PyARPES

Official Resources

• Homepage: https://github.com/chstan/arpes
• Documentation: https://arpes.readthedocs.io/
• Source Repository: https://github.com/chstan/arpes
• License: MIT License

Overview

PyARPES is a Python-based analysis framework for Angle-Resolved Photoemission Spectroscopy (ARPES) data. It provides tools for loading, processing, visualizing, and analyzing multidimensional ARPES datasets. PyARPES aims to streamline the workflow from raw data to publication-quality figures, supporting data from various synchrotrons and lab-based systems.

Scientific domain: ARPES data analysis, photoemission spectroscopy, electronic structure
Target user community: Experimental condensed matter physicists, ARPES practitioners

Capabilities (CRITICAL)

• Data Loading: Support for various file formats (HDF5, Igor, Fits, etc.) from major beamlines (ALS, SSRL, Diamond, etc.)
• Multidimensional Analysis: Handling of 2D, 3D, and 4D datasets (Energy, kx, ky, time/temperature)
• Visualization: Interactive and publication-quality plotting
• Processing: Normalization, background subtraction, curvature analysis
• Curve Fitting: MDC/EDC fitting, Fermi edge fitting
• Coordinate Transformation: Conversion between angles and momentum space
• Automation: Scriptable workflows for batch processing

Sources: PyARPES documentation, GitHub repository

Inputs & Outputs

• Input formats: .ibw (Igor Binary), .h5/.nxs (HDF5/NeXus), .fits, .txt, .zip
• Output data types: Processed datasets (HDF5), Matplotlib figures, fitted parameters

Interfaces & Ecosystem

• Python: Built on xarray, pandas, and matplotlib
• Jupyter: Designed for use in Jupyter notebooks
• xarray: Uses xarray for labeled multidimensional arrays

Workflow and Usage

1. Load data: data = load_data("file.ibw")
2. Preprocess: data = data.arpes.k_convert() (convert angles to k-space)
3. Visualize: data.sum("kx").plot()
4. Fit: Extract dispersions or gaps using built-in fitting tools

Performance Characteristics

• Python-based, leverages numpy/pandas for efficiency
• Handles large datasets via lazy loading (dask integration possible)

Application Areas

• Band structure mapping
• Fermi surface mapping
• Gap analysis in superconductors
• Time-resolved ARPES analysis

Community and Support

• Open-source (MIT)
• GitHub issues for support
• Developed by researchers at Stanford/UBC

Verification & Sources

Primary sources:

1. Homepage: https://github.com/chstan/arpes
2. Documentation: https://arpes.readthedocs.io/

Confidence: VERIFIED

Verification status: ✅ VERIFIED

• Website: ACTIVE
• Documentation: AVAILABLE
• Source: OPEN (GitHub)
• Development: ACTIVE (Research community)
• Applications: ARPES data analysis, visualization, coordinate transformation