academic-plotting

To generate publication-ready ML paper figures, supply your experiment data in CSV or JSON format to auto-select chart types and produce polished matplotlib and seaborn visuals. The system exports LaTeX-friendly PDF vector and PNG outputs with consistent styling for conference submissions.

Yes, you can automatically create system architecture diagrams by providing text describing your system components and relationships. The tool uses Gemini image generation to convert this method text into publication-quality workflow diagrams.

Yes, it applies reusable academic styling rules for fonts, color palettes, and sizing to ensure consistent formatting across all matplotlib and seaborn charts. It highlights your proposed method with a distinct color for clear comparison in ablation studies.

The best way to reproduce scientific visualization outputs is by using the deterministic figure generation scripts provided for each chart. This ensures reproducibility while maintaining consistent venue-ready styling and exporting LaTeX-friendly vector formats.

Yes, you need a GEMINI_API_KEY to generate workflow diagrams, as it powers the multi-attempt architecture diagram generation using Gemini. Data-driven matplotlib and seaborn charts do not require this API key for plotting experiment metrics.

Academic diagram generation fails without YAML configuration because the system requires YAML-configured naming and descriptions to properly structure the deterministic generation scripts. This setup ensures consistent styling and reproducible outputs for your publication figures.