My chaotic attractor. Made with #python #numpy #matplotlib and... #marimo (and also #wigglystuff)show more

Made with #python #numpy #matplotlib and #marimo (and also #wigglystuff)

A simulation of the dielectric breakdown model described in... Fractal Dimension of Dielectric Breakdown L. Niemeyer, L. Pietronero, and H. J. Wiesmann Phys. Rev. Lett. 52, 1033 – Published 19 March 1984 Made with #python, #numpy and #matplotlib.show more

"I used to feel guilty in Cambridge that I... spent all day playing games, while I was supposed to be doing mathematics. Then I realized that playing games is math." John H. Conway Made with #python #numpy #matplotlibshow more

A classic Monte Carlo simulation, it takes me right... back to my student days. 16 cars of equal length park at random along a street 20 cars long. #builtWithMarimo #python #numpy #matplotlib Thanks Claude Code for drawing nice cars in matplotlib 😀show more

There's a sequence to it. First you fall for... marimo , and the reactive DAG that makes a notebook feel alive. Then for wigglystuff, Vincent D. Warmerdam anywidgets, dropping into a cell and starting a quiet conversation with everything downstream. Loving #marimo notebook!show more

Math meets music, capturing chaos in 3D. The Aizawa... chaotic attractor, coded with Grok 4.20. Sound on. 🔊show more

3D embedding of my polynomial roots #mathart. Made with... #python #apple #MLX and #pyGFX. Original 2d version below 👇show more

Mathematics. A close look at the infinite swirling of... the Rössler Attractor, which exhibits chaotic dynamics and fractal properties. Like a cosmic taffy-pulling machine. By Marcus Volz, Used with permission.show more

Beautiful WebGPU render made with ThreeJS using a 3D... attractor formulashow more

The Thomas attractor is a mathematical concept in dynamical... systems theory representing a type of chaotic space phase that exhibits cyclic symmetry. It was named after René Thomas, the mathematician who introduced it in 1979. The system is strictly related to the Lorenz attractor another famous chaotic evolutionary space manifold. The 3D Thomas attractor is represented by the combination of three differential equations where the symmetry is invariant under rotations of 120° (or 2π/3 radians) around the x y z axis which are the space coordinates, plus μ (mu) that is a parameter controlling the behavior. The attractor is characterized by a chaotic tendency and it’s sensitive to initial conditions, meaning that as μ increases, the motion undergoes period-doubling bifurcations leading to chaos. A good visualization of this spacetime manifold is a twisted, spiral-shaped structure with 3-fold symmetry where each spiral arm is symmetric with respect to the central axis, like the one displayed below. The chaotic evolution of this attractor demonstrates the geometric beauty and complexity of unpredictable systems.show more

visualizing the lorenz attractor with claude code, threejs, webgl... shaders formula and parameters shownshow more

Just released my first Blender add-on / Python package... ever. ✨ Introducing BlenderSpike ✨ Bring simulation results from NEURON into Blender to create stunning biophysically realistic animations of neuron activity using matplotlib colormaps 🌈show more

My happiness is immeasurable and my day is made... also ignore my mouse going wildshow more

Evidence that chaos can be beautiful. Built with Three.js... and GLSL shaders, this integrates dx/dt=sin(y)−b x, dy/dt=sin(z)−b y, dz/dt=sin(x)−b z. At b≈0.19 the Thomas attractor settles into a stable yet chaotic 3D geometry. 🔊show more

Kinetic energy dissipation rate in two-dimensional turbulence. Logarithmic contours.... Plots with Matplotlib Data available here: #Python script to generate plots: import numpy as np import matplotlib as mpl import matplotlib.colors as colors import matplotlib.pyplot as plt from netCDF4 import Dataset f1=Dataset(' mode='r') dissipation = f1.variables['dissipation'][:,:,:] x = f1.variables['x'][:] y = f1.variables['y'][:] n = dissipation.shape for k in range(0, n[0]): fig, ax = plt.subplots(1,1, figsize=(8,8)) cs1 = ax.contourf(x, y, dissipation[k,:,:], np.logspace(-5,1,101), cmap='Blues_r', extend='both', norm=colors.LogNorm()) ax.set_aspect('equal') ax.xaxis.set_ticks(range(0,7)) ax.yaxis.set_ticks(range(0,7)) ax.set_xlabel(r'$x$') ax.set_ylabel(r'$y$') fname = 'frame.' + str(k) + '.png' plt.savefig(fname, dpi=200, pad_inches = 0.1) print(fname) plt.close()show more

...And that means it can also be pulled into... the web and made interactive with Threejs ( play with it yourself atshow more