Welcome to SARP Programming

The NASA Student Airborne Research Program (SARP) is an earth science reasearch internship for undergraduates held every summer. The program is based out of Irvine, CA and Newport News, VA. At SARP, students experience 8 weeks of hands on NASA research and data training. This website hosts the online content for the programming instruction for the internship.

This year SARP is fortunate to have access to the Cryocloud platform for student research projects. The cloud is an exciting new science tool that allows researchers to more easily collaborate, spend less time setting up software environments, and scale their data analysis. Tutorials relating to doing science on the cloud are also hosted on this website.

Website Organization

The contents of this website is split into three sections:

• Lessons - Notebooks used for a lesson during the two code-intensive weeks. Many lessons include practice problems and answers in the Practice Sheets section

• Additional Lectures - Less formal notebooks accompanying more sporadic as-needed instruction throughout the summer

• Practice Sheets - Notebooks with practice problems to accompany some of the lessons

• Examples - Example code for common analyses or visualizations

• Contributor Guide - Want to help make this website better? Find out more about how you can do that in the Contributor Guide

Data Format Tracks

The Lessons section is divided into subsections based on data format. The two data format groups for SARP are:

1. gridded data - data in an array format that representing information in space. This format is often used for satellite images or model output.

2. tabular data - data with rows and columns. This is data that would fit well in an excel sheet

There is also a General Python section, which contains material relevant for anyone learning Python. In SARP everyone will work on the General Python section together, then we will split into two groups based on the two data formats described above.

Table of Contents

Lessons

• General Python
  • Variables, Data Types, and if Statements
  • Lists and Dictionaries
  • Datetimes
  • Spatial Data
• Gridded Data
• Tabular Data
  • The pandas Library
  • Filtering and groupby
  • geopandas
  • Static Visualization with matplotlib
  • Mapping with cartopy and Colors

Additional Lectures

• Debugging
• Time Series Statistics
• Hierarchical Data Structure
• Raster Data Lessons
  • Arrays and numpy
  • Working with rasters
  • Accessing Data
• Visualization
  • Building on top of matplotlib
  • Dynamic Visualization
  • Viewing Images

Practice Sheets

• General Python Practice
  • Variables, Data Types and If statements Practice
  • Lists and Dictionaries Practice
  • Datetimes Practice
  • Spatial Data Practice
  • Variables, Data Types and If statements Answers
  • Lists and Dictionaries Answers
  • Datetimes Answers
  • Spatial Data Answers
• Gridded Data Practice
  • xarray Practice
  • xarray Computation Practice
  • xarray Practice
• Tabular Data Practice
  • pandas Practice
  • Filtering and Groupby Practice
  • Practice with geopandas
  • pandas Answers
  • Filtering and Groupby Answers
• Raster Data Practice (Additional Lectures)

Examples

• Plot with multiple lines and a log axis
• Flight Track Plot
• Altitude Plot with Twin Axes
• Cartopy with gridded data

Contributor Guide

• Contributing to SARP Lessons