poscidyn 0.1.1

Simulation and Visualization of Nonlinear Oscillator Dynamics in Python

Poscidyn

Simulation and Visualization of Nonlinear Oscillator Dynamics in Python

Poscidyn is a Python toolkit based on JAX, designed to streamline and accelerate common workflows in nonlinear oscillator dynamics. It enables the simulation and visualization of (nonlinear) oscillators using experimentally realistic setups, supporting both time- and frequency-domain analyses.

Features include:

• Built-in models of (nonlinear) oscillators
• Frequency sweeping (forward and backward)
• Everything vmappable

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Installation

pip install poscidyn[gpu]

Requires Python 3.10 or newer.

Documentation

Have a look at our extensive documentation on how to install, use and extend this package: https://rknetemann.github.io/poscidyn/.

Quick example

import poscidyn
import numpy as np

Q, omega_0, alpha, gamma = np.array([100.0]), np.array([1.00]), np.zeros((1,1,1)), np.zeros((1,1,1,1))
gamma[0,0,0,0] = 2.55
modal_forces = np.array([1.0])

driving_frequency = np.linspace(0.9, 1.3, 501)
driving_amplitude = np.linspace(0.1, 1.0, 10)

MODEL = poscidyn.NonlinearOscillator(Q=Q, alpha=alpha, gamma=gamma, omega_0=omega_0)
EXCITOR = poscidyn.OneToneExcitation(driving_frequency, driving_amplitude, modal_forces)

frequency_sweep = poscidyn.frequency_sweep(
    model = MODEL, excitor=EXCITOR,
) 

Credits where they are due

JAX: a Python library for accelerator-oriented array computation and program transformation, designed for high-performance numerical computing and large-scale machine learning.

Diffrax: JAX-based library providing numerical differential equation solvers.

Equinox: your one-stop JAX library, for everything you need that isn't already in core JAX.