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