What is the currently correct way to dynamically update plots in Jupyter/iPython?

Here is an example that updates a plot in a loop. It updates the data in the figure and does not redraw the whole figure every time. It does block execution, though if you’re interested in running a finite set of simulations and saving the results somewhere, it may not be a problem for you.

%matplotlib notebook

import numpy as np
import matplotlib.pyplot as plt
import time

def pltsin(ax, colors=['b']):
    x = np.linspace(0,1,100)
    if ax.lines:
        for line in ax.lines:
            line.set_xdata(x)
            y = np.random.random(size=(100,1))
            line.set_ydata(y)
    else:
        for color in colors:
            y = np.random.random(size=(100,1))
            ax.plot(x, y, color)
    fig.canvas.draw()

fig,ax = plt.subplots(1,1)
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_xlim(0,1)
ax.set_ylim(0,1)
for f in range(5):
    pltsin(ax, ['b', 'r'])
    time.sleep(1)

I put this up on nbviewer here.

There is an IPython Widget version of nbagg that is currently a work in progress at the Matplotlib repository. When that is available, that will probably be the best way to use nbagg.

EDIT: updated to show multiple plots