How to dynamically update a plot in a loop in Ipython notebook (within one cell)

Question

Environment: Python 2.7, matplotlib 1.3, IPython notebook 1.1, linux, chrome. The code is in one single input cell, using --pylab=inline

I want to use I

Answer 1

Try to add show() or gcf().show() after the plot() function. These will force the current figure to update (gcf() returns a reference for the current figure).

Answer 2

A couple of improvement's on HYRY's answer:

• call display before clear_output so that you end up with one plot, rather than two, when the cell is interrupted.
• catch the KeyboardInterrupt, so that the cell output isn't littered with the traceback.

import matplotlib.pylab as plt
import pandas as pd
import numpy as np
import time
from IPython import display
%matplotlib inline

i = pd.date_range('2013-1-1',periods=100,freq='s')

while True:
    try:
        plt.plot(pd.Series(data=np.random.randn(100), index=i))
        display.display(plt.gcf())
        display.clear_output(wait=True)
        time.sleep(1)
    except KeyboardInterrupt:
        break

Answer 3

Adding label to the other solutions posted here will keep adding new labels in every loop. To deal with that, clear the plot using clf

for t in range(100)
   if t % refresh_rate == 0:

     plt.clf()
     plt.plot(history['val_loss'], 'r-', lw=2, label='val')
     plt.plot(history['training_loss'], 'b-', lw=1, label='training')
     plt.legend()
     display.clear_output(wait=True)
     display.display(plt.gcf())

Answer 4

You can further improve this by adding wait=True to clear_output:

display.clear_output(wait=True)
display.display(pl.gcf())

Answer 5

use IPython.display module:

%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())
    time.sleep(1.0)

Answer 6

You can do it like this. It accepts x,y as list and output a scatter plot plus a linear trend on the same plot.

from IPython.display import clear_output
from matplotlib import pyplot as plt
%matplotlib inline
    
def live_plot(x, y, figsize=(7,5), title=''):
    clear_output(wait=True)
    plt.figure(figsize=figsize)
    plt.xlim(0, training_steps)
    plt.ylim(0, 100)
    x= [float(i) for i in x]
    y= [float(i) for i in y]
    
    if len(x) > 1:
        plt.scatter(x,y, label='axis y', color='k') 
        m, b = np.polyfit(x, y, 1)
        plt.plot(x, [x * m for x in x] + b)

    plt.title(title)
    plt.grid(True)
    plt.xlabel('axis x')
    plt.ylabel('axis y')
    plt.show();

you just need to call live_plot(x, y) inside a loop. Here's how it looks: