Official Resources
- Source Repository: https://github.com/pranabdas/arpespythontools
- Documentation: https://pranabdas.github.io/arpespythontools/
- License: Open source
Overview
arpespythontools is a Python library for exploring, analyzing, and visualizing ARPES (Angle-Resolved Photoemission Spectroscopy) data. It provides tools for loading experimental ARPES data, momentum conversion, Fermi level alignment, band mapping, and curvature analysis.
Scientific domain: ARPES data analysis, momentum conversion, band mapping
Target user community: Researchers analyzing experimental ARPES data and comparing with DFT band structures
Theoretical Methods
- ARPES data loading and processing
- Momentum (k) conversion from angle
- Fermi level alignment
- Band mapping and tracking
- Second derivative / curvature analysis
- Background subtraction
Capabilities (CRITICAL)
- Load SES ARPES spectra
- Momentum (k) conversion
- Fermi level alignment
- Band mapping
- Curvature/second derivative analysis
- Background subtraction
- Multiple file format support
Sources: GitHub repository, documentation site
Key Strengths
Experimental ARPES:
- Direct experimental data loading
- Standard ARPES workflows
- Momentum conversion built-in
- Fermi level handling
Analysis Tools:
- Band mapping and tracking
- Curvature analysis for band identification
- Background subtraction
- Normalization
Lightweight:
- Minimal dependencies
- NumPy/Matplotlib based
- Easy to install
- Simple API
Inputs & Outputs
-
Input formats:
- SES spectra files
- ARPES data files
- Energy/Angle grids
-
Output data types:
- k-converted spectra
- Band maps
- Curvature plots
- Aligned data
Interfaces & Ecosystem
- NumPy: Numerical computation
- Matplotlib: Visualization
- Python: Core language
Performance Characteristics
- Speed: Fast (data processing)
- Accuracy: Experimental resolution
- System size: Typical ARPES datasets
- Memory: Moderate
Computational Cost
- Analysis: Seconds to minutes
- No DFT needed: Experimental data
- Typical: Efficient
Limitations & Known Constraints
- Experimental data only: Not for DFT simulation
- SES format focus: Limited other formats
- No DFT comparison built-in: Manual comparison
- Documentation: Could be more extensive
Comparison with Other Codes
- vs PyARPES: arpespythontools is simpler, PyARPES is comprehensive framework
- vs peaks: arpespythontools is lightweight, peaks is modern framework
- vs mpes: arpespythontools is general ARPES, mpes is multidimensional
- Unique strength: Lightweight ARPES data analysis with momentum conversion and curvature analysis
Application Areas
ARPES Experiments:
- Data loading and processing
- Momentum conversion
- Band identification
- Fermi surface mapping
Band Structure Comparison:
- Experimental vs DFT comparison
- Band tracking
- Fermi level alignment
- Spectral weight analysis
Surface Science:
- Surface state identification
- Bulk band mapping
- Fermi surface topology
- Spectral function analysis
Best Practices
Data Loading:
- Use appropriate file format
- Check energy/angle calibration
- Verify Fermi level
- Apply momentum conversion correctly
Analysis:
- Use curvature for band identification
- Apply background subtraction
- Normalize appropriately
- Compare with DFT for validation
Community and Support
- Open source on GitHub
- Documentation website available
- Research code
- Active development
Verification & Sources
Primary sources:
- GitHub: https://github.com/pranabdas/arpespythontools
- Documentation: https://pranabdas.github.io/arpespythontools/
Confidence: VERIFIED
Verification status: ✅ VERIFIED
- Source code: ACCESSIBLE (GitHub)
- Documentation: ACCESSIBLE (website)
- Specialized strength: Lightweight ARPES data analysis with momentum conversion and curvature analysis