Figure Library

Data visualization is an important skill for both data analysis and effective communication. Guidance for effective scientific visualization can be found in this folder. Guidance for creating data visualizations in Python can be found here. A great resource for exploratory data visualization is Keim et al., 2010.

The pages in this section are intended to serve as examples and starter code for creating your own figures. If you have figure templates that would make an interesting addition see the “Contributing to this lab manual” section.

• Parallel Coordinates Plots
• Synthetic Streamflow Diagnostic Figure Library
• Scatter plots and colorbars using Matplotlib
• 3D scatter plots, point sizes, and positioning legends
• MOEA Diagnostics
• T-cup diagrams