Wanted: Feedback on the new PyAOS website

When Johnny handed over the reigns to the PyAOS website last month, one thing we discussed was the idea of moving over to a site that (a) isn’t tied to any individual’s web hosting and (b) makes it easy for people to make contributions.

In that spirit, I’ve put together a brand new GitHub Pages site: https://pyaos.github.io/

In making the move, I’ve updated much of the content on the site and I’ve also attempted to define the scope of what is and isn’t included in the listings of PyAOS packages and training resources.

Before making the official switch over to the GitHub Pages site (which will involve shutting down this WordPress site), I’d be grateful if people could check out the new site and provide any feedback by creating a new issue at the associated GitHub repository.

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Passing the Baton

Greetings from Boston! I’m attending this year’s Python Symposium at AMS and it’s been great! Amy McGovern gave a stellar core science keynote today on “How Python Can Help Us to Create the Physical Data Scientists of the Future”! Make sure you check out her talk when AMS makes the screencasts of this year’s talks available in a few months! (Though, unfortunately, her live drone demo won’t appear on the screencast.)

I’m writing to let y’all know that I’m stepping down as administrator of the PyAOS blog and mailing list. Damien Irving, whom you may know through his Dr. Climate blog (https://drclimate.wordpress.com/) and yeoman’s work with Data Carpentry, has generously agreed to become the administrator for PyAOS. He has great ideas for the future of the blog and mailing list and I’m looking forward to seeing where he takes it! He wanted me to let y’all know that he’s very interested in hearing your thoughts and ideas; feel free to email him at [email protected]

I wanted to thank everyone who has helped with the blog and mailing list over these 9ish years! It has been a real privilege to collaborate with so many people on this project and to see the growth of the Python community during this time. Thank you for being part of the atmospheric and oceanic sciences Python community!

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Daniel Rothenberg’s Talk on apply_ufunc, Etc.

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.

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2019 AMS Python Symposium Screencasts

The 2019 AMS Python Symposium screencasts are up!



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2018 AMS Python Symposium Screencasts

Here they are!: https://ams.confex.com/ams/98Annual/webprogram/8PYTHON.html.

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2018 AMS Python-Related Short Courses

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.

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Call for Papers for the 2018 AMS Python Symposium

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!

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AGU Session “New Approaches to Analyze Big Geoscientific Datasets”

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.”

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What’s needed for the Future of AOS Python? Get Involved!

By Ryan May (Unidata)
[email protected]

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

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What’s needed for the Future of AOS Python? Tools for Automating AOS Data Analysis and Management

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

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