paper-figure

You can generate publication-quality figures by providing your experimental data path to the automated plotting workflow. The system uses Python and matplotlib to create line plots, bar charts, scatter plots, heatmaps, and box plots suitable for academic papers.

Yes, you can create LaTeX tables for comparing method features, ablation results, and prior bounds. The automated table generation feature formats your experimental data into structured LaTeX code for direct inclusion in academic publications.

You can combine multiple plots into a single multi-panel figure by utilizing the subfigure grid functionality. This allows you to arrange several generated visualizations, such as scatter plots and heatmaps, into one cohesive layout for presentation.

Yes, you need Python and matplotlib installed because the figure generation relies on these dependencies for plotting. Additionally, numpy and pandas are required to process and manipulate the experimental data before visualization.

Yes, you can use Codex MCP to review figure quality during the generation process. This integration helps ensure that your automated visualizations meet the visual and formatting standards required for academic research papers.

The best way to automate data visualization for research presentations is to use a script-driven workflow that processes experiment results directly. This approach ensures consistency across figures and tables while eliminating manual graphic design work.