Multi-panel Figure Assembler

A Python-based tool for assembling multi-panel scientific figures. Automatically arranges 6 sub-figures (A-F) into a composite image with consistent styling, labels, and high-resolution output.

Features

• Automatic Layout: Supports 2×3 or 3×2 grid arrangements
• Edge Alignment: Intelligently crops/pads images to match dimensions
• Unified Typography: Consistent font sizing across all panels
• Auto Labeling: Adds panel labels (A-F) with customizable position
• High Resolution: Output at 300+ DPI for publication quality

Installation

Requires Python 3.8+ and the following packages:

pip install Pillow numpy

Optional for advanced features:

pip install opencv-python-headless

Usage

python scripts/main.py --input A.png B.png C.png D.png E.png F.png --output figure.png [OPTIONS]

Command Line Arguments

Argument	Required	Default	Description
--input / -i	Yes	-	6 input image paths (A-F)
--output / -o	Yes	-	Output file path
--layout / -l	No	2x3	Layout: 2x3 or 3x2
--dpi / -d	No	300	Output DPI (dots per inch)
--label-font	No	Arial	Font family for labels
--label-size	No	24	Font size for panel labels
--label-position	No	topleft	Label position: topleft, topright, bottomleft, bottomright
--padding / -p	No	10	Padding between panels (pixels)
--border / -b	No	2	Border width around each panel (pixels)
--bg-color	No	white	Background color (white/black/hex)
--label-color	No	black	Label text color (black/white/hex)

Parameters

Parameter	Type	Default	Description
--input	str	Required
--output	str	Required	Output file path
--layout	str	"2x3"
--dpi	int	300
--label-font	str	"Arial"
--label-size	int	24
--label-position	str	"topleft"
--padding	int	10
--border	int	2
--bg-color	str	"white"
--label-color	str	"black"

Examples

Basic usage:

python scripts/main.py -i A.png B.png C.png D.png E.png F.png -o figure.png

3×2 layout with custom DPI:

python scripts/main.py -i A.png B.png C.png D.png E.png F.png -o figure.png --layout 3x2 --dpi 600

Custom styling:

python scripts/main.py -i A.png B.png C.png D.png E.png F.png -o figure.png \
  --label-size 32 --label-position topright --padding 20 --border 4

Programmatic usage:

from scripts.main import FigureAssembler

assembler = FigureAssembler(
    layout="2x3",
    dpi=300,
    label_size=24,
    padding=10
)

assembler.assemble(
    inputs=["A.png", "B.png", "C.png", "D.png", "E.png", "F.png"],
    output="figure.png",
    labels=["A", "B", "C", "D", "E", "F"]
)

Output

The script generates a high-resolution composite figure with:

• All panels resized to uniform dimensions
• Panel labels (A-F) in specified positions
• Consistent padding and borders
• DPI metadata embedded in output file

Supported Formats

Input: PNG, JPG, JPEG, BMP, TIFF, GIF Output: PNG (recommended), JPG, TIFF

Notes

• Input images are automatically resized to match the largest dimension while maintaining aspect ratio
• For best results, use input images with similar aspect ratios
• Label fonts require the font to be available on your system
• PNG output preserves transparency if any input images have alpha channels

Risk Assessment

Risk Indicator	Assessment	Level
Code Execution	Python/R scripts executed locally	Medium
Network Access	No external API calls	Low
File System Access	Read input files, write output files	Medium
Instruction Tampering	Standard prompt guidelines	Low
Data Exposure	Output files saved to workspace	Low

Security Checklist

• No hardcoded credentials or API keys
• No unauthorized file system access (../)
• Output does not expose sensitive information
• Prompt injection protections in place
• Input file paths validated (no ../ traversal)
• Output directory restricted to workspace
• Script execution in sandboxed environment
• Error messages sanitized (no stack traces exposed)
• Dependencies audited

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