Skip to main content

An xarray-based toolkit for RHEED image analysis

Project description

xRHEED

📡 An xarray-based toolkit for RHEED image analysis.


CI Documentation Status PyPI version License: MIT Linter: ruff Package manager: uv DOI


🔬 What is RHEED?

Reflection High-Energy Electron Diffraction (RHEED) is an experimental technique used to monitor and control the quality of crystal surfaces.
A high-energy electron beam (∼20 keV) strikes the surface at a grazing angle (< 5°), making the method highly surface-sensitive and probing only a few atomic layers.


🎯 Project Goals

xRHEED provides a flexible and extensible Python toolkit for RHEED image analysis:

  • 🖼️ Load and preprocess RHEED images
  • 📈 Generate and analyze intensity profiles
  • ✨ Overlay predicted diffraction spot positions (kinematic theory & Ewald construction)
  • 🔄 Transform RHEED images into kx–ky space
  • 🔍 Search for reconstruction lattice constants and rotations by calculating the matching coefficient between predicted and experimental data

👉 Note: xRHEED is not a GUI application. It is designed as an xarray accessory library, intended for use in interactive environments such as Jupyter notebooks.


Installation

Using PyPI

pip install xrheed

Using pip (editable install for development)

git clone https://github.com/mkopciuszynski/xrheed
cd xrheed
pip install -e .

Using uv (with virtual environment)

  1. Install uv.
  2. Clone the repository:
git clone https://github.com/mkopciuszynski/xrheed
cd xrheed
  1. Create and activate a virtual environment.
  2. Sync dependencies:
uv sync

🚀 Quick Usage

import matplotlib.pyplot as plt
import xrheed

# Load a RHEED image
rheed_image = xrheed.load_data("rheed_image.raw", plugin="dsnp_arpes_raw")

# Show image with auto-adjusted levels
rheed_image.ri.plot_image(auto_levels=2.0)
plt.show()

# Get intensity profile and plot its origin
profile = rheed_image.ri.get_profile(center=(0, -5), width=40, height=4,
                                     plot_origin=True)

📖 Citation

If you use xRHEED in your research, please cite it:

Kopciuszynski, M. ORCID (2025). xRHEED: An xarray-based toolkit for RHEED image analysis.
GitHub. https://github.com/mkopciuszynski/xrheed
DOI: 10.5281/zenodo.17099751


📚 👉 See the full documentation for tutorials and advanced examples.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

xrheed-1.3.2.tar.gz (27.2 MB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

xrheed-1.3.2-py3-none-any.whl (35.6 kB view details)

Uploaded Python 3

File details

Details for the file xrheed-1.3.2.tar.gz.

File metadata

  • Download URL: xrheed-1.3.2.tar.gz
  • Upload date:
  • Size: 27.2 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for xrheed-1.3.2.tar.gz
Algorithm Hash digest
SHA256 cfb3b220fc8096d6ebd2c0e98886990b4d40d72fa1e13d96482fb2217584e5b4
MD5 59622a50446eaa5ecd8f04a36ddbb739
BLAKE2b-256 aa5da78b3c3560d3d4090cf7090058697f48b1c7a6b8221593b3eb90de2d5bba

See more details on using hashes here.

File details

Details for the file xrheed-1.3.2-py3-none-any.whl.

File metadata

  • Download URL: xrheed-1.3.2-py3-none-any.whl
  • Upload date:
  • Size: 35.6 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for xrheed-1.3.2-py3-none-any.whl
Algorithm Hash digest
SHA256 f5e7de54658a200ec2ac762d75db70ee5ea55d8ec07f9ae8d9a8e2c9a228e73b
MD5 2bd26519177663c9ccb3b8db12725267
BLAKE2b-256 e4f3289ec579c1354186289216cc0e68115a9b59f5f4af25fb8ea5da3e12d7f4

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page