Skill: billwanttobetop/automd-viz (version 1.0.0)

# AutoMD-Viz - Publication-Quality Visualization for Molecular Dynamics **Version:** 1.0.0 **Author:** Xuan Guo ([REDACTED_SECRET]) **License:** MIT **Repository:** https://github.com/Billwanttobetop/automd-viz --- ## 📖 Overview AutoMD-Viz is a standalone visualization toolkit for generating publication-quality figures from molecular dynamics simulation data. It supports multiple visualization types and journal-specific styles (Nature, Science, Cell). **Key Features:** - 🎨 Molecular structure visualization (PyMOL) - 📊 Data plotting (Matplotlib/Seaborn) - 🎬 Trajectory visualization (PCA/t-SNE/UMAP) - 📦 Automated report generation - 🎯 Journal-specific styles (Nature/Science/Cell) - 🔧 High-resolution output (300-600 DPI, SVG/PDF/EPS) --- ## 🚀 Quick Start ### Installation ```bash # Via ClawHub clawhub install automd-viz # Or manual installation git clone https://github.com/Billwanttobetop/automd-viz.git cd automd-viz chmod +x automd-viz.sh ``` ### Basic Usage ```bash # Generate protein structure figure ./automd-viz.sh --type structure --structure protein.pdb --style nature # Plot RMSD/RMSF data ./automd-viz.sh --type data --input rmsd.xvg --style science # Trajectory visualization (PCA) ./automd-viz.sh --type trajectory --structure protein.pdb --trajectory md.xtc # Generate complete report ./automd-viz.sh --type report --structure protein.pdb --trajectory md.xtc --style nature ``` --- ## 📋 Visualization Types ### 1. Structure Visualization (`--type structure`) Generate high-quality molecular structure figures using PyMOL. **Options:** - `--structure <file>` - Input structure (PDB/GRO) - `--style <nature|science|cell>` - Journal style - `--representation <cartoon|surface|sticks>` - Display style - `--color <spectrum|chain|secondary>` - Coloring scheme - `--resolution <300|600>` - Output DPI **Example:** ```bash ./automd-viz.sh --type structure \ --structure protein.pdb \ --style nature \ --representation cartoon \ --color spectrum \ --resolution 600 ``` **Output:** - `structure_nature.png` (high-resolution raster) - `structure_nature.pse` (PyMOL session) --- ### 2. Data Plotting (`--type data`) Plot time-series data (RMSD, RMSF, energy, etc.) with journal-quality formatting. **Options:** - `--input <file>` - Input data file (XVG format) - `--style <nature|science|cell>` - Journal style - `--xlabel <text>` - X-axis label - `--ylabel <text>` - Y-axis label - `--title <text>` - Plot title **Example:** ```bash ./automd-viz.sh --type data \ --input rmsd.xvg \ --style science \ --xlabel "Time (ns)" \ --ylabel "RMSD (nm)" ``` **Output:** - `data_plot.pdf` (vector graphics) - `data_plot.png` (raster graphics) --- ### 3. Trajectory Visualization (`--type trajectory`) Visualize trajectory in reduced dimensionality space (PCA/t-SNE/UMAP). **Options:** - `--structure <file>` - Reference structure - `--trajectory <file>` - Trajectory file (XTC/TRR) - `--method <pca|tsne|umap>` - Dimensionality reduction method - `--style <nature|science|cell>` - Journal style **Example:** ```bash ./automd-viz.sh --type trajectory \ --structure protein.pdb \ --trajectory md.xtc \ --method pca \ --style nature ``` **Output:** - `trajectory_pca_2d.pdf` (2D projection) - `trajectory_pca_3d.pdf` (3D projection) - `free_energy_landscape.pdf` (FEL) --- ### 4. Automated Report (`--type report`) Generate a complete set of publication-ready figures. **Options:** - `--structure <file>` - Reference structure - `--trajectory <file>` - Trajectory file - `--input <dir>` - Analysis results directory - `--style <nature|science|cell>` - Journal style **Example:** ```bash ./automd-viz.sh --type report \ --structure protein.pdb \ --trajectory md.xtc \ --input analysis-results/ \ --style nature ``` **Output:** - `figures/` directory with all figures - `VISUALIZATION_REPORT.md` (summary) --- ## 🎨 Journal Styles ### Nature Style - Font: Arial - Font size: 7-9 pt - Line width: 0.5-1.0 pt - Color: Colorblind-friendly palette - Format: PDF/EPS (vector) ### Science Style - Font: Helvetica - Font size: 8-10 pt - Line width: 0.75-1.25 pt - Color: High-contrast palette - Format: PDF/EPS (vector) ### Cell Style - Font: Arial - Font size: 8-12 pt - Line width: 1.0-1.5 pt - Color: Vibrant palette - Format: PDF/EPS (vector) --- ## 🔧 Dependencies **Required:** - Python 3.7+ - NumPy - Matplotlib - Seaborn **Optional (for advanced features):** - PyMOL (structure visualization) - scikit-learn (PCA/t-SNE) - umap-learn (UMAP) - MDAnalysis (trajectory processing) **Auto-install:** ```bash pip install numpy matplotlib seaborn scikit-learn umap-learn MDAnalysis ``` --- ## 📚 Integration with AutoMD-GROMACS AutoMD-Viz is designed to work seamlessly with AutoMD-GROMACS analysis results. **After running analysis:** ```bash # Run analysis advanced-analysis -s md.tpr -f md.xtc # Visualize results automd-viz --type report --input advanced-analysis/ --style nature ``` **Supported analysis outputs:** - RMSD/RMSF/Rg (from `analysis.sh`) - PCA/Clustering (from `advanced-analysis.sh`) - Binding analysis (from `binding-analysis.sh`) - Trajectory analysis (from `trajectory-analysis.sh`) - Property analysis (from `property-analysis.sh`) --- ## 🐛 Troubleshooting See `publication-viz-errors.md` for common issues and solutions. **Quick fixes:** - PyMOL not found → Install PyMOL or use `--no-structure` - Font issues → Install required fonts or use `--font-fallback` - Memory errors → Reduce trajectory frames with `--stride` --- ## 📖 Examples See `examples/` directory for complete workflows: - `example_protein/` - Protein structure visualization - `example_ligand/` - Protein-ligand complex - `example_membrane/` - Membrane protein system - `example_trajectory/` - Trajectory analysis --- ## 🤝 Contributing Contributions welcome! Please submit issues and pull requests on GitHub. --- ## 📄 License MIT License - see LICENSE file for details. --- ## 📧 Contact - Author: Xuan Guo - Email: [REDACTED_SECRET] - GitHub: @Billwanttobetop


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