Scientific Visualization
中级data最低上下文：64K
Creates publication-quality scientific figures and plots using matplotlib, seaborn, and plotly. Supports common scientific plot types including heatmaps, volcano plots, survival curves, network graphs, phylogenetic trees, and multi-panel figures with proper statistical annotations, color-blind safe palettes, and journal formatting.
使用场景
- Creating publication-ready figures for journal submissions
- Generating volcano plots for differential expression results
- Building multi-panel figures with consistent styling
- Producing interactive dashboards for exploratory analysis
- Making color-blind accessible scientific visualizations
- Formatting plots to specific journal requirements (Nature, Science, Cell)
示例提示词
Create a publication-quality multi-panel figure for a genomics paper. The figure should contain: - Panel A: Volcano plot of differential expression (log2FC vs -log10 p-value) - Panel B: Heatmap of top 50 differentially expressed genes - Panel C: UMAP embedding colored by cell type - Panel D: Bar plot of cell type proportions across conditions Requirements: - Use a color-blind safe palette (e.g., Wong or Tol) - 300 DPI, suitable for Nature-style journals - Consistent font sizes (axis labels 8pt, titles 10pt) - Panel labels (A, B, C, D) in bold - Statistical annotations where appropriate - Save as both PDF (vector) and PNG (raster) Generate the complete matplotlib/seaborn Python code with sample data.
推荐模型
兼容工具
claude-codecursorkiroany
模态
输入: text, code, file
→输出: text, code
相关 Skills
作者
OpenModels Community