Pre-event Material: Reference

Key Points

Intro and Preparation for ICESat-2 Hackweek
  • hackweeks combine interactive tutorials with open project time in a shared learning environment

  • everyone will need their own laptop to participate

  • we offer all tutorials in the Python programming language

  • we require everyone to work through these preliminary tutorials before arriving

Getting set up with Git and GitGub
  • Git is a version control system; GitHub is a web environment for sharing code

  • we use Git as a way to control who has access to our cloud resources

  • You must have a GitHub account to attend this hackweek

  • Your GitHub membership in our organization must be set to “Public” so you can access our shared cloud resources

Getting Connected to our Shared Computing Environment
  • JupyterHub provides a consistent environment within which we can work on tutorials and projects

  • accessing JupyterHub servers requires a GitHub userid

  • JupyterHub offers each participant their own separate Jupyter Notebook environment and disk space for storing temporary scripts and files

Getting Started with Conda
  • everyone is encouraged to arrive with Python installed on their laptop for the project work

  • there are several different versions of Python, but we will use Python 3.6 for this hackathon

  • Conda package manager will be used to install Python and other libraries

  • Conda can be installed in two ways (Anaconda and Miniconda)

  • Conda package manager works across systems

Getting a NASA Earthdata login
  • An Earthdata login is needed for you to be able to gain access to the latest ICESat-2 data from NASA

Introductory Python Resources
  • Python is a free, open source programming language widely used in data analysis and scientific computing. Introductory tutorials are freely available online.

An Introduction to the Scientific Python Ecosystem
  • With Numpy, Scipy and Matplotlib (along with a vast ecosystem of related and more specialized tools), the Python programming language offers a flexible and robust platform for many tasks in scientific research, from quick one-off analyses to large-scale projects.

An Introduction to the Pandas Library
  • With Pandas, Python provides an excellent environment for the analysis of tabular data.

FIXME: more reference material.