A jupyter-lab function for updating a plot dynamically in-place.

Live plot covers only pyplot.plot (for now) and requires some explicit imports. It works with jupyter_lab on my mac, I haven't tested it in any other environemnt. You are welcome to create pull requests.

The working principle is simple: create a loop in which you populate a dictionary with the data you want to plot. Use the keys to label different datasets. Still within the loop, pass it to live_plot, sit back and watch.

After importing the required libraries and defining the live_plot function (see example notebook), populate the dictionary in a loop. Better still, use a defaultdict to return an empty list the first time you use a new key:

import numpy as np
from collections import defaultdict

data = defaultdict(list)

for i in range(100):
    data['foo'].append(np.sin(i/10) + 0.1*np.random.random())
    data['bar'].append(np.random.random()+0.3)
    live_plot(data)

you should observe this in the cell output (the framerate will depend on the speed of the loop)

Make sure you create a few cells below the animation, otherwise the view snaps back into place and it's a bit annoying.