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
    Meteorology
  • 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)
rmay@ucar.edu

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, shill@atmos.ucla.edu and skclark@princeton.edu

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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What’s Needed for the Future of AOS Python? More Software Carpentry!

By Damien Irving (Postdoctoral Fellow, CSIRO Oceans and Atmosphere)
@DrClimate

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.

Continue reading

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The Future of AOS Python: Preparing AOS Students for Tomorrow’s Computational Challenges

By Daniel Rothenberg (Postdoctoral Associate, Center for Global Change Science, MIT)
@danrothenberg

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.

Continue reading

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SciPy 2017

Scott Collis, on the mailing list, reminds us about SciPy 2017: “If you are on the fence about going.. Get off the fence.. It’s a great conference. Well worthwhile..” Hope folks can make it!

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PyData 2017

PyData 2017 will be held in the Seattle-area on July 5-7, 2017. They’re accepting proposals now (deadline is May 1). See here for information on the conference!

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2017 AMS Python Symposium Screencasts and Downloads

Screencasts/downloads for the AMS Python Symposium talks/posters are
online: https://ams.confex.com/ams/97Annual/webprogram/7PYTHON.html.

Lots of great papers! Enjoy!

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