pyDySP: Dynamic Signal Processing in Python

pyDySP is a lightweight Python package for dynamic signal processing of experimental data, built around two core classes:

  • Channel – a single time-history with rich metadata and lazy processing

  • Test – a collection of Channels with batch tools, spectra, transfer functions, and plotting helpers

It is developed for laboratory and field measurements (shaking-table tests, soil–structure interaction experiments, structural dynamics, etc.) but is general enough for any time-series workflow.

Features

  • Drift correction, filtering, baseline correction and trimming (non-destructive, lazy processing)

  • Fourier and Welch spectra with peak detection helpers

  • Arias intensity and significant-duration windows

  • Elastic response spectra (Newmark-beta method)

  • Cross-spectra, coherence, transfer functions and time delays

  • Batch processing on multi-channel tests (drift, filter, baseline, trim)

  • Simple channel “health” diagnostics

  • SoFSI/EQUALS .mat and wide-format .csv I/O

  • Publication-ready plotting utilities for notebooks and reports

Getting started

If you are new to pyDySP, the recommended reading order is:

  1. Installation – how to install pyDySP and set up an environment.

  2. Quick Start – short, end-to-end examples with real signals.

  3. API Reference – API reference for Channel, Test and helpers.

Documentation: