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Showing entries tagged [metpy]

Python-Focused Software Training Workshop at Jackson State University

14 August 2018

Jackson State University in Jackson, Mississippi will be hosting a Unidata Regional Software Training Workshop August 30-31, 2018. Unidata software developers will be leading the Python-focused workshop, which will cover the use of the MetPy and Siphon packages in the context of atmospheric science. A basic familiarity with Python is assumed — check out the Unidata Online Python Training for a refresher.

Unidata holds regional workshops in part to facilitate easy access to software training for those who may not be able to travel to training workshops held at the Unidata Program Center in Boulder, Colorado. Attendance is explicitly not limited to Jackson State students and staff; we encourage those within easy travel distance to consider attending.

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MetPy 0.8 Released

18 May 2018

MetPy 0.8.0 has been released. This release has a wide collection of new features as well as minor bug fixes, including several contributions from our community. For full release notes see the GitHub Release Page.

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Posted by Ryan May

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2018 Software Training Workshops Coming in October

15 May 2018

Registration will be opening shortly for Unidata's 2018 Software Training Workshop. The workshop features courses on Unidata's display and analysis packages MetPy, IDV, and AWIPS, as well as courses on data access and management tools including the Local Data Manager (LDM) and the THREDDS Data Server (TDS), and netCDF.

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MetPy 0.7 Released

15 January 2018

MetPy 0.7.0 has been released. This release has a wide collection of new features as well as minor bug fixes, including several contributions from our community. For full release notes see the GitHub Release Page.

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Posted by Ryan May

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Reminder: Register for MetPy Short Course at 2018 AMS Annual Meeting

12 December 2017

Unidata developers Ryan May and John Leeman, together with Kevin Goebbert from Valparaiso University, will be teaching a one-day short course titled “Python for Dynamical Meteorology Using MetPy” at the 2018 AMS Annual Meeting in Austin, Texas. The format of the course is like that of our larger Python workshop, relying on Jupyter notebooks to teach several core concepts. The crux of the course is to access remote data sets and use MetPy to perform analyses relevant to synoptic/dynamic meteorology. The goal is to go beyond the traditional introduction to Python and work on some concrete, meteorology-specific problems. As a result, familiarity with Python, NumPy, and Matplotlib is assumed.

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