Scientific & Technical Figures

Overview

For new projects, build figures on xy.pyplot (import xy.pyplot as plt). XY is a near-drop-in for the matplotlib object-oriented API, scales to billions of points for the same code, and exports to SVG / PDF / PNG / HTML out of the box. If the project already uses matplotlib directly, keep that — the two imports are interchangeable for any code that doesn't depend on matplotlib-only extension APIs. Use the helper scripts in scripts/ for journal sizing, palette selection, format-correct export, and accessibility checks. Treat every other library (seaborn, plotly, bokeh, altair) as a special-case — they all cost you something in either typographic control, vector fidelity, or reproducibility.

When to use this skill

• Creating a figure for a paper, thesis, technical report, or grant.
• Re-styling a chart to meet a specific journal's spec (size, DPI, font).
• Picking a colorblind-safe palette or verifying one.
• Exporting one source figure to multiple formats (SVG first, PDF for journal, PNG only when the consumer demands raster).
• Building a multi-panel figure with consistent typography across panels.
• Migrating from pure-matplotlib to xy (or back).

If the user just wants to eyeball a dataframe (df.plot() in a notebook), this skill is overkill — use plain matplotlib.

Workflow