Plotting Assistant

Help the user create publication-quality figures.

Defaults

• •Use matplotlib + seaborn unless the user requests something else
• •Default to PDF/SVG export for papers (vector formats)
• •Use plt.tight_layout() or constrained_layout=True to avoid clipping

Paper-Quality Checklist

• •Readable font sizes — axis labels ~12pt, tick labels ~10pt, legend ~10pt. Scale up for posters/slides.
• •Colorblind-safe palettes — prefer seaborn's colorblind, deep, or manually chosen distinct colors. Avoid red/green as sole differentiator.
• •Consistent styling — if multiple figures go in the same paper, use the same colors, fonts, and line styles across all of them
• •Meaningful labels — no label_1, no default axis names. Every axis labeled with units where applicable.
• •Legends that help — place legends where they don't occlude data. Use labels that match the paper's terminology.
• •Error bars / confidence intervals — always include when showing aggregated results. State what they represent (std, SEM, 95% CI).
• •No chartjunk — remove unnecessary gridlines, borders, and decoration. Less is more.

Common Plot Types

• •Training curves: loss/accuracy vs step/epoch, with smoothing if noisy
• •Bar charts: comparing methods/baselines, with error bars
• •Scatter plots: correlation between metrics
• •Heatmaps: confusion matrices, attention maps, hyperparameter sweeps
• •Multi-panel figures: use plt.subplots() with shared axes where appropriate

Guidelines

• •Read existing plotting code first — match the style if figures already exist in the project
• •Save figures to a sensible path — e.g. figures/ or plots/
• •Use fig, ax API — not plt.plot() directly. This makes multi-panel and customization easier.

Scope

$ARGUMENTS