nature-figure

Create publication-quality Nature-style figures by defining a figure contract for layout and color consistency, then implementing a hero panel with supporting panels using Python (matplotlib/seaborn) or R (ggplot2/patchwork) backends.

Manage multi-panel figure layout and palette discipline by establishing a contract-driven workflow that defines a core conclusion, panel map, and evidence hierarchy before plotting, ensuring strict color consistency across complex Nature-family journal figures.

Export multi-panel figures to SVG, PDF, and TIFF formats by applying a reproducible export workflow that uses SVG as the primary output, supplemented by PDF, PNG, and TIFF for meeting specific journal submission requirements.

You cannot mix Python and R backends in the same figure workflow; strict backend exclusivity is required to ensure reproducible outputs, meaning you must choose either matplotlib/seaborn or ggplot2/patchwork/ComplexHeatmap for a given figure.

A Nature-style figure needs a figure contract before plotting to align team expectations by defining the core conclusion, panel map, evidence hierarchy, and export targets, ensuring the final visual argument is clear and cohesive.

Yes, this approach supports ComplexHeatmap and patchwork for R multi-panel figure assembly, providing reusable patterns for layout, direct labeling, and palette discipline within a contract-driven export workflow.