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DSPkit

A lightweight DSP toolkit for structural health monitoring.

DSPkit provides a clean, function-based API for the signal-processing tasks that appear daily in vibration analysis and structural health monitoring (SHM): spectral estimation, filtering, time-frequency analysis, and adaptive decomposition. It depends only on NumPy, SciPy, and Matplotlib — no heavy ML frameworks or extra wavelet libraries required.

Modules at a glance

Module What it does
spectral FFT amplitude spectrum, Welch PSD / CSD, coherence, autocorrelation
filters Zero-phase Butterworth filters (LP / HP / BP / BS / notch) and decimation
utils Detrend, RMS, peak, crest factor, numerical integration and differentiation
timefreq STFT, CWT scalogram (analytic Morlet), Wigner-Ville, Smoothed Pseudo WVD
instantaneous Hilbert transform → envelope, instantaneous phase & frequency
emd Empirical Mode Decomposition, Hilbert-Huang Transform, marginal spectrum
plots Thin matplotlib wrappers — every function accepts an ax argument

Design philosophy

  • Function-based. Every operation is a plain function f(x, fs, ...) → result. No classes to instantiate, no state to manage.
  • Engineering defaults. Parameters are chosen for typical SHM signals (1 kHz sampling, second-order structural systems). Override anything you need.
  • Composable. All plot functions accept an ax parameter so they embed naturally into any multi-panel figure layout.
  • Minimal dependencies. NumPy + SciPy + Matplotlib. The CWT uses a self-implemented FFT-based Morlet transform — no pywavelets required.

Spectral analysis Time-frequency analysis

Quick example

import dspkit as dsp
from dspkit._testing import generate_2dof, natural_frequencies_2dof

fs = 1000.0
t, a1, a2 = generate_2dof(duration=60.0, fs=fs, noise_std=1.0,
                           output="acceleration", seed=42)

fn1, fn2 = natural_frequencies_2dof()

# Welch PSD
freqs, Pxx = dsp.psd(a1, fs, nperseg=4096)
dsp.plot_psd(freqs, Pxx, xlim=(0, 80), title="Welch PSD")

# Time-frequency: CWT scalogram
import numpy as np
analysis_freqs = np.geomspace(2.0, fs / 4, num=100)
f, t_cwt, W = dsp.cwt_scalogram(a1[:2048], fs, freqs=analysis_freqs)
dsp.plot_scalogram(f, t_cwt, W, title="CWT Scalogram")