Official Resources
- Source Repository: https://github.com/kmnhan/erlabpy
- Documentation: https://erlabpy.readthedocs.io/
- License: Open source
Overview
erlabpy is a complete Python workflow for angle-resolved photoemission spectroscopy (ARPES) experiments. It provides tools to handle, manipulate, and visualize data from ARPES experiments with comprehensive analysis capabilities including momentum conversion, Fermi surface mapping, and band structure analysis.
Scientific domain: ARPES workflow, data analysis, visualization
Target user community: Researchers performing ARPES experiments needing a complete analysis workflow
Theoretical Methods
- ARPES data handling and manipulation
- Momentum conversion
- Fermi surface mapping
- Band dispersion analysis
- Symmetrization and averaging
- Background subtraction
- Self-energy analysis
Capabilities (CRITICAL)
- Complete ARPES workflow
- Data loading from multiple beamlines
- Momentum conversion
- Fermi surface mapping
- Band dispersion analysis
- Symmetrization
- Self-energy extraction
- Interactive visualization
Sources: GitHub repository, ReadTheDocs
Key Strengths
Complete Workflow:
- End-to-end ARPES analysis
- From raw data to publication
- Multiple beamline support
- Consistent workflow
Advanced Analysis:
- Self-energy extraction
- Symmetrization
- Momentum distribution curves
- Energy distribution curves
- Fermi surface mapping
Interactive:
- Interactive visualization
- Jupyter integration
- Real-time exploration
- Publication-quality output
Inputs & Outputs
-
Input formats:
- Multiple beamline formats
- ARPES data files
- Energy/Angle grids
-
Output data types:
- k-converted data
- Fermi surface maps
- Band dispersions
- Self-energy plots
Interfaces & Ecosystem
- NumPy/Xarray: Data handling
- Matplotlib/Holoviews: Visualization
- Python: Core language
Performance Characteristics
- Speed: Fast (data processing)
- Accuracy: Experimental resolution
- System size: Large ARPES datasets
- Memory: Moderate to high
Computational Cost
- Analysis: Seconds to minutes
- No DFT needed: Experimental data
- Typical: Efficient
Limitations & Known Constraints
- Experimental data only: Not for DFT simulation
- Specific beamline formats: May need adaptation
- Memory: Large datasets can be demanding
- Learning curve: Comprehensive tool
Comparison with Other Codes
- vs PyARPES: erlabpy is more complete workflow
- vs peaks: erlabpy is comprehensive, peaks is modern framework
- vs arpespythontools: erlabpy is full workflow, arpespythontools is lightweight
- Unique strength: Complete ARPES workflow with self-energy analysis and interactive visualization
Application Areas
ARPES Experiments:
- Full data analysis pipeline
- Multiple beamline support
- Automated processing
- Publication preparation
Strongly Correlated Systems:
- Self-energy analysis
- Quasiparticle dispersion
- Spectral weight transfer
- Kink analysis
Topological Materials:
- Dirac cone mapping
- Surface state identification
- Fermi surface topology
- Spin-resolved ARPES
Best Practices
Data Loading:
- Use appropriate beamline loader
- Check energy/angle calibration
- Verify Fermi level
- Apply momentum conversion
Analysis:
- Use symmetrization for gap measurement
- Extract self-energy carefully
- Compare with DFT for validation
- Use interactive tools for exploration
Community and Support
- Open source on GitHub
- ReadTheDocs documentation
- Active development
- Research community
Verification & Sources
Primary sources:
- GitHub: https://github.com/kmnhan/erlabpy
- Documentation: https://erlabpy.readthedocs.io/
Confidence: VERIFIED
Verification status: ✅ VERIFIED
- Source code: ACCESSIBLE (GitHub)
- Documentation: ACCESSIBLE (ReadTheDocs)
- Specialized strength: Complete ARPES workflow with self-energy analysis and interactive visualization