Matplotlib is among the most popular and advanced function libraries for the production of journal-quality data visualisations, with extensive, often under-utilised resources to make figures both richer in content and easier on the eye. In this course, participants will learn the finer details of producing figures for publication with Matplotlib, including tips on:

• how to choose plot types based on the quantity and distribution of data
• how to legibly display multiple sets of data or models on the same plot, and customise cycling through distinguishing features
• how (and why) to choose fonts and colors to accommodate common visual impairments
• how to customise axis label formats and tick locations
• how to standardise plot settings with rcparams.

This course consists of two lectures with demonstrations via Jupyter notebooks, and two exercise sessions for students to practice with prepared data. The course will be taught in English via zoom.

Prerequisites

Participants should be comfortable with Numpy, I/O of data tables, using dictionaries, formatting strings, and producing basic plots (e.g. line plots, scatter plots, or histograms) with Matplotlib.

Prior experience with subplots, error bars, images, and/or statistical plots (e.g., with Seaborne) is helpful but not required. Likewise, prior exposure to LaTeX will be helpful for formatting data labels, but the basic syntax will be included in the course, and a reference link for common math symbols will be provided.

For the hands on exercises access to a laptop or desktop computer with a working Python 3 installation is required. Further instructions will be shared with registered participants closer to the event.

Instructor

Rebecca Pitts (application expert at LUNARC, PhD in Astronomy) will be the principal instructor.

Schedule

• Lecture block 1 – 10:00-10:45
• Exercise block 1 – 11:00-12:00
• Lecture block 2 – 13:00-13:45
• Exercise block 2 – 14:00-15:00

Registration

For more information and registration, please visit the LUNARC event page.