[Python] matplotlib.animation.FuncAnimation lagging when resizing the plot window (more than 5...

The code below generates random data, and displays it in realtime with Matplotlib. The sliders allow the user to change the y-axis range. All of this works.

Problem: when resizing the window size or moving the window, there is a performance problem: the plot is lagging during 5 seconds (and sometimes it comes back, and then lags again), sometimes even closer to 10 seconds (in an average i5 computer).

How to troubleshoot this in matplotlib.animation?



import numpy as np, threading, time
import matplotlib.animation as animation, matplotlib.pyplot as plt, matplotlib.widgets as widgets
class Test():
def generate(self): # generate random data
while True:
self.realtime_y = {i: np.random.rand(100) for i in range(4)}
self.realtime_m = {i: np.random.rand(100) for i in range(4)}
time.sleep(0.030)
def start_gen(self):
threading.Thread(target=self.generate).start()
def realtime_visualization(self):
fig = plt.figure("Test", figsize=(10, 6))
ax1, ax2, ax3, ax4 = fig.subplots(4)
ax1.set_ylim(Y_AXIS_RANGE)
ax2.set_ylim(Y_AXIS_RANGE)
sliders = [widgets.Slider(ax3, f"Ymin", Y_AXIS_RANGE[0], Y_AXIS_RANGE[1], valinit=Y_AXIS_RANGE[0]),
widgets.Slider(ax4, f"Ymax", Y_AXIS_RANGE[0], Y_AXIS_RANGE[1], valinit=Y_AXIS_RANGE[1])]
sliders[0].on_changed(lambda v: [ax1.set_ylim(bottom=v), ax2.set_ylim(bottom=v)])
sliders[1].on_changed(lambda v: [ax1.set_ylim(top=v), ax2.set_ylim(top=v)])
l1 = {}
l2 = {}
for i, c in enumerate(["k", "r", "g", "b"]):
l1, *_ = ax1.plot(self.realtime_y, color=c)
l2, *_ = ax2.plot(self.realtime_m, color=c)
def func(n):
for i in range(4):
l1.set_ydata(self.realtime_y)
l2.set_ydata(self.realtime_m)
self.realtime_animation = animation.FuncAnimation(fig, func, frames=None, interval=30, blit=False, cache_frame_data=False)
plt.show()
Y_AXIS_RANGE = [-3, 3]
t = Test()
t.start_gen()
t.realtime_visualization()


Obviously with interval=500 the problem is less visible, but the animation has less fps, which is not always possible (e.g. if we have fast-moving data).

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