["python","matplotlib","jupyter","jupyter-notebook"]
What is the currently correct way to dynamically update plots in Jupyter/iPython?

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)
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. The %matplotlib widget magic requires the ipympl Matplotlib Jupyter Extension package. You can install a working environment with pip install jupyter ipympl %matplotlib widget 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) plt.show() # run this cell to dynamically update plot for f in range(5): pltsin(ax, ['b', 'r']) time.sleep(1) I put this up on nbviewer here, and here's a direct link to the gist