pyprocar

Official Resources

• Homepage: https://romerogroup.github.io/pyprocar/
• GitHub: https://github.com/romerogroup/pyprocar
• Documentation: https://pyprocar.readthedocs.io/
• PyPI: https://pypi.org/project/pyprocar/
• License: GPL v3

Overview

pyprocar is a comprehensive Python library for pre- and post-processing of electronic structure data from multiple DFT codes. It excels at plotting band structures with orbital projections, Fermi surfaces, and spin textures with publication-quality output.

Scientific domain: Electronic structure visualization, multi-code post-processing Target user community: DFT users (VASP, QE, Elk, ABINIT) needing advanced visualization

Theoretical Background

pyprocar processes electronic structure data for:

• Projected band structures (fatbands)
• Fermi surface visualization
• Spin texture analysis
• Band unfolding from supercells

Capabilities (CRITICAL)

• Band Structure: Standard and projected band plotting
• Fermi Surface: 2D/3D Fermi surface visualization
• Spin Textures: Spin-resolved band analysis
• Band Unfolding: Supercell to primitive cell unfolding
• DOS/PDOS: Density of states analysis
• Multi-Code: VASP, QE, Elk, ABINIT support
• Publication Quality: High-quality matplotlib figures

Key Strengths

Multi-Code Support:

• VASP (PROCAR, vasprun.xml)
• Quantum ESPRESSO
• Elk
• ABINIT

Advanced Visualization:

• Orbital-projected fatbands
• Spin texture mapping
• 3D Fermi surfaces
• Customizable color schemes

Band Unfolding:

• Supercell unfolding
• Spectral weight visualization
• Defect/alloy band structures

Inputs & Outputs

• Input formats: PROCAR, vasprun.xml (VASP), QE output, Elk output, ABINIT output
• Output data types: Matplotlib figures, publication-ready plots

Installation

pip install pyprocar

Usage Examples

import pyprocar

# Plot band structure
pyprocar.bandsplot('PROCAR', outcar='OUTCAR', mode='plain')

# Projected bands (fatbands)
pyprocar.bandsplot('PROCAR', outcar='OUTCAR', mode='parametric',
                   orbitals=[0,1,2], atoms=[0])

# Fermi surface
pyprocar.fermi3D('PROCAR', outcar='OUTCAR', bands=[4,5])

# Band unfolding
pyprocar.unfold('PROCAR', outcar='OUTCAR', supercell_matrix=[[2,0,0],[0,2,0],[0,0,2]])

Performance Characteristics

• Speed: Efficient Python implementation
• Memory: Handles large PROCAR files
• Visualization: High-quality matplotlib output

Limitations & Known Constraints

• PROCAR required: Needs projection data
• Memory: Large supercells need significant RAM
• Learning curve: Advanced features require practice

Comparison with Other Tools

• vs sumo: pyprocar has more projection features
• vs vaspvis: pyprocar multi-code, vaspvis VASP-only
• Unique strength: Multi-code support, comprehensive projections

Application Areas

• Orbital character analysis
• Spin texture visualization
• Fermi surface studies
• Defect band structures
• Alloy electronic structure

Verification & Sources

Primary sources:

1. Documentation: https://pyprocar.readthedocs.io/
2. GitHub: https://github.com/romerogroup/pyprocar

Confidence: VERIFIED - Developed by Romero Group

Verification status: ✅ VERIFIED

• Official homepage: ACCESSIBLE
• Documentation: COMPREHENSIVE
• Source code: OPEN (GitHub, GPL v3)
• Developer: Romero Group (WVU)
• Active development: Regular releases