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name: scientific-figure-making description: Generates publication-ready scientific figures in Python/matplotlib with consistent house style (Helvetica/Arial-like fonts, high-contrast palettes, minimal spines, frameless legends). Use when creating bar, trend, scatter, heatmap, or multi-panel figures for papers, slides, or reports that must match repository aesthetics and print/vector export. version: 1.0.0 author: Chen Liu chen.liu.cl2482@yale.edu license: MIT tags: [Matplotlib, Scientific Figures, Publication Quality, Data Visualization, Bar Charts, Heatmaps, LaTeX, Research, Reproducibility] dependencies: [matplotlib>=3.7.0, numpy>=1.24.0]

Scientific Figure Making

Expert guidance for publication-ready scientific figures in Python/matplotlib: consistent house style, high-level plot helpers, and reproducible export. Implementation details, API, and examples live in the reference docs; this file is the quick reference.

Demos and guidance for this skill come from real figure-making scripts in the figures4papers repository (figure_* folders). See references/demos.md for links to each project; the design theory and patterns were derived from those scripts.

When to use this skill

• Creating publication-ready bar charts, trend plots, heatmaps, or multi-panel figures for papers or reports.
• Enforcing a consistent house style (fonts, colors, spines, export) across many figures.
• Needing print- and vector-safe output (PDF/SVG/PNG at 300 DPI or higher) with minimal manual tweaking.
• Reproducibility: same style and helpers produce the same look everywhere.

When NOT to use / Alternatives

• Interactive dashboards or web apps → Use Plotly, Altair, or Bokeh.
• Quick exploratory EDA → Use seaborn or pandas plotting; switch to this skill when finalizing for publication.
• Complex 3D or geographic viz → Use Mayavi, Plotly 3D, or dedicated GIS tools.
• Heavy design/infographics → Use Illustrator, Inkscape, or Figma; this skill targets data-driven academic figures.

Core concepts

• Apply publication style first. Set global typography and axes (font size, line width, spines off top/right, frameless legends). Choose scale by figure type: larger for dense bar/comparison panels, smaller for compact trend/scatter. Keep TeX off unless you need math and have a working TeX setup.
• Use the high-level plot helpers for grouped bars, trend lines with optional uncertainty, heatmaps, scatter, and a shaded sphere for conceptual diagrams. They encode house defaults (palette, scaling, legend placement). Finish with the shared export helper so layout and formats are consistent.
• Export rules: Default 300 DPI; use higher DPI for dense bar-heavy panels. Export uses tight bounding box and optional padding; support PNG, PDF, SVG as needed.

See references/api.md for the full API and conventions to implement.

Workflow 1: Bar chart for method comparison

• Apply publication style with the chosen figure style (font size, line width).
• Create figure and axes; use wide aspect (e.g. width 3–4× height) if many metrics.
• Use the grouped-bar helper with categories, series, labels; turn on value annotations if desired.
• Set y-limits so differences are visible.
• Export with the finalize helper (e.g. PNG and PDF, 300 DPI).

Workflow 2: Multi-panel figure with shared legend

• Apply publication style; create subplot grid (e.g. 1×3 or 2×2).
• Plot data on the first N-1 axes; collect legend handles/labels from one axis.
• On the last axis, turn the axis off and add a single legend with those handles/labels.
• Set titles and y-limits on data panels.
• Export with the finalize helper.

See references/common-patterns.md for layout patterns (ultra-wide panels, dedicated legend axis).

Palette policy

Use semantic colors from the skill palette:

• Blues for key/proposed methods.
• Greens for positive/improvement variants.
• Reds for contrasting baselines/ablations.
• Neutral for references/background categories.
• Highlight only for targeted callouts.

When choosing or extending palettes, consider Nature's artwork guide for journal-friendly, accessible color choices (e.g. hierarchy, contrast, and avoiding red/green-only distinctions).

Exact keys and hex values are in references/api.md.

Common issues

Issue	Solution
LaTeX not found / usetex errors	Use style with TeX disabled (default).
Fonts look wrong on another machine	Rely on the default font fallback chain (DejaVu Sans, Helvetica, Arial, sans-serif).
Legend overlaps data	Use a dedicated legend panel: add an extra subplot, turn axis off, and put the legend there. See references/common-patterns.md.
Bars too small or crowded	Use wider figsize (e.g. 3–4× width vs height) and/or fewer categories per row.
Shape/length mismatch errors	Ensure category count matches series columns; each trend series must match x length. See references/api.md.

References (deep dives)

• references/demos.md — Real-world demos: links to figures4papers figure_* project folders (bar charts, trends, heatmaps, multi-panel).
• references/design-theory.md — Typography, export policy, color theory, layout, bar/line/scatter encoding, reproduction rules.
• references/api.md — Full API and conventions to implement.
• references/common-patterns.md — Ultra-wide panels, dedicated legend axis, categorical bars, print-safe colors.
• references/tutorials.md — Step-by-step bar chart, multi-panel trend, heatmap.