Generate publication-quality visualization code for research figures.
Figure Types
| Type | Use for | Format |
|---|---|---|
| matplotlib | Quantitative data: bar charts, scatter plots, line graphs, heatmaps, box plots | Python |
| seaborn | Statistical visualizations: distributions, regressions, pair plots | Python |
| Mermaid | Conceptual diagrams: workflows, taxonomies, hierarchies, timelines | Mermaid |
Decision guide
- Numbers, measurements, time series → matplotlib/seaborn
- Concepts, relationships, processes → Mermaid
- When in doubt: generate both
Output Format
## Figure 1: [Title]
**Caption:** [Full figure caption as it would appear in a paper — what is shown and the key takeaway]
**Description:** [What this figure shows and why it matters for the research]
**Type:** Python (matplotlib/seaborn)
```python
import matplotlib.pyplot as plt
import numpy as np
# [Complete, runnable code with realistic placeholder data]
plt.tight_layout()
plt.savefig('figure1.png', dpi=300)
plt.show()
```
Code requirements
- Complete and runnable — no placeholders like
your_data_here - All imports included — never reference a library without importing it
- Realistic placeholder data — use domain-appropriate values and ranges
- Publication quality —
dpi=300, proper axis labels, legend, title - Accessible colors — avoid red/green only distinctions
Validation (always run after generating Python code)
Before presenting code to the user, validate syntax with Python's AST parser:
python -c "import ast; ast.parse(open('figure1.py').read()); print('✅ Syntax OK')"
If the check fails, fix the syntax error and re-validate before showing the result.
For inline code blocks, validate with:
import ast
code = """
# paste generated code here
"""
try:
ast.parse(code)
print("✅ Syntax OK")
except SyntaxError as e:
print(f"❌ Syntax error: {e}")
Note: AST validation checks syntax only — it does not catch runtime errors (e.g. wrong data shapes). Always include comments explaining how to adapt placeholder data to real data.
How to run
Python figures:
pip install matplotlib seaborn numpy
python figure1.py
Mermaid diagrams:
- Paste into mermaid.live
- Or use any Markdown renderer that supports Mermaid (Obsidian, GitHub, etc.)