As I mentioned last week, the 2019 AMS Python Symposium screencasts are up. I really appreciated all the talks in the symposium, but I wanted to mention one talk in particular for folks to check out: Daniel Rothenberg’s “Rapidly Prototyping High-Performance Meteorological Data Systems Using Xarray and Numba” gave really practical advice on how (relatively) unknown tools included in xarray, etc., like apply_ufunc, can enable scientists to write performant code. Here’s the URL: https://ams.confex.com/ams/2019Annual/meetingapp.cgi/Paper/348989.
There are three short courses being offered at the 2018 AMS meeting in
Austin that involve Python:
- AMS Short Course: A Beginner’s Course to Using Python in Climate and
- AMS Short Course: Reproducible Atmospheric Science Workflows Using Open
Source Tools: An Introduction to the Popper Experimentation Protocol
- AMS Short Course: Python for Dynamical Meteorology Using MetPy
See the short courses page for more information.
The Call for Papers for the 2018 AMS Eighth Symposium on Advances in Modeling and Analysis Using Python is out!: https://annual.ametsoc.org/2018/index.cfm/programs/conferences-and-symposia/eighth-symposium-on-advances-in-modeling-and-analysis-using-python/. Look forward to seeing folks in Austin!
An announcement from Joe Hamman to the PyAOS mailing list about IN45: “New Approaches to Analyze Big Geoscientific Datasets” at AGU 2017 in New Orleans; here’s the abstract:
“Rapid analysis and interpretation of large model and measurement datasets is increasingly undertaken as a sequence of institution-supported pre-processing and user-devised post processing (e.g., scripting of specialized statistics and visualization). By providing a pre agreed format for data and metadata, the first stage ensures dataset utility and interoperability. In the second stage the user community employs diverse software practices and specialized toolkits to pursue their data analysis. Users now routinely attempt to ingest entire satellite records or MIP archives to complete their analysis. This often requires interactive and batch workflows to scale from the desktop to distributed HPC systems. Such workflows must adjust to available memory constraints, provide access to CPU and cluster-level parallelism, while remaining flexible and easy to customize. How ought researchers utilize the unprecedented volume of data with metadata-aware analysis tools to answer tomorrow’s data-intensive questions? This session will demonstrate state of-the-art approaches to gigabyte- through petabyte-scale geoscientific data analysis.”
By Ryan May (Unidata)
The Python programming language is a tool near and dear to the hearts of the regular readers of this blog. What truly separates Python from other open-source languages is something distinctly non-technical: the community. This is a frequently heard theme, best expressed in Brett Cannon’s opening remarks at PyCon 2014: “I came for the language, but I stay for the community.” For over ten years I’ve been fortunate to be a part of this welcoming, helpful, and friendly group. For the future of AOS Python, we need to continue to grow and expand our own community; this happens by increased participation and contribution, whether that be from code, documentation, reporting bugs, or even asking and answering questions. Continue reading
By Spencer Hill (Postdoc, UCLA AOS & Caltech GPS) and Spencer Clark (PhD student, Princeton AOS)
@spencerahill, [email protected] and [email protected]
Preface: the future looks good
Python’s standing in the AOS community has never been stronger: its user base is passionate and growing, and AOS-relevant packages and functionality continue to proliferate. These trends seems poised to continue, with (among other things) the emergence of the xarray package for labeled N-dimensional arrays and the dask package for out-of-core computation.
In this post, we discuss one outstanding community need and our recent work in Python on a solution. Meeting it would further accelerate Python’s already impressive momentum in the AOS community. Continue reading
By Damien Irving (Postdoctoral Fellow, CSIRO Oceans and Atmosphere)
When thinking about education and training in scientific computing, you’d be hard pressed to find a bigger success story than Software Carpentry. Over the past five years or so, this volunteer organisation has not only provided training for thousands of researchers around the globe, it has also revolutionised the way we produce training materials. Rather than have individual experts produce stand-alone, static textbooks that are almost immediately outdated, the global community of volunteer Software Carpentry instructors – who all undergo a short training course in educational psychology and instructional design – is collaboratively (via GitHub) and continuously updating and improving its lesson materials.
By Daniel Rothenberg (Postdoctoral Associate, Center for Global Change Science, MIT)
Over the last six years, I served the American Meteorological Society as a member and co-chair of its Student Conference Planning Committee. Each year, just a few weeks after the Annual Meeting, we’d start the long and difficult process of crafting a valuable Conference experience for both new and veteran participants alike. But despite our attendees’ diverse interests, some topics always attracted a broad swath of interest. Chief among those was the application of modern computing tools, techniques, and technologies to today’s (and tomorrow’s) tough problems.