I spend way too much time tweaking Matplotlib and Seaborn defaults to meet the strict formatting guidelines of top-tier academic journals. I got tired of copying and pasting the same boilerplate code to fix fonts, line widths, and DPI settings, or manually editing SVGs later.
To fix my own workflow, I built cnsplots. It’s a lightweight wrapper that generates publication-ready figures (like those in Cell, Nature, and Science) with zero configuration, letting you focus on the data instead of fighting plotting aesthetics.
I'd love for anyone who regularly builds Python data visualizations to try it out. Open to any feedback or hearing how you handle standardizing your own plots!
I spend way too much time tweaking Matplotlib and Seaborn defaults to meet the strict formatting guidelines of top-tier academic journals. I got tired of copying and pasting the same boilerplate code to fix fonts, line widths, and DPI settings, or manually editing SVGs later.
To fix my own workflow, I built cnsplots. It’s a lightweight wrapper that generates publication-ready figures (like those in Cell, Nature, and Science) with zero configuration, letting you focus on the data instead of fighting plotting aesthetics.
I'd love for anyone who regularly builds Python data visualizations to try it out. Open to any feedback or hearing how you handle standardizing your own plots!