Refreshing plot in matplotlib - matplotlib

Question

As part of displaying the progression of a linear regression model fit, I need to be able to update/refresh an xy plot. Below is a simple script for 3 sets of y data, which need to be shown sequentially. However, they are piled up on top of each other. When fig.canvas.flush_events() is substituted with fig.clear() or fig.clf() the result is a blank plot. What am I - as a newbie -missing?
import torch as tc
import matplotlib.pyplot as plt
tc.manual_seed(1)
X=tc.linspace(-3,3,30)
y0=X.pow(2)+0.5*tc.randn(X.shape[0])
y1=y0/1.3
y2=y0/1.6
y=[y0,y1,y2]
fig=plt.figure()
ax=fig.add_subplot()
ax.set_xlim(-3.3,3.3)
ax.set_ylim(-0.5,9.5)
for i in range(3):
y_new=y[i]
ax.plot(X,y_new,'db')
fig.canvas.draw()
fig.canvas.flush_events()
plt.pause(1)
fig.show()

Answer 1

In your loop, you are creating a new line every time you call ax.plot. The better way is to create a Line2D artist and to update the coordinates of the point in the loop:
(NB i've converted your example to using numpy instead of torch)
import matplotlib.pyplot as plt
import numpy as np
X = np.linspace(-3, 3, 30)
y0 = np.power(X, 2) + 0.5 * np.random.randn(X.shape[0])
y1 = y0 / 1.3
y2 = y0 / 1.6
y = [y0, y1, y2]
fig = plt.figure()
ax = fig.add_subplot()
l, = ax.plot(X, y0, 'db')
ax.set_xlim(-3.3, 3.3)
ax.set_ylim(-0.5, 9.5)
for i in range(3):
y_new = y[i]
l.set_ydata(y_new)
fig.canvas.draw()
plt.pause(1)
plt.show()
For this kind of things, you'd be better off using the FuncAnimation module provided by maptlotlib though:
import matplotlib.pyplot as plt
from matplotlib import animation
import numpy as np
X = np.linspace(-3, 3, 30)
y0 = np.power(X, 2) + 0.5 * np.random.randn(X.shape[0])
y1 = y0 / 1.3
y2 = y0 / 1.6
y = [y0, y1, y2]
fig = plt.figure()
ax = fig.add_subplot()
l, = ax.plot(X, y0, 'db')
ax.set_xlim(-3.3, 3.3)
ax.set_ylim(-0.5, 9.5)
def animate(y_new):
l.set_ydata(y_new)
return l,
ani = animation.FuncAnimation(fig, func=animate, frames=y, interval=1000)
fig.show()