Figure Generator

Generate publication-quality visualization code for research figures.

Figure Types

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)

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.)