Simple animations Using clear_output

Sometimes you want to clear the output area in the middle of a calculation. This can be useful for doing simple animations. In terminals, there is the carriage-return ('\r') for overwriting a single line, but the notebook frontend can clear the whole output area, not just a single line.

To clear output in the Notebook you can use the clear_output() function. If you are clearing the output every frame of an animation, calling clear_output() will create noticeable flickering. You can use clear_output(wait=True) to add the clear_output call to a queue. When data becomes available to replace the existing output, the clear_output will be called immediately before the new data is added. This avoids the flickering by not rendering the cleared output to the screen.

Simple example

Here we show our progress iterating through a list:



In [1]:

    
import sys
import time



In [2]:

    
from IPython.display import display, clear_output
for i in range(10):
    time.sleep(0.25)
    clear_output(wait=True)
    print(i)
    sys.stdout.flush()

AsyncResult.wait_interactive

The AsyncResult object has a special wait_interactive() method, which prints its progress interactively, so you can watch as your parallel computation completes.

This example assumes you have an IPython cluster running, which you can start from the cluster panel



In [3]:

    
from IPython import parallel
rc = parallel.Client()
view = rc.load_balanced_view()

amr = view.map_async(time.sleep, [0.5]*100)

amr.wait_interactive()









    



 100/100 tasks finished after    6 s
done

Matplotlib example

You can also use clear_output() to clear figures and plots.



In [4]:

    
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt



In [5]:

    
from scipy.special import jn
x = np.linspace(0,5)
f, ax = plt.subplots()
ax.set_title("Bessel functions")

for n in range(1,10):
    time.sleep(1)
    ax.plot(x, jn(x,n))
    clear_output(wait=True)
    display(f)

# close the figure at the end, so we don't get a duplicate
# of the last plot
plt.close()