live updating with matplotlib

Question

So I have some phone accelerometry data and I would like to basically make a video of what the motion of the phone looked like. So I used matplotlib to create a 3D graph of the data:

from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import pandas as pd
import pickle
def pickleLoad(pickleFile):
    pkl_file = open(pickleFile, 'rb')
    data = pickle.load(pkl_file)
    pkl_file.close()
    return data
data = pickleLoad('/Users/ryansaxe/Desktop/kaggle_parkinsons/accelerometry/LILY_dataframe')
data = data.reset_index(drop=True)
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
xs = data['x.mean']
ys = data['y.mean']
zs = data['z.mean']
ax.scatter(xs, ys, zs)
ax.set_xlabel('X Label')
ax.set_ylabel('Y Label')
ax.set_zlabel('Z Label')
plt.show()

Now time is important and is actually also a factor that I only see one point at a time because time is also a factor and it lets me watch the progression of the accelerometry data!

What can I do with this to make it a live updating graph?

Only thing I can think of is to have a loop that goes through row by row and makes the graph from the row, but that will open so many files that it would be insane because I have millions of rows.

So how can I create a live updating graph?

Answer 1

Here is a bare-bones example that updates as fast as it can:

import pylab as plt
import numpy as np

X = np.linspace(0,2,1000)
Y = X**2 + np.random.random(X.shape)

plt.ion()
graph = plt.plot(X,Y)[0]

while True:
    Y = X**2 + np.random.random(X.shape)
    graph.set_ydata(Y)
    plt.draw()

The trick is not to keep creating new graphs as this will continue to eat up memory, but to change the x,y,z-data on an existing plot. Use .ion() and .draw() setup the canvas for updating like this.

Addendum: A highly ranked comment below by @Kelsey notes that:

You may need a plt.pause(0.01) after the plt.draw() line to get the refresh to show