pyOMA2: A Python module for conducting operational modal analysis
pyOMA2: A Python module for conducting operational modal analysis - Published in JOSS (2025)
Science Score: 93.0%
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✓codemeta.json file
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✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 1 DOI reference(s) in JOSS metadata -
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○Institutional organization owner
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✓JOSS paper metadata
Published in Journal of Open Source Software
Keywords
Repository
Python module for conducting operational modal analysis
Basic Info
Statistics
- Stars: 38
- Watchers: 7
- Forks: 17
- Open Issues: 7
- Releases: 20
Topics
Metadata Files
README.md
pyOMA2
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:
shell
pip install pyOMA-2
or with conda/mamba:
shell
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
shell
docker compose build
Run Jupyter Notebook
shell
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 - usenotebook=Truefor 3D plots andsave_gif=Truefor animations - Authentication disabled for convenience - only use on trusted networks
Run Python shell
shell
docker compose up pyoma2
Run a command in the container
shell
docker compose run --rm pyoma2 python3 your_script.py
Enter interactive shell
shell
docker compose run --rm pyoma2 /bin/bash
GUI Applications (Optional)
For 3D visualizations and interactive plots, enable X11 forwarding:
Linux:
shell
xhost +local:docker
docker compose up pyoma2
macOS: ```shell
Install XQuartz first: brew install --cask xquartz
xhost +localhost docker compose up pyoma2 ```
Windows/WSL2: ```shell
Install VcXsrv or Xming, then:
export DISPLAY=$(cat /etc/resolv.conf | grep nameserver | awk '{print $2}'):0 docker compose up pyoma2 ```
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
Schematic organisation of the module showing inheritance between classes

Owner
- Login: dagghe
- Kind: user
- Repositories: 1
- Profile: https://github.com/dagghe
JOSS Publication
pyOMA2: A Python module for conducting operational modal analysis
Authors
Politecnico di Torino, Italy
Tags
operational modal analysis dynamics of structures system identification ambient vibrationsGitHub Events
Total
- Create event: 13
- Release event: 4
- Issues event: 21
- Watch event: 22
- Delete event: 6
- Issue comment event: 55
- Push event: 69
- Pull request review event: 1
- Pull request event: 11
- Fork event: 12
Last Year
- Create event: 13
- Release event: 4
- Issues event: 17
- Watch event: 17
- Delete event: 6
- Issue comment event: 53
- Push event: 65
- Pull request review event: 1
- Pull request event: 11
- Fork event: 12
Issues and Pull Requests
Last synced: 3 months ago
All Time
- Total issues: 19
- Total pull requests: 16
- Average time to close issues: about 2 months
- Average time to close pull requests: 6 days
- Total issue authors: 10
- Total pull request authors: 9
- Average comments per issue: 2.05
- Average comments per pull request: 0.44
- Merged pull requests: 9
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 8
- Pull requests: 8
- Average time to close issues: about 1 month
- Average time to close pull requests: 13 days
- Issue authors: 4
- Pull request authors: 7
- Average comments per issue: 3.0
- Average comments per pull request: 0.38
- Merged pull requests: 3
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- Ayubirad (5)
- Nitnelav (5)
- SwarupMahato (2)
- dfm88 (1)
- DavideMoretti (1)
- e-dub (1)
- MArnaud (1)
- keltouny (1)
- dimatcherniak (1)
- wweijtje (1)
Pull Request Authors
- dfm88 (6)
- akaszynski (2)
- riegerflo (2)
- dagghe (1)
- Ayubirad (1)
- Nitnelav (1)
- keltouny (1)
- alexandreBerezin (1)
- faroit (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
-
Total downloads:
- pypi 311 last-month
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 20
- Total maintainers: 2
pypi.org: pyoma-2
Python module for conducting Operational Modal Analysis
- Homepage: https://github.com/dagghe/pyOMA2
- Documentation: https://pyoma.readthedocs.io/en/main/
- License: MIT
-
Latest release: 1.2.0
published 6 months ago
Rankings
Maintainers (2)
Dependencies
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- pydantic-core ==2.23.3
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- pyqt5-sip ==12.15.0
- pytest ==8.3.3
- pytest-cov ==5.0.0
- python-dateutil ==2.9.0.post0
- python-json-logger ==2.0.7
- pytz ==2024.2
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- qtpy ==2.4.1
- referencing ==0.35.1
- requests ==2.32.3
- rfc3339-validator ==0.1.4
- rfc3986-validator ==0.1.1
- rich ==13.8.1
- rpds-py ==0.20.0
- scipy ==1.10.1
- scooby ==0.10.0
- send2trash ==1.8.3
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- six ==1.16.0
- sniffio ==1.3.1
- snowballstemmer ==2.2.0
- soupsieve ==2.6
- sphinx ==7.1.2
- sphinx-rtd-theme ==2.0.0
- sphinxcontrib-applehelp ==1.0.4
- sphinxcontrib-devhelp ==1.0.2
- sphinxcontrib-htmlhelp ==2.0.1
- sphinxcontrib-jquery ==4.1
- sphinxcontrib-jsmath ==1.0.1
- sphinxcontrib-qthelp ==1.0.3
- sphinxcontrib-serializinghtml ==1.1.5
- stack-data ==0.6.3
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- tinycss2 ==1.3.0
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- tornado ==6.4.1
- tox ==4.18.1
- tqdm ==4.66.5
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- trame-client ==3.2.5
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- trame-vuetify ==2.7.1
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- virtualenv ==20.26.4
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- websocket-client ==1.8.0
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- yarl ==1.11.1
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- aiohappyeyeballs ==2.4.0
- aiohttp ==3.10.5
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- arrow ==1.3.0
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- attrs ==24.2.0
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- beautifulsoup4 ==4.12.3
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- charset-normalizer ==3.3.2
- cmocean ==4.0.3
- colorama ==0.4.6
- colorcet ==3.1.0
- comm ==0.2.2
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- coverage ==7.6.1
- cycler ==0.12.1
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- fastjsonschema ==2.20.0
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- fonttools ==4.53.1
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- h11 ==0.14.0
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- httpx ==0.27.2
- identify ==2.6.0
- idna ==3.8
- imageio ==2.35.1
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- iniconfig ==2.0.0
- ipdb ==0.13.13
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- ipython ==8.18.1
- ipywidgets ==8.1.5
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- packaging ==24.1
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- pandoc ==2.4
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- parso ==0.8.4
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- rfc3986-validator ==0.1.1
- rich ==13.8.1
- rpds-py ==0.20.0
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- scooby ==0.10.0
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- snowballstemmer ==2.2.0
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- sphinxcontrib-htmlhelp ==2.1.0
- sphinxcontrib-jquery ==4.1
- sphinxcontrib-jsmath ==1.0.1
- sphinxcontrib-qthelp ==2.0.0
- sphinxcontrib-serializinghtml ==2.0.0
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