Skip to main content

Python module for conducting Operational Modal Analysis

Project description

pyOMA2

pyoma2_logo_v2_COMPACT

python python python python

pre-commit

Code style: black


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.

The module now supports analysis of both single and multi-setup data measurements, which includes handling multiple acquisitions with a mix of reference and roving sensors. We've also introduced interactive plots, allowing users to select desired modes for extraction directly from the plots generated by the algorithms. Additionally, a new feature enables users to define the geometry of the structures being tested, facilitating the visualization of mode shapes after modal results are obtained. The underlying functions of these classes have been rigorously revised, resulting in significant enhancements and optimizations.

Please note that this is still an alpha release, and we are continuously refining the docstrings, documentation, and other aspects of the module. However, we have provided three working examples that demonstrate the module's capabilities: Example1_SingleSetup.ipynb (for single setup), Example2_MultiSetupPoSER.ipynb (for multi setups using the Post-Single Estimation Rescaling method), and Example3_MultiSetupPreGER.ipynb (for multi setups using the Pre-Global Estimation Rescaling method).

Documentation

You can check the documentation at the following link:

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

Quick start

Install the library

pip 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/


Examples

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


Schematic organisation of the module showing inheritance between classes.

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

pyoma_2-0.3.2.tar.gz (38.7 MB view details)

Uploaded Source

Built Distribution

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

pyoma_2-0.3.2-py3-none-any.whl (38.7 MB view details)

Uploaded Python 3

File details

Details for the file pyoma_2-0.3.2.tar.gz.

File metadata

  • Download URL: pyoma_2-0.3.2.tar.gz
  • Upload date:
  • Size: 38.7 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.0.0 CPython/3.9.18

File hashes

Hashes for pyoma_2-0.3.2.tar.gz
Algorithm Hash digest
SHA256 35a0be5aa8772704cd8b3ce69bf0112b44320b9c17806b52578d3ba34fdd42db
MD5 1aa8104e1c1ce8411066a540b28fc2cd
BLAKE2b-256 e6bc388515534fa06bca37206b613e18b073ea50b638549b94006b9b822414ad

See more details on using hashes here.

File details

Details for the file pyoma_2-0.3.2-py3-none-any.whl.

File metadata

  • Download URL: pyoma_2-0.3.2-py3-none-any.whl
  • Upload date:
  • Size: 38.7 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.0.0 CPython/3.9.18

File hashes

Hashes for pyoma_2-0.3.2-py3-none-any.whl
Algorithm Hash digest
SHA256 8e99ea47272a49b9c03c38001870d35e36af20ca3c65c4c76b0064acd244b67f
MD5 f80fc3e21d0959f5309983ac2136e572
BLAKE2b-256 5af01c103a5e32e3e51cc2bb503cb01669f75453ad1bfff8fc4189c73515ef58

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