PyOMA2: An Open-Source Python Software for Operational Modal Analysis

In this paper, we introduce the new pyOMA2 Python module, the updated version of pyOMA, an open-source Python module designed for Operational Modal Analysis (OMA). This new iteration transforms pyOMA from a basic function library into a sophisticated module, fully leveraging Python’s class capabilities. pyOMA2 expands its scope to support both single and multi-setup data measurements, effectively managing multiple acquisitions involving a combination of reference and roving sensors. A notable addition is interactive plotting, enabling users to directly select modes for extraction from algorithm-generated plots. Furthermore, a novel feature allows users to define the geometry of the structures under analysis, enhancing the visualization of mode shapes post-analysis. The core functions of these classes have undergone thorough revisions, leading to substantial improvements and optimizations. pyOMA2 not only retains the algorithms found in its predecessor, including Frequency Domain Decomposition (FDD), Enhanced Frequency Domain Decomposition (EFDD), Frequency-Spatial Domain Decomposition (FSDD), and both the covariance-based and data-driven Stochastic Subspace Identification (SSIcov, SSIdat) methods but also introduces the poly-reference Least Square Frequency Domain (pLSCF) method, also known as Polymax. The inclusion of these advanced algorithms, along with the module's improved features, makes pyOMA2 a valuable resource in the field of structural dynamics, providing researchers and practitioners with an effective tool for conducting operational modal analysis.