visual-sync

To check figure consistency across R plotting scripts, you can audit your code to scan for font families, text sizes, color usage, line weights, and themes. This process surfaces visual inconsistencies across all figures in your project.

The best way to unify visual themes in multi-figure manuscripts is to audit plotting scripts for theme usage and legend positions, generate an interactive summary of inconsistencies, and defer changes until user approval to ensure coherent visuals.

Yes, the visual consistency audit works with both R and Python workflows. It scans plotting scripts in both languages to evaluate font families, text sizes, color palettes, and line weights to identify cross-figure coherence issues.

An interactive plotting script audit handles font and color inconsistencies by presenting findings in a structured interactive summary, highlighting cross-figure coherence issues, and guiding user decisions before applying any non-invasive changes.

Yes, you can review proposed visual theme changes before they are applied. The audit defers execution until user approval, facilitating a guided transition to a central theme setup without automatically modifying your original plotting scripts.