gwexpy.interop.pyoma_

Interoperate with pyOMA operational modal analysis results.

Interoperability with pyOMA (Operational Modal Analysis).

pyOMA returns results as Python dicts with keys like "Fn" (natural frequencies), "Zeta" (damping ratios), and "Phi" (mode shapes).

References

https://github.com/dagghe/pyOMA

Functions

from_pyoma_results(cls,Β results,Β *[,Β fs])

Convert pyOMA result dict to a GWexpy type.
gwexpy.interop.pyoma_.from_pyoma_results(cls: type, results: dict, *, fs: float | None = None) → Any[source]

Convert pyOMA result dict to a GWexpy type.

Parameters:

  • cls (type) – Target type. Use pandas.DataFrame (or pass the string "DataFrame") for modal parameter summary, or FrequencySeriesMatrix for mode-shape based FRF reconstruction.

  • results (dict) –

    pyOMA result dictionary. Expected keys:

    • "Fn" : ndarray (n_modes,) β€” natural frequencies [Hz]

    • "Zeta" : ndarray (n_modes,) β€” damping ratios

    • "Phi" : ndarray (n_dof, n_modes) β€” mode-shape matrix (optional)

    • "Xi" : alias for "Zeta" in some pyOMA versions

    • "Freq" : alias for "Fn" in some versions

  • fs (float, optional) – Sampling frequency [Hz]. Stored in metadata.

Return type:

pandas.DataFrame or FrequencySeriesMatrix