pynamicalsys: A Python toolkit for the analysis of dynamical systems
A fast, flexible, and user-friendly toolkit for analyzing chaotic systems and dynamical behaviors in Python.
Welcome to pynamicalsys’s documentation! This is the official documentation for pynamicalsys, a Python toolkit for the analysis of dynamical systems. Here, you will find everything you need to get started, from installation instructions to detailed API references.
Overview
pynamicalsys is designed to provide a fast, flexible, and user-friendly environment for analyzing nonlinear dynamical systems. It is intended for students, researchers, educators, and enthusiasts who want to explore the world of chaos and dynamical systems. Beyond standard tools like trajectory generation and Lyapunov exponents calculation, pynamicalsys includes advanced features such as
Bifurcation diagrams, Poincaré sections, and stroboscopic maps for analyzing system trajectories.
Symplectic integrators for analyzing Hamiltonian systems.
Linear dependence index for chaos detection.
Covariant Lyapunov vectors and the angles between them.
Recurrence plots and recurrence time statistics.
Chaos indicators based on weighted Birkhoff averages.
Statistical measures of diffusion and transport in dynamical systems.
Computation of periodic orbits, their stability and their manifolds.
Basin metric for quantifying the structure of basins of attraction.
Plot styling for consistent and customizable visualizations.
pynamicalsys is built on top of NumPy and Numba, ensuring high performance and efficiency. Thanks to Numba accelerated computation, pynamicalsys offers speedups up to 130x compared to the original Python implementation of the algorithms. This makes it suitable for large-scale simulations and analyses.