vaspvis

Official Resources

• Homepage: https://derekdardzinski.github.io/vaspvis/
• GitHub: https://github.com/DerekDardzinski/vaspvis
• Documentation: https://derekdardzinski.github.io/vaspvis/
• PyPI: https://pypi.org/project/vaspvis/
• License: MIT License

Overview

vaspvis is a highly flexible and customizable Python library for visualizing electronic structure data from VASP calculations. It provides an intuitive API for generating publication-quality band structure and density of states plots with extensive customization options.

Scientific domain: Electronic structure visualization, VASP post-processing Target user community: VASP users requiring flexible band structure and DOS visualization

Theoretical Background

vaspvis processes VASP output to visualize:

• Kohn-Sham eigenvalues along k-paths
• Orbital-projected band character (fatbands)
• Total and projected density of states
• Spin-polarized electronic structure

Capabilities (CRITICAL)

• Band Structure: Standard, projected, and spin-polarized plots
• DOS/PDOS: Total and atom/orbital-projected density of states
• Fatbands: Orbital character visualization with variable line width
• Spin Textures: Spin up/down channel visualization
• Customization: Colors, line styles, energy ranges, projections
• Multi-plot: Combine band structure and DOS in single figure

Key Strengths

Flexibility:

• Extensive customization options
• Multiple projection schemes
• Configurable color maps
• Adjustable plot aesthetics

VASP Integration:

• Direct VASP file parsing
• EIGENVAL, PROCAR support
• KPOINTS path handling
• POSCAR structure reading

Publication Quality:

• Matplotlib-based rendering
• Vector format export (PDF, SVG)
• Customizable fonts and sizes
• Professional appearance

Inputs & Outputs

• Input formats:

  • EIGENVAL (eigenvalues)
  • PROCAR (projections)
  • KPOINTS (k-path)
  • POSCAR (structure)
  • INCAR (calculation parameters)

• Output data types:

  • Matplotlib figures
  • PNG, PDF, SVG images
  • Customizable plots

Interfaces & Ecosystem

• Python integration:
  • Matplotlib for visualization
  • NumPy for data handling
  • Object-oriented API

Installation

pip install vaspvis

Usage Examples

from vaspvis import Band, Dos

# Plot band structure
band = Band(folder='path/to/vasp/calc')
band.plot_bands()

# Plot with orbital projections
band.plot_atom_orbitals(atoms=[0], orbitals=[0,1,2])

# Plot DOS
dos = Dos(folder='path/to/vasp/calc')
dos.plot_dos()

Performance Characteristics

• Speed: Fast file parsing
• Memory: Efficient for standard calculations
• Visualization: High-quality matplotlib output

Limitations & Known Constraints

• VASP-specific: Only works with VASP output
• PROCAR required: Projections need LORBIT setting
• k-path: Requires proper KPOINTS setup

Comparison with Other Tools

• vs py4vasp: vaspvis more customizable, py4vasp official
• vs sumo: Both flexible, different API styles
• vs pyprocar: Similar capabilities, different interfaces
• Unique strength: Highly customizable matplotlib integration

Application Areas

• Band structure visualization
• DOS analysis
• Orbital character analysis
• Spin-polarized systems
• Publication figure generation

Best Practices

• Use LORBIT=11 or 12 for projections
• Set appropriate energy range
• Choose meaningful color schemes
• Export as vector formats for publications

Community and Support

• GitHub issue tracker
• Documentation with examples
• Active development

Verification & Sources

Primary sources:

1. Official documentation: https://derekdardzinski.github.io/vaspvis/
2. GitHub repository: https://github.com/DerekDardzinski/vaspvis

Confidence: VERIFIED

Verification status: ✅ VERIFIED

• Official homepage: ACCESSIBLE
• Documentation: AVAILABLE
• Source code: OPEN (GitHub, MIT)
• Developer: Derek Dardzinski
• Active development: Regular updates