This web page is the course web page for the AMS Short Course on Using Python in Climate and Meteorology (January 22–23, 2011), in Seattle, WA. Schedules, handouts, Powerpoint slides, and links to exercises are all found here.
- Things Students Should Do Before the Course
- Schedule for the Course
- Detailed Description of the Course
The course coordinator is Charles Doutriaux, Program for Climate Model Diagnosis and Intercomparison (PCMDI), Lawrence Livermore National Laboratory, (email: [email protected]). However, for questions during January 2011, please contact Johnny Lin (email: [email protected]). Teachers for the course include:
Acknowledgment: Thanks to Cara Campbell at AMS for all her help in making this course possible!
Resources from the Course
This includes lectures, exercises, modules, and links to modules, packages, and datasets used in the short course. The resources are arranged by sections of the short course, given in the order of the Schedule:
|Introduction to Python, Part 1: Language Basics||Slides & Exercises|
|Introduction to Python, Part 2: Arrays and I/O||Slides & Exercises|
|CDAT, Part 1: Concepts, Masked Variables and I/O||Slides & Exercises|
|CDAT, Part 2: cdutil and genutil Modules.||Slides & Exercises|
|CDAT, Part 3: Graphics||Powerpoint | PDF|
|CDAT, Part 4: Binding with Other Languages||Slides & Exercises|
|Earth System Grid: Accessing and Analyzing AR5 Data and Future Directions for CDAT||Part 1: Powerpoint | PDF
Part 2: Powerpoint | PDF
|Gridding: Gridspec, Unstructured Grids, and Adding Mosaic Grid Support to LibCF|
|Language Exercise/Example: More on Using Arrays and OS Operations||Parts from the Arrays and I/O slides we didn’t get to previously|
|Case Study, Part 1: Hybrid-language Climate Models: A Python-Fortran Hybrid Version of the Neelin-Zeng QTCM1)||Slides & Paper|
|Case Study, Part 2: Analysis of In Situ Meteorological Measurements Using CDAT and Python||Slides & Exercises|
If you want all the files, the above files are available as the files.tar (73 Mb) collection of files. (This tarball contains datasets and code for the class, as well as the lectures and exercises slides.) Untar the collection by changing your current working directory to the one files.tar is in and then using the Unix command:
tar xvf files.tar
A directory files will be created in your current working directory, with the contents inside. If you cannot use the Unix tar command, visit the index page of files and download each file individually.
Note that some of the presentations make reference to the class virtual machine (VM). That VM is not available for download because of the large size of the file. Thus, the location of the Python, IDLE, f2py, etc. on your machine will probably differ from the class VM.