Overview

This workshop will provide participants with the tools and techniques to effectively communicate their research results using Matplotlib, a powerful and versatile plotting library in Python. Participants will learn how to use colormaps for colorblind accessibility, how to control label sizes and formatting legends, and how to think about manipulation of the visualizations they are trying to create.

In this workshop, we will delve into the importance of writing re-usable functions for plotting. By having a well-structured code base, researchers can enjoy numerous benefits, including:

  • Efficiency: By having a centralized source of plotting code, updates and changes can be made more quickly and easily, saving valuable time and allowing researchers to focus on their analysis and interpretation of results.

  • Reduced Errors: A well-architected code base can help reduce the risk of errors that might arise from duplicating code or making inconsistent changes. By having a single source of truth for plotting code, researchers can ensure that all plots are consistent and accurate.

  • Flexibility: Re-usable functions can be designed with flexibility in mind, so that they can be adapted to new data or updated to reflect changes in standards or best practices. This makes it easier to keep the code up-to-date and responsive to evolving needs.

Accelerating the Research-to-Paper Pipeline

In this workshop, we will keep in mind the ultimate goal of industry scientists and academics: publishing their research results. Effective communication of results is critical for advancing their careers, and well-structured plotting code can help them reach this goal.

By writing re-usable functions for plotting, participants will be able to quickly and easily make changes to their plots based on feedback from advisors, peers, or other reviewers. This eliminates the need to spend time on manual formatting, freeing up more time for innovation and analysis.

Additionally, having a well-architected code base reduces the risk of duplicating code or making inconsistent changes, increasing the likelihood that their results will be accepted for publication. In short, the skills and techniques taught in this workshop will help participants communicate their results more effectively and efficiently, enabling them to focus on what they do best: advancing their research.

Learning Outcomes

Learning Outcomes By the end of this workshop, participants will be able to:

  • Organize and label their results using figure and axes objects for effective communication.
  • Control label sizes, font choices, and overall plot formatting for clarity and readability.
  • Create legends that accurately convey the information in their plots and match the overall style.
  • Utilize colormaps for colorblind accessibility and understand the importance of color in communication.
  • Choose appropriate file formats and resolutions (vector vs pixel representations) for different purposes, such as publication, presentation, or online sharing.
  • Navigate the source code and documentation for matplotlib effectively, and use it to address their specific needs and preferences.
  • Write re-usable functions for plotting that can be easily modified in one place to address updates and changes.
  • Reduce time and effort spent on manual formatting and duplicated code, freeing up more time for innovation and analysis.
  • Communicate their results more effectively and efficiently, increasing the likelihood of their results being accepted for publication.

Workshop Format

The workshop is organized in the following modules:

  1. Presentation: A 50-80 minute presentation will provide an overview of the concepts and techniques for effective and organized plotting using matplotlib.

  2. Hands-on component: An optional 1-3 hour “office hours” style component will give participants the opportunity to practice what they’ve learned in a sandbox environment provided by the workshop. This will allow participants to manipulate plots, play with formatting improvements, and see the results of their changes in real-time, using a variety of realistic datasets (geospatial, image, time-series, etc).

  3. Application: An optional 1-3 hour “office style” component will cover applying the lessons of module (2) towards their specific research goals. participants will bring examples of their own data visualization results and we will work on incorporating the lessons learned. This section also presents an opportunity to collaborate and identify common issues they face while plotting their data. By combining their efforts, they can save time on plotting results and focus more on communicating them effectively and rapidly.

The hands-on and application components are designed to be supportive and interactive experiences, where participants can ask questions, receive feedback, and work at their own pace. These components provide valuable opportunities for participants to solidify their understanding of the material, apply it to real-world scenarios, and collaborate with their peers.

See the workshop structure page for more information about general workshop format.