plot-from-data

You generate publication-quality matplotlib plots by selecting a pre-built academic style template, substituting your raw numeric data into the script data region, and rendering a dpi=300 PNG using consistent reference parameters.

Supported academic visualization charts include bar charts, line charts with confidence bands, training curves with break markers, scatter charts for t-SNE clustering, broken-axis layouts, and dual-series radar charts for research workflows.

Yes, you can create a dual-series radar chart in matplotlib by selecting the radar style template and substituting your numeric data arrays into the script data section to render a comparative publication-quality figure.

No, you do not need to manually configure matplotlib parameters for ablation bar charts. The Skill applies a pre-built paper style template that automatically matches plotting parameters to the reference specs.

The best way to plot training curves with confidence bands is to use the line style template, replace the script data region with your array values, and generate a dpi=300 PNG using the style-matched reference parameters.

To handle broken-axis scatterplots for t-SNE clustering visualization, select the scatter style template, substitute your clustering numeric data into the script data section, and render the publication-quality figure.