openmodalpy 0.2.0

OpenModalPy: Python tools for POD, MPOD, DMD, SPOD, PSD-POD, BSMD, and ST-POD on spatiotemporal data

OpenModalPy

Modal decomposition of spatiotemporal data in Python. Pure NumPy/SciPy — no external solver dependencies.

Methods: POD · mPOD · PSD-POD · SPOD · ST-POD · DMD (LS/TLS) · HODMD (LS/TLS) · BSMD

Installation

uv add openmodalpy

The installed package is imported as openmodalpy.

Or as a standalone CLI:

uv tool install openmodalpy

For modern 3D slice/isosurface plotting:

uv add "openmodalpy[viz3d]"

FFT Backend

FFT dispatch is handled by fftkit, installed automatically. It probes the available backends and picks the fastest one, falling back to SciPy when nothing else is present — so no configuration is needed.

To pin a backend explicitly:

export FFTKIT_BACKEND=mkl      # or scipy, numpy, cupy, accelerate

from openmodalpy.core.config import FFT_BACKEND
print(FFT_BACKEND)   # the backend actually in use

Accelerator support comes from the corresponding extra:

uv add "openmodalpy[mkl]"   # Intel MKL
uv add "openmodalpy[gpu]"   # CuPy / PyTorch

The legacy PYMODAL_FFT_BACKEND variable is still honoured as a fallback, but FFTKIT_BACKEND is the supported name.

Quick Start

from openmodalpy import PODAnalyzer, DMDAnalyzer, SPODAnalyzer

pod = PODAnalyzer(file_path="data.mat", n_modes_save=10)
pod.run_analysis()

dmd = DMDAnalyzer(file_path="data.mat", n_modes_save=10)
dmd.load_and_preprocess()
dmd.perform_dmd(method="ls")

spod = SPODAnalyzer(file_path="data.mat", nfft=256, overlap=0.5)
spod.run_analysis()

Configuration-Driven Workflow

A single JSONC file runs multiple methods on one dataset:

{
  "case": {
    "name": "my_case",
    "data": { "kind": "file", "path": "data.mat" },
    "n_modes_save": 10, "nfft": 128, "overlap": 0.5
  },
  "runs": [
    { "id": "pod",  "method": "pod" },
    { "id": "spod", "method": "spod" },
    { "id": "dmd",  "method": "dmd", "params": { "method": "ls" } },
    { "id": "hodmd","method": "hodmd", "params": { "delays": 4 } },
    { "id": "bsmd", "method": "bsmd" }
  ]
}

openmodalpy run --config analysis.jsonc

CLI

openmodalpy analyze pod --config case.jsonc         # one method
openmodalpy run --config suite.jsonc                # full suite
openmodalpy run --config suite.jsonc --dry-run      # preview
openmodalpy methods list                            # supported methods
openmodalpy examples list                           # bundled examples
openmodalpy results inspect output.hdf5             # inspect result

Bundled Example Configs

The public package ships only self-contained generator-backed example configs:

• double_gyre.jsonc
• cylinder_wake.jsonc
• taylor_green.jsonc
• run_benchmarks.jsonc

These configs do not depend on repository-local benchmark datasets.

Supported Methods

Method	Class	What it extracts
POD	variance-optimal	energy-ranked spatial modes
mPOD	variance-optimal	scale-separated modes
PSD-POD	variance-optimal	broadband spectral modes
SPOD	variance-optimal	frequency-local modes
ST-POD	variance-optimal	space-time structures (delay embedding)
DMD (LS)	evolution-fit	modes with frequency and growth rate
DMD (TLS)	evolution-fit	de-biased DMD for noisy data
HODMD	evolution-fit	delay-embedded DMD
TLS-HODMD	evolution-fit	de-biased delay-embedded DMD
BSMD	triadic interaction	nonlinear triad structures

The BSMD implementation follows Schmidt (2020) and was inspired by the reference MATLAB implementation.

Data Format

OpenModalPy auto-detects .mat and .npz files:

{
    "q": np.ndarray,   # (Ns, Nspace) — snapshots × spatial points
    "dt": float,       # time step
    "Nx": int,         # grid points in x
    "Ny": int,         # grid points in y
    "x": np.ndarray,   # x-coordinates
    "y": np.ndarray,   # y-coordinates
}

Custom loaders:

def my_loader(path):
    return {"q": data, "dt": 0.01, "Nx": 100, "Ny": 50, "x": x, "y": y}

pod = PODAnalyzer(file_path="ignored", data_loader=my_loader)

References

Method	Key reference
POD	Lumley (1967); Sirovich (1987)
mPOD	Mendez et al. (2019)
SPOD	Towne, Schmidt & Colonius (2018)
DMD	Schmid (2010); Tu et al. (2014)
TLS-DMD	Hemati et al. (2017)
HODMD	Le Clainche & Vega (2017)
BSMD	Schmidt (2020)

License

MIT — see LICENSE