scientific-visualization

Scientific visualization transforms data into clear, accurate figures for publication. Create journal-ready plots with multi-panel layouts, error bars, significance markers, and colorblind-safe palettes. Export as PDF/EPS/TIFF using matplotlib, seaborn, and plotly for manuscripts. This skill should be used when: Apply journal-specific styles using the matplotlib style files in assets/ : For statistical plots, use seaborn with publication styling: Critical requirements (detailed in references/publication_guidelines.md ): Implementation: Always use colorblind-friendly palettes (detailed in references/color_palettes.md ): Recommended: Okabe-Ito palette (distinguishable by all types of color blindness): For heatmaps/continuous data: Always test figures in grayscale to ensure interpretability. Font guidelines (detailed in references/publication_guidelines.md ): Implementation: Journal-specific widths (detailed in references/journal_requirements.md ): Check figure size compliance: Best practices: Example implementation (see references/matplotlib_examples.md for complete code): See references/matplotlib_examples.md Example 1 for complete code. Key steps: Using seaborn for automatic confidence intervals: See references/matplotlib_examples.md Example 2 for complete code. Key steps: See references/matplotlib_examples.md Example 4 for complete code. Key steps: Using seaborn for...