Render this document as pdf by rst2pdf course_outline.rst
09:00 - 09:30 Introduction:
Taking seats, booting notebooks... Introduction of tutors and course outline, brief participant survey. Technology setup: Launching the Virtual Machine for the course via VirtualBox.
09:30 - 10:15 Collaborative Software Development:
The idea of treating users as co-developers is at the heart of OSS. We will demonstrate the ease of contributing to OSS projects.
10:15 - 10:30 Python Quick Start:
Python will be the key to use the presented radar software tools. Learn how to use Python for writing scripts and for interactive data analysis.
10:30 - 11:00 Coffee break
11:00 - 11:30 The Ipython Notebook:
Using the IPython notebook as an interactive Python environment.
11:30 - 12:30 Hands on Py-ART:
Overview of the Py-ART software, Exercises.
12:30 - 13:00 Hands on wradlib:
Overview of the wradlib software, Exercises.
13:00 - 14:00 Lunch break
14:00 - 14:30 Hands on wradlib:
cont.
14:30 - 15:30 Hands on BALTRAD:
Overview of the BALTRAD software, Exercises.
15:30 - 16:00 Coffee break
16:00 - 16:45 Interoperability Demonstration Experiment:
See BALTRAD, Py-ART and wradlib interact.
16:45 - 17:00 Feedback round:
Feedback of participants on the use of the presented OSS tools.
Time to settle in and say hello. Course tutors will open the course and introduce themselves and their projects, as well as the general structure and concept of the short course.
With a brief online survey, we will get an overview of the participants' background and motivations.
We will provide a "real-time" demonstration of collaborative development based on a Distributed Version Control System.
The Virtual Machine (VM) already comes with hg and git clients so everyone will be set. Course tutors will demonstrate the following steps:
User 1 Submit a pull request against this course outline User 2
Course participants can create their own accounts at http://githubcom or http://bitbucket.org where they can host the code they wrote during the course.
The Python quick start will cover e.g.:
- Installing Python, Python distributions / scientific stacks, most important dependencies - Starting Python from the shell and "hello world" - Executing a Python script - General guidelines on programming style and syntax in Python - Control flow - Array operations in Numpy - Plotting with matplotlib
Participants will learn how to use the IPython notebook for interactive data analysis and coding. The IPython notebook will also be the format for all the exercises within this course.
Session outline can be found at: https://github.com/EVS-ATMOS/pyart_short_course
These notebooks and associate data will automagically be loaded onto the Virtual machine for use in the course.. For static notebooks:
- Lesson 1: Introduction to the Py-ART data model
- http://nbviewer.ipython.org/github/EVS-ATMOS/pyart_short_course/blob/master/1%20Investigating%20the%20Py-ART%20Radar%20Object.ipynb
- Lesson 2: Simple Py-ART usage, a lesson from Miami
- http://nbviewer.ipython.org/github/EVS-ATMOS/pyart_short_course/blob/master/2%20Simple%20Py-ART%20Usage%20plotting%20PPI%20data%20on%20a%20map%20and%20add%20a%20new%20field.ipynb
- Lesson 3: Example Py-ART processing module, LP based phase proccessing
- http://nbviewer.ipython.org/github/EVS-ATMOS/pyart_short_course/blob/master/3%20Using%20LP%20to%20retrieve%20propigation%20phase%20from%20polarmetric%20phase%20shift.ipynb
- Lesson 4: Mapping multiple radars onto a grid and visualizing
- http://nbviewer.ipython.org/github/EVS-ATMOS/pyart_short_course/blob/master/4%20Gridding%20multiple%20NEXRAD%20to%20a%20mesh.ipynb
- Bonus 1: CHILL RHIs of Co Supercell
- http://nbviewer.ipython.org/github/EVS-ATMOS/pyart_short_course/blob/master/Bonus%201%2C%20CHILL%20RHI.ipynb
The entire session outline can be found at http://wradlib_short_course.bitbucket.org.
Overview:
- History and background - Community and collaboration - Development paradigm - Installation, documentation - Package structure and modules - Examples
Exercises:
- Read polar DX data from German Weather Service and University of Bonn - Georeferencing and plotting a PPI - Weighted compositing - Overlays with other geodata
Overview:
- Brief history and background - Development paradigm - Package structure and modules - ODIM_H5: the OPERA Data Information Model for use with the HDF5 file format - How to contribute
Examples:
- Read polar data and see how they're represented - Quality controls and the quality management infrastructure - Processing a lot of data at once - Generating composites/mosaics
In this final exercise, we will demonstrate pairwise interaction between the presented OSS tools.
We will show how BALRAD and Py_ART can directly eschange data in a Python environment:
@Scott, Daniel and Jonathan: Please outline the Py-ART + BALTRAD interaction.
We will show how BALTRAD and wradlib can exchange data via ODIM_H5 files:
- a polar volume from Suergavere (Estland) will be processed using BALTRAD's odx_toolbox - the result will be read, georeferenced and presented by wradlib - processing alternatives might be tested using wradlib's own processing capabilities
We will discuss, together with the participants, the perspectives for using OSS software in different institutional environments. Participants are invited to feedback on their impression of the presented OSS tools and whether these tools are an option for their future activities.