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

Question

In the answers to how to dynamically update a plot in a loop in ipython notebook (within one cell), an example is given of how to dynamically update a plot inside a Jupyter notebook within a Python loop. However, this works by destroying and re-creating the plot on every iteration, and a comment in one of the threads notes that this situation can be improved by using the new-ish %matplotlib nbagg magic, which provides an interactive figure embedded in the notebook, rather than a static image.

However, this wonderful new nbagg feature seems to be completely undocumented as far as I can tell, and I'm unable to find an example of how to use it to dynamically update a plot. Thus my question is, how does one efficiently update an existing plot in a Jupyter/Python notebook, using the nbagg backend? Since dynamically updating plots in matplotlib is a tricky issue in general, a simple working example would be an enormous help. A pointer to any documentation on the topic would also be extremely helpful.

To be clear what I'm asking for: what I want to do is to run some simulation code for a few iterations, then draw a plot of its current state, then run it for a few more iterations, then update the plot to reflect the current state, and so on. So the idea is to draw a plot and then, without any interaction from the user, update the data in the plot without destroying and re-creating the whole thing.

Here is some slightly modified code from the answer to the linked question above, which achieves this by re-drawing the whole figure every time. I want to achieve the same result, but more efficiently using nbagg.

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

Answer 1

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

Answer 2

I'm using jupyter-lab and this works for me (adapt it to your case):

from IPython.display import clear_output from matplotlib import pyplot as plt import numpy as np import collections %matplotlib inline  def live_plot(data_dict, figsize=(7,5), title=''):     clear_output(wait=True)     plt.figure(figsize=figsize)     for label,data in data_dict.items():         plt.plot(data, label=label)     plt.title(title)     plt.grid(True)     plt.xlabel('epoch')     plt.legend(loc='center left') # the plot evolves to the right     plt.show(); 

Then in a loop you populate a dictionary and you pass it to live_plot():

data = collections.defaultdict(list) for i in range(100):     data['foo'].append(np.random.random())     data['bar'].append(np.random.random())     data['baz'].append(np.random.random())     live_plot(data) 

make sure you have a few cells below the plot, otherwise the view snaps in place each time the plot is redrawn.