%pylab

fig, ax = plt.subplots()
x = np.linspace(0, 10, 1000)
lines = ax.plot(x, np.sin(x), lw=2)

for i in range(100):
    lines[0].set_ydata(np.sin(x + 0.1 * i))
    fig.canvas.draw()

import matplotlib.pyplot as plt
import numpy as np

# First set up the figure, the axis, and the plot element we want to animate
fig = plt.figure()
ax = plt.axes(xlim=(0, 2), ylim=(-2, 2))
line, = ax.plot([], [], lw=2)

# initialization function: plot the background of each frame
def init():
    line.set_data([], [])
    return line,

# animation function.  This is called sequentially
x = np.linspace(0, 2, 1000)

def animate(i):
    y = np.sin(2 * np.pi * (x - 0.01 * i))
    line.set_data(x, y)
    return line,

from matplotlib import animation

# call the animator.  blit=True means only re-draw the parts that have changed.
anim = animation.FuncAnimation(fig, animate, init_func=init,
                               frames=200, interval=20, blit=True)

anim.save('basic_animation.mp4', fps=30)

import numpy as np
from scipy import integrate

# define the lorenz derivatives
def lorenz_deriv((x, y, z), t0, sigma=10., beta=8./3, rho=28.0):
    """Compute the time-derivative of a Lorenz system."""
    return [sigma * (y - x), x * (rho - z) - y, x * y - beta * z]

# Choose random starting points, uniformly distributed from -15 to 15
N_trajectories = 20
np.random.seed(1)
x0 = -15 + 30 * np.random.random((N_trajectories, 3))

# Solve for the trajectories
t = np.linspace(0, 4, 1000)
x_t = np.asarray([integrate.odeint(lorenz_deriv, x0i, t)
                  for x0i in x0])

from mpl_toolkits.mplot3d import Axes3D

fig = plt.figure()
ax = fig.add_axes([0, 0, 1, 1], projection='3d')

# choose a different color for each trajectory
colors = plt.cm.jet(np.linspace(0, 1, N_trajectories))

for i in range(N_trajectories):
    x, y, z = x_t[i].T
    ax.plot(x, y, z, c=colors[i])

from mpl_toolkits.mplot3d import Axes3D

# 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 = []
pts = []
for c in colors:
    lines += ax.plot([], [], [], '-', c=c)
    pts += ax.plot([], [], [], 'o', c=c)

# 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.  This will be called sequentially with the frame number
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].T
        line.set_data(x, y)
        line.set_3d_properties(z)

        pt.set_data(x[-1:], y[-1:])
        pt.set_3d_properties(z[-1:])

    # rotate the point of view
    ax.view_init(30, 0.3 * i)
    fig.canvas.draw()
    return lines + pts

# instantiate the animator.
anim = animation.FuncAnimation(fig, animate, init_func=init,
                               frames=500, interval=30, blit=True)

# Save as mp4. This requires mplayer or ffmpeg to be installed
#anim.save('lorenz_attractor.mp4', fps=15, extra_args=['-vcodec', 'libx264'])

%run ../examples/double_pendulum.py

%run ../examples/particle_box.py

%run ../examples/animate_schrodinger.py