ntsa — nonlinear time-series analysis for dynamical-system models
Characterizes the dynamical regime of a model from a single long trajectory: delay embedding (optimal lag + false nearest neighbours), Lyapunov exponents, regime classification (fixed point / limit cycle period-k / frequency-locked / quasiperiodic / chaotic), and a per-case diagnostic figure — one row of 8 panels [time series | PSD | 3-D delay portrait | first-return map of local maxima | plane-crossing Poincaré section | recurrence plot | 3-D MDS | Lyapunov spectrum] — Key Reference: Kantz & Schreiber, Nonlinear Time Series Analysis (2004).
Documentation: andreanovoa.github.io/ntsa
| Tutorial: tutorial_ntsa.ipynb
Example:
Install
pip install ntsa # once released; until then (dynamodels is not on PyPI yet either):
pip install "dynamodels @ git+https://github.com/andreanovoa/dynamodels" \
"ntsa @ git+https://github.com/andreanovoa/ntsa"
Quickstart
from dynamodels.physical import Lorenz63
from ntsa import characterize as chz
chz.characterize([Lorenz63()], pdf_name='figs/l63.pdf')
python -m ntsa.characterize # 4-case demo -> figs/ntsa_defaults.pdf (+ .png)
Works with any model implementing the
model protocol —
dynamodels is the reference
implementation. Part of the same ecosystem as
romda (real-time
bias-aware data assimilation).
Development
pip install -e ".[dev]"
python -m pytest tests/
ruff check ntsa/ tests/
Releases publish to PyPI via trusted publishing on version tags; docs deploy to
GitHub Pages on push to main (see .github/workflows/).
License
MIT
1 maintainer
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