import numpy as np from matplotlib import pyplot as plt from matplotlib import animation from JSAnimation import IPython_display def basic_animation(frames=100, interval=30): """Plot a basic sine wave with oscillating amplitude""" fig = plt.figure() ax = plt.axes(xlim=(0, 10), ylim=(-2, 2)) line, = ax.plot([], [], lw=2) x = np.linspace(0, 10, 1000) def init(): line.set_data([], []) return line, def animate(i): y = np.cos(i * 0.02 * np.pi) * np.sin(x - i * 0.02 * np.pi) line.set_data(x, y) return line, return animation.FuncAnimation(fig, animate, init_func=init, frames=frames, interval=interval) def lorenz_animation(N_trajectories=20, rseed=1, frames=200, interval=30): """Plot a 3D visualization of the dynamics of the Lorenz system""" from scipy import integrate from mpl_toolkits.mplot3d import Axes3D from matplotlib.colors import cnames def lorentz_deriv(coords, t0, sigma=10., beta=8./3, rho=28.0): """Compute the time-derivative of a Lorentz system.""" x, y, z = coords return [sigma * (y - x), x * (rho - z) - y, x * y - beta * z] # Choose random starting points, uniformly distributed from -15 to 15 np.random.seed(rseed) x0 = -15 + 30 * np.random.random((N_trajectories, 3)) # Solve for the trajectories t = np.linspace(0, 2, 500) x_t = np.asarray([integrate.odeint(lorentz_deriv, x0i, t) for x0i in x0]) # Set up figure & 3D axis for animation fig = plt.figure() ax = fig.add_axes([0, 0, 1, 1], projection='3d') ax.axis('off') # choose a different color for each trajectory colors = plt.cm.jet(np.linspace(0, 1, N_trajectories)) # set up lines and points lines = sum([ax.plot([], [], [], '-', c=c) for c in colors], []) pts = sum([ax.plot([], [], [], 'o', c=c, ms=4) for c in colors], []) # prepare the axes limits ax.set_xlim((-25, 25)) ax.set_ylim((-35, 35)) ax.set_zlim((5, 55)) # set point-of-view: specified by (altitude degrees, azimuth degrees) ax.view_init(30, 0) # initialization function: plot the background of each frame def init(): for line, pt in zip(lines, pts): line.set_data([], []) line.set_3d_properties([]) pt.set_data([], []) pt.set_3d_properties([]) return lines + pts # animation function: called sequentially def animate(i): # we'll step two time-steps per frame. This leads to nice results. i = (2 * i) % x_t.shape[1] for line, pt, xi in zip(lines, pts, x_t): x, y, z = xi[:i + 1].T line.set_data(x, y) line.set_3d_properties(z) pt.set_data(x[-1:], y[-1:]) pt.set_3d_properties(z[-1:]) ax.view_init(30, 0.3 * i) fig.canvas.draw() return lines + pts return animation.FuncAnimation(fig, animate, init_func=init, frames=frames, interval=interval)