Scientific Figure

Scientific Figure

Compose publication-quality multi-panel figures at exact journal dimensions (mm/pt), validate font sizes against journal minima before export, and write PDF/PNG that the journal will accept without resize.

Why this skill exists

The previous react-pdf workflow had two structural failures:

1. Composition did not respect physical size. Flexbox layout shifted dimensions in subtle ways during render.
2. Fonts shrank below readable thresholds. When content overflowed, react-pdf uniformly scaled the entire figure, sometimes pushing axis labels under 4 pt.

This skill replaces that workflow. Panels are placed at exact mm coordinates, text is preserved as SVG `` elements so font sizes are inspectable before export, and the validator rescales individual panels rather than the whole figure when a font minimum is violated.

Pipeline

1. Plan        2. Build elements        3. Compose          4. Validate fonts     5. Export
   (journal      (matplotlib/seaborn      (svgutils, at        (per-element pt vs    (Inkscape if
    size, panel   per panel; SVG out;     exact mm/pt;         journal minimum;      present;
    grid)         optional icons)         text preserved)      rescale panel if      cairosvg
                                                               below minimum)        otherwise)

Every step uses on-the-fly execution via uv run --with so no permanent installs are required for the skill itself.

Step 1: Plan the figure

Before generating anything, fix the following:

• Target journal — sets the canvas width. See references/journal-specs.md for the full table; the four most common are summarized below.
• Panel grid — 1x1, 1x2, 2x2, wide-top + sub-panels, or freeform mm coordinates.
• Color palette — pick from references/color-palettes.md and reuse across all panels.

Journal width / font minimum cheat sheet

| Journal | 1 column | 2 column | Min body font | Notes | |---|---|---|---|---| | Nature | 89 mm | 183 mm | 5 pt | 8 pt for panel labels | | Science | 55 mm | 120 mm | 6 pt | Myriad/Helvetica preferred | | Cell | 85 or 112 mm | 174 mm | 6 pt | Max 225 mm tall | | PNAS | 87 mm | 180 mm | 6 pt | No label below 2 mm tall |

The validator in step 4 enforces these. Pick the journal early so the validator can warn early.

Step 2: Build the elements

Each panel is generated independently as an SVG. Common sources:

• Plots: matplotlib/seaborn, saved with bbox_inches='tight', transparent=True, format='svg'. See the [[plot-styling]] skill for the library decision tree and SciencePlots recipes.
• Icons: transparent PNGs from the [[transparent-icons]] skill.
• Schematics: for new Python-driven work use [[svg-primitives]] (auto-fit boxes, validated arrows, layered z-order); for hand-authored SVG patterns see [[svg-figure]].

Save each element to a working directory (typically panels/), then compose them in step 3.

Step 3: Compose the figure

The composer is built around svgutils (MIT license; uv run --with svgutils). It places panels at exact mm coordinates and preserves text as inspectable SVG `` elements.

Two ways to compose: the Figure helper in scripts/compose.py (most cases) and direct svgutils.compose for full control.

Recipe A: helper (recommended)

uv run --with svgutils --with lxml python scripts/compose.py panels-config.json -o figure.svg

panels-config.json schema:

{
  "width_mm": 183,
  "height_mm": 120,
  "journal": "nature",
  "panels": [
    {"id": "A", "src": "panels/spectrum.svg", "x_mm": 0,  "y_mm": 0, "scale": 0.5, "label": "A"},
    {"id": "B", "src": "panels/topomap.svg",  "x_mm": 92, "y_mm": 0, "scale": 0.5, "label": "B"},
    {"id": "C", "src": "panels/timecourse.svg", "x_mm": 0, "y_mm": 60, "scale": 1.0, "label": "C", "width_mm": 183}
  ]
}

Panel labels (A, B, C) are placed at top-left of each panel in 12 pt bold sans-serif.

Recipe B: direct svgutils (full control)

from svgutils.compose import Figure, SVG, Panel, Text

fig = Figure(
    "183mm", "120mm",
    Panel(
        SVG("panels/spectrum.svg").scale(0.5),
        Text("A", 5, 15, size=12, weight="bold"),
    ).move(0, 0),
    Panel(
        SVG("panels/topomap.svg").scale(0.5),
        Text("B", 5, 15, size=12, weight="bold"),
    ).move(92, 0),
)
fig.save("figure.svg")

See references/composition-workflow.md for the patterns (panel scaling, label placement, scale bars, multi-panel grid utilities).

Step 4: Validate fonts before export

This is the step that prevents the journal-rejection scenario. Run:

uv run --with lxml python scripts/validate_fonts.py figure.svg --journal nature

The validator parses every ` and element with a font-size`, walks the accumulated transform stack to compute the effective font size at the final physical dimensions, and reports anything below the journal minimum. Output is JSON:

{
  "svg": "figure.svg",
  "journal": "nature",
  "minimum_pt": 5.0,
  "checked_count": 47,
  "skipped_count": 0,
  "issue_count": 1,
  "issues": [
    {
      "text": "Frequency (Hz)",
      "specified_pt": 9.0,
      "effective_pt": 4.5,
      "scale_x": 0.5,
      "scale_y": 0.5,
      "minimum_pt": 5.0,
      "tag_id": ""
    }
  ]
}

skipped_count counts text elements where no font-size could be resolved (CSS class selectors, inherited styles). Exit codes: 0 clean, 1 issues found, 2 script error (malformed SVG, missing file).

If a panel is the culprit (its .scale() is too small), three remedies:

1. Rescale that panel up (and other panels down) instead of accepting the small text.
2. Increase the source plot's font size so that even at panel scale 0.5 it still passes (e.g., 12 pt source → 6 pt at 0.5 scale, which passes Science 6 pt minimum).
3. Switch to a larger canvas (e.g., upgrade 1-col to 1.5-col).

See references/font-validation.md for the full mechanics and rationale.

Step 5: Export to PDF/PNG

uv run --with cairosvg python scripts/export.py figure.svg --out figure.pdf --dpi 300

export.py detects Inkscape on $PATH at runtime. When present, Inkscape produces the highest-fidelity PDF (text remains text, fonts subsetted). When absent, the script falls back to cairosvg with a stderr warning that text without an installed font may be converted to paths or skipped.

Installing Inkscape (one-time, recommended)

brew install inkscape    # macOS
sudo apt install inkscape # Debian/Ubuntu

brew install inkscape is a single line and a ~200 MB one-time cost; once installed the script auto-detects it. The cairosvg fallback works without Inkscape but produces lower-fidelity PDFs when journal-required fonts are not installed.

Caption guidelines

Generate a figure caption that:

• Starts with a concise title (bold, one sentence).
• Describes each panel: "(A) Description. (B) Description. (C) Description."
• Defines all abbreviations on first use (e.g., "electroencephalography (EEG)").
• States sample sizes, statistical tests, and error bar meanings.
• States scale bar values if present.

Quality assurance

The [[figure-qa]] agent proactively runs on the composed SVG to check geometric correctness, alignment, color-palette compliance, and label legibility. The agent invokes validate_fonts.py for the font-size pass under the hood and the SVG branch's palette and geometry checks alongside.

Additional resources

• references/composition-workflow.md — svgutils patterns and idioms
• references/font-validation.md — pt minimum mechanics, transform-stack math
• references/journal-specs.md — full table of journal dimensions and font rules
• references/color-palettes.md — colorblind-safe palettes (Wong, Okabe-Ito, viridis, Crameri)
• scripts/compose.py — svgutils composer (CLI + library)
• scripts/validate_fonts.py — font-size validator
• scripts/export.py — Inkscape/cairosvg exporter
• examples/two-column-figure.py — end-to-end working example (matplotlib panels → compose → validate → export)

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: neuromechanist
• Source: neuromechanist/research-skills
• License: BSD-3-Clause

Install and usage instructions live in the source repository linked above.