pyOMA-2 1.2.1

Python module for conducting Operational Modal Analysis

pyOMA2

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This is the new and updated version of pyOMA module, a Python module designed for conducting operational modal analysis. With this update, we've transformed pyOMA from a basic collection of functions into a more sophisticated module that fully leverages the capabilities of Python classes.

Key Features & Enhancements:

• Support for single and multi-setup measurements, which includes handling multiple acquisitions with mixed reference and roving sensors.
• Interactive plots for intuitive mode selection, users can now extract desired modes directly from algorithm-generated plots.
• Structure geometry definition, enabling 3D visualization of mode shapes once modal results are obtained.
• Uncertainty estimation for modal properties in Stochastic Subspace Identification (SSI) algorithms.
• Specialized clustering classes for Automatic OMA using SSI, streamlining modal parameter extraction.
• New OMAX (OMA with Exogenous Input) functionality for SSI, expanding the module’s capabilities to handle forced excitation scenarios.

Documentation

You can check the documentation at the following link:

https://pyoma.readthedocs.io/en/main/

Quick start

Install the library with pip:

pip install pyOMA-2

or with conda/mamba:

conda install pyOMA-2

You'll probably need to install tk for the GUI on your system, here some instructions:

Windows:

https://www.pythonguis.com/installation/install-tkinter-windows/

Linux:

https://www.pythonguis.com/installation/install-tkinter-linux/

Mac:

https://www.pythonguis.com/installation/install-tkinter-mac/

Docker (Recommended for isolated environment)

Run pyOMA2 in a container with all dependencies pre-installed - no local Python setup needed, just Docker.

Build the image

docker compose build

Run Jupyter Notebook

docker compose up jupyter

Then open http://localhost:8888 in your browser (no token required for local development).

Docker Limitations:

• Interactive Qt windows (pyvistaqt) not available - use notebook=True for 3D plots and save_gif=True for animations
• Authentication disabled for convenience - only use on trusted networks

Run Python shell

docker compose up pyoma2

Run a command in the container

docker compose run --rm pyoma2 python3 your_script.py

Enter interactive shell

docker compose run --rm pyoma2 /bin/bash

GUI Applications (Optional)

For 3D visualizations and interactive plots, enable X11 forwarding:

Linux:

xhost +local:docker
docker compose up pyoma2

macOS:

# Install XQuartz first: brew install --cask xquartz
xhost +localhost
docker compose up pyoma2

Windows/WSL2:

# Install VcXsrv or Xming, then:
export DISPLAY=$(cat /etc/resolv.conf | grep nameserver | awk '{print $2}'):0
docker compose up pyoma2

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Examples

To see how the module works please take a look at the jupyter notebook provided:

• Example1 - Getting started.ipynb
• Example2 - Real dataset.ipynb
• Example3 - Multisetup PoSER.ipynb
• Example4 - MultiSetup PreGER.ipynb
• Example5 - Clustering for Automatic OMA.ipynb
• Extra - Tips and Tricks 1.ipynb

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Schematic organisation of the module showing inheritance between classes

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How to Cite

If you use pyOMA2 in your research, please cite our JOSS paper:

BibTeX:

@article{Pasca2025,
  doi = {10.21105/joss.07656},
  url = {https://doi.org/10.21105/joss.07656},
  year = {2025},
  publisher = {The Open Journal},
  volume = {10},
  number = {115},
  pages = {7656},
  author = {Pasca, Dag P. and Margoni, Diego Federico},
  title = {pyOMA2: A Python module for conducting operational modal analysis},
  journal = {Journal of Open Source Software}
}

APA:

Pasca, D. P., & Margoni, D. F. (2025). pyOMA2: A Python module for conducting operational modal analysis. Journal of Open Source Software, 10(115), 7656. https://doi.org/10.21105/joss.07656

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