EMPeaks

EMPeaks is an open-source analysis package that performs fast, automated peak deconvolution and fitting for spectroscopy data such as XPS and Raman by treating spectra as mixture probability distributions and applying Spectrum-Adapted EM/ECM algorithms. It also supports automated background subtraction and multiple peak functions (Gaussian, Lorentzian, Pseudo-Voigt, Doniach–Šunjić, etc.), enabling high-throughput batch analysis of large datasets.

Information

Official site https://pypi.org/project/EMPeaks/
Openness ★★★
License
  • Python version (EMPeaks, PyPI): BSD License
  • R version (EMpeaksR, CRAN): MIT License + LICENSE file
Core Developers
  • Yasunobu Ando (listed as the maintainer of the Python package)
  • Naoka Nagamura (validation with experimental data and organization of usage examples, etc.)
  • (Related) R version: Tarojiro Matsumura (Author/Maintainer of EMpeaksR)
Availability
  • Python version (PyPI): Python 3.8 or later (distribution indicates OS Independent)
  • Typical usage: run from Jupyter Notebook, etc. (operation in virtual environments such as VS Code, pip/pipenv, Anaconda, venv, WSL2, etc. is assumed)
  • (Related) R version (CRAN: EMpeaksR): available in an R environment
Related Papers
  • Naoka Nagamura and Yasunobu Ando, “Introduction to ‘EMPeaks’, high-throughput automated spectrum analysis assisted by machine learning,”
    Vacuum and Surface Science, Vol. 67, No. 10, pp. 500-505 (2024).
    DOI: 10.1380/vss.67.500
  • T. Matsumura, N. Nagamura, S. Akaho, K. Nagata, Y. Ando,
    “Spectrum adapted expectation-maximization algorithm for high-throughput peak shift analysis,”
    Science and Technology of Advanced Materials, 20(1), 733-745 (2019).
    DOI: 10.1080/14686996.2019.1620123
  • T. Matsumura, N. Nagamura, S. Akaho, K. Nagata, Y. Ando,
    “Spectrum adapted expectation-conditional maximization algorithm for extending high–throughput peak separation method in XPS analysis,”
    Science and Technology of Advanced Materials: Methods, 1(1), 45-55 (2021).
    DOI: 10.1080/27660400.2021.1899449
  • T. Matsumura, N. Nagamura, S. Akaho, K. Nagata, Y. Ando,
    “High-throughput XPS spectrum modeling with autonomous background subtraction for 3d5/2 peak mapping of SnS,”
    Science and Technology of Advanced Materials: Methods, 3(1), 2159753 (2023).
    DOI: 10.1080/27660400.2022.2159753
  • T. Matsumura, N. Nagamura, S. Akaho, K. Nagata, Y. Ando,
    “Maximum a posteriori estimation for high-throughput peak fitting in X-ray photoelectron spectroscopy,”
    Science and Technology of Advanced Materials: Methods, 4(1), 2373046 (2024).
    DOI: 10.1080/27660400.2024.2373046
Related Sites