Setting Up Your Computer for the Workshop

Several of the Workshop sessions will involve hands-on activities that allow you to experiment with the technologies speakers are describing. To make the most effective use of your time at the workshop, the presenters have suggested that you prepare your laptop computing environment before you arrive.

Basic Computing Environment

A basic familiarity with the following core software tools will be very helpful:

• git
  If you don't have it already, install the git version control tools on your system. You can find instructions for installing git you your platform on the Unidata Python Training Workshop site.
• Github
  If you do not already have a Github account, create one and experiment a bit to gain familiarity. You don’t need to be an expert, but it would be helpful if you’ve created an account and poked around.
• Python Environment
  If you don't already have an Anaconda-based Python distribution, install Miniconda Python (version 3.6) from https://conda.io/miniconda.html.
  Workshop presenters have provided a list of specific Python packages they will be using, and which you will need in order to follow along with the hands-on exercises. The easiest way to make sure you have the required packages is to use the conda package management system to create (and potentially update) your environment. Instructions describing how to do this are on the Unidata Users Workshop Github site: browse to
  https://github.com/Unidata/unidata-users-workshop
  and follow the instructions there.
• Jupyter notebooks
  If you’re not already familiar with Jupyter notebooks, it would be useful to gain some familiarity before the workshop.
• Archive extraction tool
  Some presenters will be making sample data available in .tar.gz format files. If you have a laptop running Linux or MacOS, your operating system will open these files natively. If your laptop is running Windows, you'll need a utulity program such as the freely-available 7-Zip package to be able to open the data archive files.

Getting Help

Unidata’s Online Python Training site contains a wealth of information to help you learn about all of these tools. Additionally, the first workshop session (Monday, June 25) will be devoted to getting the technology in order; Unidata staff and others will be on hand to assist with any configuration issues that arise. If you have questions about configuring your Python environment before you arrive in Boulder, you can send e-mail to support-python@unidata.ucar.edu.

Presenters' Additional Suggestions

Some presenters are making additional information relevant to their talks available prior to the workshop. These sites will doubtless be in flux, but check them out if you have the time and interest before the workshop begins.

• Sandro Fiore: The ENES Climate Analytics Service
  If you'd like to experiment with the technologies described in this talk, install a virtual machine player (e.g. VirtualBox or VMWare Player)
  Download the Ophidia VM: https://download.ophidia.cmcc.it/vmi_desktop/1.2/OphidiaVM_1.2.ova
  Presentation slides
• David Gagne: The Joy of Deep Learning: Simulated Mesocyclone Prediction
  Visit https://github.com/djgagne/swirlnet
• Kevin Goebbert: Using GOES-16 in Teaching Meteorology Using Python
  Visit: https://github.com/kgoebber/satellites_in_classroom
• Alex Haberlie: Classifying Rainfall Regions in Weather Radar Mosaics
  Visit https://github.com/ahaberlie/unidata-workshop-2018
• Joe Hamman: Reducing Time to Science with PANGEO
  Visit http://pangeo-data.org/
  Visit https://github.com/pangeo-data/pangeo
• Lak Lakshmanan: How Cloud Computing and Machine Learning are Transforming Science
  To follow along with this session during the workshop, sign up for a Google Cloud Platform free trial account
  Visit Google Code Labs
• Luke Madaus: WRF Simulations on the Cloud with Community Datasets
  If you're interested in experimenting with the containerized version of WRF after the workshop, install Docker
• Ryan Sobash: Web-based Visualization of Ensemble Forecast Datasets
  Visit: https://github.com/rsobash/unidata
• Tyler Wixtrom: Ensemble Forecast Visualization and Verification in Python
  Visit: https://github.com/tjwixtrom/workshop2018