Matplotlib：如何使用大数据集为pcolormesh设置动画

Question

I am using matplotlib.pyplot to animate some array data. The data is in the form of an intensity map, so I have a mesh of x and y locations, and a value associated with those locations.

The difficulty is that I cannot simply update the intensity data because the x and y locations change as well.

For example, I can get something like this work, but it requires having an over-determined x and y grid first that will cover the entire range:

cax = ax.pcolormesh(x, y, G[:-1, :-1, 0],
                    vmin=-1, vmax=1, cmap='Blues')
fig.colorbar(cax)

def animate(i):
     cax.set_array(G[:-1, :-1, i].flatten())

This works, but I end up with a fairly large intensity array filled mostly with zeros.

I have found an example here that allows the x and y values to be changed. Here is a modified MWE:

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation

fig2 = plt.figure()

x = np.arange(-9, 10)
y = np.arange(-9, 10).reshape(-1, 1)
base = np.hypot(x, y)
ims = []
for add in np.arange(15):
    x = np.arange(-9+add, 10+add)
    y = np.arange(-9+add, 10+add)
    x, y = np.meshgrid(x, y)
    ims.append((plt.pcolormesh(x, y, base + add, norm=plt.Normalize(0, 30)),))

im_ani = animation.ArtistAnimation(fig2, ims, interval=50, repeat_delay=3000,
                                   blit=True)
plt.show()

The issue here is two-fold. First, I have about 3000 frames, so the list ims becomes unmanageable. Secondly, how can I get the data to clear between frames and not show every frame all at once? Perhaps there's a better way altogether?

Bonus: using a slider could be an alternative to an animation. I've used Slider on these types of data before, but only by initializing a huge x and y grid.

Thanks for the help! Apologies if I'm not using the proper tags.

Answer 1

I may misunderstand the problem here, but using a FuncAnimation seems more appropriate here.

With blitting

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation

fig, ax = plt.subplots()

x = np.arange(-9, 10)
y = np.arange(-9, 10).reshape(-1, 1)
base = np.hypot(x, y)

def animate(i):
    x = np.arange(-9+i, 10+i)
    y = np.arange(-9+i, 10+i)
    x, y = np.meshgrid(x, y)
    pc = ax.pcolormesh(x, y, base + i, norm=plt.Normalize(0, 30))
    return pc,

ax.axis([-9,30,-9,30])
im_ani = animation.FuncAnimation(fig, animate, frames=30, interval=50, 
                                 repeat_delay=3000, blit=True)
plt.show()

Without blitting

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation

fig, ax = plt.subplots()

x = np.arange(-9, 10)
y = np.arange(-9, 10).reshape(-1, 1)
base = np.hypot(x, y)


store=[]
def animate(i):
    x = np.arange(-9+i, 10+i)
    y = np.arange(-9+i, 10+i)
    x, y = np.meshgrid(x, y)
    if store:
        store[0].remove()
        del store[0]
    pc = ax.pcolormesh(x, y, base + i, norm=plt.Normalize(0, 30))
    store.append(pc)


ax.axis([-9,30,-9,30])
im_ani = animation.FuncAnimation(fig, animate, frames=30, interval=50, 
                                 repeat_delay=3000)
plt.show()