Larch

Official Resources

• Homepage: https://xraypy.github.io/xraylarch/
• GitHub: https://github.com/xraypy/xraylarch
• Documentation: https://xraypy.github.io/xraylarch/
• PyPI: https://pypi.org/project/xraylarch/
• Publication: M. Newville, J. Synchrotron Rad. 20, 913 (2013)
• License: BSD 3-Clause License

Overview

Larch is an open-source Python library and set of applications for processing and analyzing X-ray absorption spectroscopy (XAS) data from synchrotron beamlines. It provides comprehensive tools for XAFS (X-ray Absorption Fine Structure), including XANES (near-edge) and EXAFS (extended) analysis, as well as XRF (X-ray fluorescence) mapping and analysis.

Scientific domain: X-ray absorption spectroscopy, XAFS, XANES, EXAFS, XRF Target user community: Synchrotron beamline scientists and XAS researchers

Theoretical Methods

• XAFS data processing and analysis
• Background subtraction and normalization
• Fourier transform for EXAFS
• FEFF path fitting
• Linear combination fitting
• Principal component analysis
• XRF peak fitting

Capabilities (CRITICAL)

• XANES Analysis: Pre-edge, edge, and post-edge analysis
• EXAFS Analysis: Fourier transform, fitting with FEFF paths
• XRF Mapping: Fluorescence data processing
• GUI Applications: Larix (XAS Viewer), GSE MapViewer
• FEFF Integration: Path fitting with FEFF calculations
• Batch Processing: Scripting for high-throughput
• Database: X-ray absorption edge and emission line data

Sources: Larch documentation, J. Synchrotron Rad. publication

Key Strengths

Comprehensive XAS:

• Full XAFS workflow
• XANES and EXAFS analysis
• Background removal
• Normalization and merging

Python Native:

• Modern Python implementation
• NumPy/SciPy based
• Jupyter compatible
• Scriptable workflows

GUI Applications:

• Larix for XAS viewing
• Interactive fitting
• Visualization tools
• User-friendly interface

Inputs & Outputs

• Input formats:

  • ASCII data files
  • HDF5 files
  • Various beamline formats
  • FEFF output files

• Output data types:

  • Processed spectra
  • Fit results
  • χ(k) and χ(R) data
  • Reports and figures

Installation

pip install xraylarch
# Or with conda
conda install -c conda-forge xraylarch

Usage Examples

from larch import Interpreter
from larch.xafs import autobk, xftf

# Create Larch session
session = Interpreter()

# Read and process XAS data
data = read_ascii('fe_foil.dat')

# Background subtraction
autobk(data, rbkg=1.0, kweight=2)

# Fourier transform
xftf(data, kmin=2, kmax=12, dk=2, window='hanning')

# Plot results
plot_chir(data)

Performance Characteristics

• Speed: Efficient Python/NumPy implementation
• Memory: Handles large datasets
• Parallelization: Batch processing support

Limitations & Known Constraints

• XAS focus: Primarily for X-ray absorption
• Learning curve: XAS concepts required
• FEFF needed: Path fitting requires FEFF calculations
• Beamline formats: Some formats need custom readers

Comparison with Other Tools

• vs Demeter: Larch Python-native, Demeter Perl-based
• vs FEFF: Larch analysis, FEFF calculation
• vs Athena/Artemis: Larch is modern successor
• Unique strength: Python ecosystem, comprehensive XAS

Application Areas

• Synchrotron XAS experiments
• Catalysis research
• Environmental science
• Materials characterization
• Coordination chemistry

Best Practices

• Calibrate energy scale
• Use appropriate background parameters
• Validate with reference compounds
• Document processing parameters

Community and Support

• GitHub repository
• Mailing list (XAFS community)
• Active development
• Matt Newville (developer)

Verification & Sources

Primary sources:

1. GitHub: https://github.com/xraypy/xraylarch
2. M. Newville, J. Synchrotron Rad. 20, 913 (2013)
3. Documentation: https://xraypy.github.io/xraylarch/

Confidence: VERIFIED - Published in J. Synchrotron Rad.

Verification status: ✅ VERIFIED

• GitHub repository: ACCESSIBLE
• Documentation: COMPREHENSIVE
• Source code: OPEN (BSD-3)
• Academic citations: Well-cited
• Active development: Maintained