modewise
Data-adaptive signal decomposition for multichannel recordings (fMRI first; EEG, MEG and fNIRS by design), with a Rust core, this Python package and a command line.
- Decompositions: MVMD, noise-assisted MVMD, VMD, VLMD, EMD, EEMD, CEEMDAN, MEMD and the MODWT multiresolution analysis.
- Analysis: spectra and band amplitude (ALFF), Hilbert and Morlet time–frequency maps, functional connectivity, epochs with an explicit haemodynamic lag, regions from atlases, NIfTI parcellation, and permutation statistics across a cohort.
- Pipelines in TOML or Python run over whole datasets, cached by content and recorded.
import numpy as np
import modewise
t = np.arange(600) / 1.25
x = np.vstack([np.sin(2 * np.pi * 0.02 * t) + np.sin(2 * np.pi * 0.2 * t)])
result = modewise.mvmd(x, 1.25, num_modes=2, alpha=2000.0, init="linear")
print(result.centre_frequencies_hz)
Documentation: https://ipeglin.github.io/modewise/ · Source: https://github.com/ipeglin/modewise · Licence: BSD-3-Clause.
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Release files for modewise 0.2.0
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Source distribution (sdist)
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| modewise-0.2.0.tar.gz | 1.5 MB | Details |
Built distributions (wheels)
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| modewise-0.2.0-cp310-abi3-win_amd64.whl | CPython 3.10 | abi3 | Windows x86-64 | Details |
| modewise-0.2.0-cp310-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl | CPython 3.10 | abi3 | Linux glibc 2.17+ x86-64 | Details |
| modewise-0.2.0-cp310-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl | CPython 3.10 | abi3 | Linux glibc 2.17+ ARM64 | Details |
| modewise-0.2.0-cp310-abi3-macosx_11_0_arm64.whl | CPython 3.10 | abi3 | macOS 11.0+ ARM64 | Details |
| modewise-0.2.0-cp310-abi3-macosx_10_12_x86_64.whl | CPython 3.10 | abi3 | macOS 10.12+ x86-64 | Details |
Total release size: 31.5 MB
Release files / modewise-0.2.0.tar.gz
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