Figure Legend Generator

Generate publication-quality figure legends for scientific research charts and images.

Supported Chart Types

| Chart Type | Description | |------------|-------------| | Bar Chart | Compare values across categories | | Line Graph | Show trends over time or continuous data | | Scatter Plot | Display relationships between variables | | Box Plot | Show distribution and outliers | | Heatmap | Display matrix data intensity | | Microscopy | Fluorescence/confocal images | | Flow Cytometry | FACS plots and histograms | | Western Blot | Protein expression bands |

Usage

python scripts/main.py --input <image_path> --type <chart_type> [--output <output_path>]

Parameters

| Parameter | Required | Description | |-----------|----------|-------------| | --input | Yes | Path to chart image | | --type | Yes | Chart type (bar/line/scatter/box/heatmap/microscopy/flow/western) | | --output | No | Output path for legend text (default: stdout) | | --format | No | Output format (text/markdown/latex), default: markdown | | --language | No | Language (en/zh), default: en |

Examples

# Generate legend for bar chart
python scripts/main.py --input figure1.png --type bar

# Save to file
python scripts/main.py --input plot.jpg --type line --output legend.md

# Chinese output
python scripts/main.py --image.png --type scatter --language zh

Legend Structure

Generated legends follow academic standards:

1. Figure Number - Sequential numbering
2. Brief Title - Concise description
3. Main Description - What the figure shows
4. Data Details - Key statistics/measurements
5. Methodology - Brief experimental context
6. Statistics - P-values, significance markers
7. Scale Bars - For microscopy images

Technical Notes

• Difficulty: Low
• Dependencies: PIL, pytesseract (optional OCR)
• Processing: Vision analysis for chart type detection
• Output: Structured markdown by default

References

• references/legend_templates.md - Templates by chart type
• references/academic_style_guide.md - Formatting guidelines

Risk Assessment

| Risk Indicator | Assessment | Level | |----------------|------------|-------| | Code Execution | Python scripts with tools | High | | Network Access | External API calls | High | | File System Access | Read/write data | Medium | | Instruction Tampering | Standard prompt guidelines | Low | | Data Exposure | Data handled securely | Medium |

Security Checklist

• [ ] No hardcoded credentials or API keys
• [ ] No unauthorized file system access (../)
• [ ] Output does not expose sensitive information
• [ ] Prompt injection protections in place
• [ ] API requests use HTTPS only
• [ ] Input validated against allowed patterns
• [ ] API timeout and retry mechanisms implemented
• [ ] Output directory restricted to workspace
• [ ] Script execution in sandboxed environment
• [ ] Error messages sanitized (no internal paths exposed)
• [ ] Dependencies audited
• [ ] No exposure of internal service architecture

Prerequisites

# Python dependencies
pip install -r requirements.txt

Evaluation Criteria

Success Metrics

• [ ] Successfully executes main functionality
• [ ] Output meets quality standards
• [ ] Handles edge cases gracefully
• [ ] Performance is acceptable

Test Cases

1. Basic Functionality: Standard input → Expected output
2. Edge Case: Invalid input → Graceful error handling
3. Performance: Large dataset → Acceptable processing time

Lifecycle Status

• Current Stage: Draft
• Next Review Date: 2026-03-06
• Known Issues: None
• Planned Improvements:
  • Performance optimization
  • Additional feature support