xRHEED

Welcome to the xRHEED documentation!

xRHEED is an xarray-based toolkit for analyzing Reflection High Energy Electron Diffraction (RHEED) images.

Explore the Introduction to learn about the method and project goals. Check out the Quick Usage guide for a fast start. See the Example Notebooks for interactive tutorials.

Contents

User Guide

• Introduction
  • What is RHEED?
  • Project Goals
• Installation
  • Using pip (from PyPI)
  • Using pip (editable install for development)
  • Using uv (with a virtual environment)
  • Dependencies
• Quick Usage
  • Working with Profiles
  • Next Steps
• Geometry
  • Primary coordinate system
  • Incident angle
  • Azimuthal sample rotation
  • Spherical-like diffraction angles
  • Screen geometry and coordinates
• Kinematic Diffraction Model
  • Ewald Sphere in Reciprocal Space
  • Algorithm for Calculating \(\theta\) and \(\varphi\)
• Data Loading
  • Using Plugins
  • Manual
  • Returned DataArray
• Logging
  • Example

Example Notebooks

• Getting Started
  • Load xRHEED module
  • Load a RHEED Image
  • Data format
  • The .ri Accessor
  • RHEED Image Preparation
  • High-Pass Filter
• Diffraction Profiles
  • Data Preparation
  • Profile Extraction
  • Profile Extraction Using the get_profile Method
  • Plotting the Profile
  • Converting the Profile to \(k_y\)
  • Profile Fitting
  • Fine Adjustment of the Screen Scale
  • Preparing a Final Plot
  • Vertical Profiles
• RHEED Image Stacks
  • Loading a Series of RHEED Images
  • Plot the Images from the Stack
  • RHEED Stack Profiles
  • 1D Profile from a 3D Stack
• Spot Calculation Using Ewald Construction
  • Preparation of RHEED Image Data
  • Construction of the 2D Lattice Object
  • The Ewald Class
  • Fine Adjustment
  • Fine Adjustment Example
  • Final Image
• Transforming into k-space
  • Prepare the Data
  • Use a High-Pass Filter
  • Prepare the 2D Lattice
  • Ewald Object
  • Final Transformation
  • Final Plot with the Reciprocal Lattice
• Multi-Image Transformation
  • Image Preprocessing and Correction
  • Linear fits for interpolation across all images
  • Prepare a Lattice Object for the Expected Reconstruction
  • Generate Ewald Object and Overlay Expected Spot Positions
  • Transform Image Stack to kx–ky Space
  • Compute the Mean Along the Azimuthal Dimension
  • Prepare the Final Plot
• Ewald Spot Matching
  • Algorithm Explained
  • Cache Data
  • Prepare the RHEED Data
  • Scenario I: Unknown Azimuthal Orientation of the Sample
  • Scenario II: Unknown Lattice Constant
  • Scenario III: Unknown Scale and Azimuthal Orientation
  • Summary

API Reference

• xrheed

Development

• Changelog
  • [2.1.0] – 2026-04-24
  • [2.0.0] – 2025-11-25
  • [1.3.4] – 2025-09-30
  • [1.1.2] – 2025-09-19
  • [1.1.1] – 2025-09-15
  • [1.0.0] – 2025-09-11
  • [0.5.0] – 2025-09-11
  • [0.4.0] – 2025-09-11
  • [0.3.0] – 2025-09-09
  • [0.2.0] – 2025-09-04
  • [0.1.0] – 2025-08-29
• Contributing to This Project
  • 🛠 Getting Started
  • 💬 Communication