Researchers in atmosphere and ocean science (AOS) spend an increasing amount of time coding in Python, but most are never taught how to do this efficiently. This virtual workshop will cover a suite of general tools and best practices for writing reliable and maintainable code with less effort, before taking a deep dive on some of the most popular PyAOS libraries.

March 2, 4, 9, and 11 at 2:00 to 6:00 PM Eastern Time (Virtual)

Registration close date: Tuesday, February 23, 2021 at 11:59 PM ET

Participant cap: 45 individuals

REGISTRATION FULL

Registration rates:
$64 for student members
$128 for members
$204 for non-members

Registration policy:

AMS requires a valid payment to be made within 5 days of the start of a course or sooner if registration has reached capacity. You will be contacted by AMS staff if payment is required. Refunds will not be issued to attendees within 7 days of the start of a course.

Course Description:

Python has become the programming language of choice in atmosphere and ocean science (AOS). By consulting online tutorials and help pages, most researchers in this community are able to pick up the basic syntax and programming constructs. This self-taught knowledge is sufficient to get work done, but it often involves spending hours to do things that should take minutes, reinventing a lot of wheels, and a nagging uncertainty at the end of it all regarding the reliability and reproducibility of the results. To help address these issues, this workshop will cover a suite of programming best practices that aren’t so easy to glean from a quick Google search, before taking a deep dive on some of the most popular PyAOS libraries.

Taught over four half days in collaboration with The Carpentries, topics covered will include:

• Software installation with conda
• Project Jupyter
• Data handling with xarray
• Programming best practices including functions, command line programs and defensive programming
• Version control with Git and GitHub
• A taste of the PyAOS ecosystem including MetPy and Py-ART
• Big data with Dask, Binder and Pangeo

The course is aimed at graduate students and other researchers. Participants must already be using Python for their data analysis. They don't need to be highly proficient, but a familiarity with Python syntax and basic constructs such as loops, lists and conditionals (i.e. if statements) is required.

Instructors: