.. _mplot3d-lorenz_attractor: mplot3d example code: lorenz_attractor.py ========================================= .. plot:: /home/tcaswell/other_source/matplotlib/doc/mpl_examples/mplot3d/lorenz_attractor.py :: # Plot of the Lorenz Attractor based on Edward Lorenz's 1963 "Deterministic # Nonperiodic Flow" publication. # http://journals.ametsoc.org/doi/abs/10.1175/1520-0469%281963%29020%3C0130%3ADNF%3E2.0.CO%3B2 # # Note: Because this is a simple non-linear ODE, it would be more easily # done using SciPy's ode solver, but this approach depends only # upon NumPy. import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D def lorenz(x, y, z, s=10, r=28, b=2.667) : x_dot = s*(y - x) y_dot = r*x - y - x*z z_dot = x*y - b*z return x_dot, y_dot, z_dot dt = 0.01 stepCnt = 10000 # Need one more for the initial values xs = np.empty((stepCnt + 1,)) ys = np.empty((stepCnt + 1,)) zs = np.empty((stepCnt + 1,)) # Setting initial values xs[0], ys[0], zs[0] = (0., 1., 1.05) # Stepping through "time". for i in range(stepCnt) : # Derivatives of the X, Y, Z state x_dot, y_dot, z_dot = lorenz(xs[i], ys[i], zs[i]) xs[i + 1] = xs[i] + (x_dot * dt) ys[i + 1] = ys[i] + (y_dot * dt) zs[i + 1] = zs[i] + (z_dot * dt) fig = plt.figure() ax = fig.gca(projection='3d') ax.plot(xs, ys, zs) ax.set_xlabel("X Axis") ax.set_ylabel("Y Axis") ax.set_zlabel("Z Axis") ax.set_title("Lorenz Attractor") plt.show() Keywords: python, matplotlib, pylab, example, codex (see :ref:`how-to-search-examples`)