Icarus Figures — publication-grade scientific figures for papers

Turn "a dataset + a claim" into a publication-grade figure — the kind a top-journal editor accepts with no revision request. Icarus Figures is a self-contained Claude/Codex skill: reproducible matplotlib/seaborn plots for data figures, and original TikZ for hero/method figures.

Example output

The point is not "many chart thumbnails"; it is complex paper figures with hierarchy, insets, cross-panel evidence, and honest uncertainty.

Complex biomedical evidence panel - a single integrated figure with a hero response manifold, decision field, calibration/ROC diagnostics, volcano + Manhattan omics evidence, imaging plate, survival validation, forest plot, covariance heatmap, and mechanism graph (examples/complex_panels.py):

Complex spatial-physical evidence panel - field + streamline + trajectory + sensors as the hero panel, with response surface, spectrum, time-series uncertainty, QQ residual check, phase portrait, anomaly zoom, and registered imaging readouts:

Complex method/system figure - architecture, workflow, evidence flow, state machine, production timeline, dependency graph, clustering, set overlap, ternary trade-space, and quality-lift slopegraph:

Framework / method hero figure (original, compilable TikZ) - the figure a paper opens with: a two-paradigm framework (mechanistic + data-driven, fused at the contribution), with a real method object embedded in each lane (a degradation curve crossing its failure threshold, an actual MLP, the fusion's prediction lines) instead of text-in-boxes. One pdflatex away, zero compile risk to the main document (examples/hero_tikz/framework_hero.tex; see framework-figures.md):

Additional composite galleries are reproducible coverage examples, not the visual front door: showcase_gallery.py, coverage_gallery.py, domain_showcases.py.

Lower-level API smoke/demo galleries are kept as regression checks rather than the visual front door: gallery.py, archetypes_gallery.py, paradigms_gallery.py, modern_gallery.py, scientific_gallery.py, advanced_gallery.py, rich_gallery.py, before_after.py.

Quickstart (with Claude)

1. Put your data in data/processed/your_data.csv.
2. Open Claude in this folder and say:

   Plot a figure from data/processed/your_data.csv; the claim is "Y declines linearly with X".

3. The icarus-figures skill triggers and produces:
   • a reproducible script scripts/figN_<name>.py
   • the figure in three formats outputs/figures/figN_<name>.{pdf,png,svg}
   • a caption that states the conclusion
   • a row in outputs/figures/figure_manifest.csv

Use it as a library (no Claude needed)

pip install -e .          # or: pip install -e ".[extras]"  for pandas/seaborn/scipy

import numpy as np
from paperfig import paper_style, save, timeseries_ci, scatter_fit, heatmap

paper_style(font='sans', journal='ieee')   # font: 'sans'|'serif'|'cn'; journal: None|'ieee'|'nature'|'pnas'

t = np.linspace(0, 10, 50); y = 1 - np.exp(-0.3 * t)
fig, ax = timeseries_ci(t, y + np.random.normal(0, .05, 50), y, y - .06, y + .06,
                        ylabel='Signal $y$')
ax.set_title('My result')                  # tweak the returned fig/ax freely
save(fig, 'fig1_main_result')              # → outputs/figures/fig1_main_result.{pdf,png,svg}

48 callable chart types, each returning (fig, ax) or (fig, axes):

Best-fit route: quantitative and ML panels use the callable matplotlib API; mechanism / architecture / workflow figures use the TikZ composition paradigms; image-heavy evidence uses image_panel plus quantitative subpanels. Pie/donut charts are intentionally not highlighted because the cookbook treats them as low data-ink defaults; prefer sorted bars unless composition is the point.

• Journal presets — paper_style(journal=...) sets that publisher's single-column figure size + font sizes:

  journal	1-col width	journal	1-col width	journal	1-col width
  nature	89 mm	cell	85 mm	acs	3.33 in
  science	57 mm	pnas	87 mm	rsc	83 mm
  ieee	3.5 in	elsevier	90 mm	aps	86 mm

  (From each publisher's public author guidelines — verify against the current guidelines, specs drift.)

• LaTeX is optional — figures render without a LaTeX install; opt in with paper_style(tex=True) for true LaTeX typography.

Or just a matplotlib style (no API)

import paperfig                 # registers the style name
import matplotlib.pyplot as plt
plt.style.use('paperfig')       # now any plt.* plot is publication-grade

The idea

Publication-grade = the cookbook's §0b four axes, all true at once: Depth (the figure argues a mechanism) × Elegance (one figure, one claim) × Unimpeachable (carries its own uncertainty + reproducible) × Visible gap (reads journal-grade at a glance). Archetypes A1–A13 are the floor, not the ceiling.

Relation to SciencePlots

SciencePlots is the excellent, popular toolkit for matplotlib styles — and Icarus Figures borrows its best ideas through the compatible paperfig Python package: a plt.style.use('paperfig') .mplstyle, cascading journal presets, and citability. The differences:

• Icarus Figures does not require LaTeX (SciencePlots does); LaTeX is opt-in (tex=True).
• Icarus Figures adds what a style sheet can't: a quality bar (§0b four axes), hero/method-figure composition paradigms (§I P1–P6), callable archetype functions (timeseries_ci, alignment_scatter, …), and original TikZ for method figures (§K).

Think of it as: SciencePlots-style presets, plus the judgment and building blocks to make the figure itself publication-grade. If you only want journal styles, SciencePlots is great; if you want the figure's content held to a bar, use Icarus Figures.

Layout

Hard rules

Reproducible (a script reads data from a file; no inline data > 20 rows; no AI-generated images) · vector three-format (PDF + PNG + SVG; plus TIFF/EPS at journal submission via save(fig, name, formats=('pdf','tiff'), dpi=600)) · style preset called once · colorblind- and grayscale-safe · units / N / uncertainty on the figure · pass the §0b four axes + §F reproducibility checklist before "done".

These are enforced, not just exhorted: python scripts/critique.py scripts/figN_<name>.py static-analyses a figure script against the bar — hard-rule misses (no uncertainty, jet colormap, equal grid, default style, PNG-only) come back as FAIL (non-zero exit, so it gates CI), and the judgment-half (does the figure argue a mechanism? does one hero panel dominate? B&W-legible?) prints as prompts you answer by hand. See references/figure-critique.md.

Requirements

Python 3.9+, matplotlib, numpy, pandas; seaborn for heatmaps; a LaTeX install with TikZ for §K hero figures (optional). See requirements.txt.

License

MIT © 2026 TAO-QKV. Use it, fork it, adapt it for your papers.