Interactive matplotlib using ipywidgets

Question

I want to implement an interactive plot using Matplotlib and ipywidgets in IPython (python3). So, how I can do this efficiently (change smoothly without delay)?

And another question is why this code works?!

from ipywidgets import *
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

x = np.linspace(0, 2 * np.pi)

def update(w = 1.0):
    fig = plt.figure()
    ax = fig.add_subplot(1, 1, 1)
    ax.plot(x, np.sin(w * x))

    fig.canvas.draw()

interact(update);

But, this doesn't work?!

from ipywidgets import *
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

x = np.linspace(0, 2 * np.pi)
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
line, = ax.plot(x, np.sin(x))

def update(w = 1.0):
    line.set_ydata(np.sin(w * x))
    fig.canvas.draw()

interact(update);

Solution

The second approach is the right one for the notebook backend

%matplotlib notebook

Or with ipympl.

However, it won't work with the inline backend which does not update the plot.

Comments

[mdornfe1]

Did you ever find a way to get the second example to work? I have the same problem right now.

[tfv]

For me, the second approach does not work for the notebook backend. No graph is shown at all.

[Quant]

I recommend using ipympl (aka jupyter-matplotlib), which is the notebook backend split into a separate package. The reason for the split is that release cycles of Jupyter are much faster than matplotlib. ipympl is separated from matplotlib to be able to track this better.

[jmborr]

Also, fig.show() is necessary in order to show the canvas.