Document a trick for dynamic plotting in Jupyter/iPython

[wangkuiyi]

We tried the following code snippet. It works with try.jupyter.org:

%matplotlib inline
import time
import pylab as pl
from IPython import display
for i in range(10):
    pl.plot(pl.randn(100))
    display.clear_output(wait=True)
    display.display(pl.gcf())
    pl.gcf().clear()
    time.sleep(1.0)

where pl.gcf().clear() clears existing plots.

Documents for users' review: https://ipython.org/ipython-doc/3/api/generated/IPython.display.html

[livc]

I tried to write a code snippet on @wangkuiyi 's basis.
The effect is as follow:

import numpy as np
import time
import pylab as pl
from IPython import display

def plot(x, y):
    pl.ylim(0, 10)
    pl.xlim(0, 10)
    
    pl.plot(x, y)
    pl.xlabel('X')
    pl.ylabel('Error')

    display.clear_output(wait=True)
    display.display(pl.gcf())
    pl.gcf().clear()
    time.sleep(1.0)
    
    
def train(callback):
    x = []
    y = []
    for i in range(11):
        x.append(i)
        y.append(np.random.rand()*10)
        callback(x, y)
        
    
train(plot)