Science Foundation (NSF) funded Unidata Science Gateway (doi:10.5065/688s-2w73) (under NSF Award 1901712). We thank Andrea Zonca (San Diego Supercomputing Center), Jeremy Fischer (Indiana University), the NSF funded Jetstream team, and the NSF funded XSEDE Extended Collaborative Support Service (ECSS) program for assistance with this JupyterHub.