pynamical

Python package for modeling, simulating, visualizing, and animating discrete nonlinear dynamical systems and chaos

pynamical uses pandas, numpy, and numba for fast simulation, and matplotlib for beautiful visualizations and animations to explore system behavior. Compatible with Python 2+3.

Install:

You can install pynamical with conda:

conda install -c conda-forge pynamical

Or with pip:

pip install pynamical

Documentation:

Available on readthedocs.

Demos/tutorial:

1. Pynamical: quick overview
2. Pynamical: the logistic model and bifurcation diagrams
3. Pynamical: 2D and 3D phase diagrams
4. Pynamical: static and animated cobweb plots
5. Pynamical: animated 3D phase diagrams
6. Pynamical: demonstrating other models

Quick walkthrough:

First load pynamical. Then simulate some model and visualize its bifurcation diagram in just 2 lines of code:

from pynamical import logistic_map, simulate, bifurcation_plot
pops = simulate(model=logistic_map, num_gens=100, rate_min=0, rate_max=4, num_rates=1000, num_discard=100)
bifurcation_plot(pops)

Zoom into a slice of this bifurcation diagram to see its fractal structure:

pops = simulate(model=logistic_map, num_gens=100, rate_min=3.7, rate_max=3.9, num_rates=1000, num_discard=100)
bifurcation_plot(pops, xmin=3.7, xmax=3.9)

Plot a two-dimensional phase diagram of the logistic map:

from pynamical import phase_diagram
pops = simulate(model=logistic_map, num_gens=4000, rate_min=3.6, rate_max=4.0, num_rates=50, num_discard=100)
phase_diagram(pops, xmin=0.25, xmax=0.75, ymin=0.8, ymax=1.01, size=7, color='viridis')

Or a three-dimensional phase diagram of the cubic map:

from pynamical import cubic_map, phase_diagram_3d
pops = simulate(model=cubic_map, num_gens=3000, rate_min=3.5, num_rates=30, num_discard=100)
phase_diagram_3d(pops, xmin=-1, xmax=1, ymin=-1, ymax=1, zmin=-1, zmax=1, alpha=0.2, color='viridis', azim=330)

Animate the 3D phase diagram of the logistic map to reveal the strange attractor's structure:

Animate a cobweb plot of the logistic map's parameter space to explore sensitivity and behavior: