scientific-visualization-tools

To create publication-ready scientific figures from a CSV, use this Skill to generate static plots like scatter, box, violin, bar, and heatmap charts. It processes your tabular data using pandas and outputs high-quality images suitable for research publications.

Yes, you can generate interactive Plotly charts for exploratory data analysis. This Skill leverages the plotly dependency to produce interactive HTML charts and dashboards, allowing you to dynamically explore research data distributions and relationships.

You need pandas, matplotlib, seaborn, and plotly installed to plot scientific data. These dependencies handle data manipulation and rendering for both static publication figures and interactive visualizations.

Yes, this tool supports generating violin and box plots for comparing treatment arms. You can specify columns like 'arm' for the x-axis and 'response' for the y-axis to visualize drug response distributions across different groups.

The best way to save a matplotlib figure is by specifying the output file path during generation, such as saving directly to figures/assay_box.png. This Skill handles the file output automatically based on the path you provide.

Use seaborn for static publication-ready figures like heatmaps and violin plots, and use plotly for interactive HTML charts. This Skill supports both, allowing seaborn for print-quality outputs and plotly for dynamic exploratory analysis.