Matplotlib funcanimation Blit缓慢 [英] Matplotlib funcanimation blit slow

问题描述

我在 Matplotlib 中遇到动画缓慢的问题.我正在对模拟的结果进行动画处理，最简单的方法是使用随时间变化颜色的矩形阵列来可视化.

I'm having issues with a slow animation in Matplotlib. I'm animating results from a simulation, which is easiest visualized with an array of rectangles that change color with time.

遵循这里的建议，我使用 blitting 仅绘制在每帧中发生变化的矩形的(一小部分).我也尝试使用 FuncAnimation 实现这一点，但是当使用 Blit=True 时，脚本运行速度要慢得多.

Following recommendations here, I'm using blitting to only draw the (small fraction) of rectangles that change in each frame. I also tried to implement this using FuncAnimation, but when using that with Blit=True, the script runs much slower.

我想知道这是不是因为我将所有的矩形返回给 FuncAnimation，所以即使它们没有改变，它也会重绘所有矩形.有没有办法将每一帧的不同艺术家传递给 FuncAnimation?我尝试只传递一个已更改的元组(animate"函数中注释掉的块)，但这导致看似随机的动画帧...

I'm wondering if this is because I'm returning all of the rectangles to FuncAnimation, so it redraws all of them even if they haven't changed. Is there a way to pass different artists at each frame to FuncAnimation? I tried just passing a tuple of the ones that had changed (the commented out block in the "animate" function), but that led to seemingly random animation frames...

使用:

$ python2 [script].py blit
$ python2 [script].py anim

谢谢！

import sys
import numpy as np
import matplotlib
matplotlib.use("TkAgg")
import matplotlib.pyplot as plt
import matplotlib.animation as manim

def animate_data(plot_type):
    """
    Use:
    python2 plot_anim.py [option]
    option = anim OR blit
    """

    # dimension parameters
    Nx = 30
    Ny = 20
    numtimes = 100
    size = 0.5

    if plot_type == "blit":
        # "interactive mode on"
        plt.ion()
    # Prepare to do initial plot
    fig = plt.figure()
    ax = fig.add_subplot(1,1,1)
    ax.set_aspect('equal', 'box')
    ax.xaxis.set_major_locator(plt.NullLocator())
    ax.yaxis.set_major_locator(plt.NullLocator())
    # An array in which to store the rectangle artists
    rects = np.empty((Nx, Ny), dtype=object)
    # Generate initial figure of all green rectangles
    for (i,j),k in np.ndenumerate(rects):
        color = 'green'
        rects[i, j] = plt.Rectangle([i - size / 2, j - size / 2],
                size, size, facecolor=color, edgecolor=color)
        ax.add_patch(rects[i, j])
    ax.autoscale_view()

    # "Old" method using fig.canvas.blit()
    if plot_type == "blit":
        plt.show()
        fig.canvas.draw()
        # Step through time updating the rectangles
        for tind in range(1, numtimes):
            updated_array = update_colors(rects)
            for (i, j), val in np.ndenumerate(updated_array):
                if val:
                    ax.draw_artist(rects[i, j])
            fig.canvas.blit(ax.bbox)

    # New method using FuncAnimate
    elif plot_type == "anim":
        def animate(tind):
            updated_array = update_colors(rects)
#            # Just pass the updated artists to FuncAnimation
#            toupdate = []
#            for (i, j), val in np.ndenumerate(updated_array):
#                if val:
#                    toupdate.append(rects[i, j])
#            return tuple(toupdate)
            return tuple(rects.reshape(-1))
        ani = manim.FuncAnimation(fig, animate, frames=numtimes,
                interval=10, blit=True, repeat=False)
        plt.show()

    return

# A function to randomly update a few rectangles
def update_colors(rects):
    updated_array = np.zeros(rects.shape)
    for (i, j), c in np.ndenumerate(rects):
        rand_val = np.random.rand()
        if rand_val < 0.003:
            rects[i, j].set_facecolor('red')
            rects[i, j].set_edgecolor('red')
            updated_array[i, j] = 1
    return updated_array

if __name__ == "__main__":
    if len(sys.argv) > 1:
        plot_type = sys.argv[1]
    else:
        plot_type = "blit"
    animate_data(plot_type)

推荐答案

每帧更新 600 个矩形很慢，代码中的 cbar_blit 模式更快，因为你只更新颜色改变的矩形.您可以使用 PatchCollection 来加速绘图，代码如下:

Update 600 rectangles every frame is very slow, cbar_blit mode in your code is faster because you only update the rectangles which's color is changed. You can use PatchCollection to speedup drawing, here is the code:

import numpy as np
import matplotlib
matplotlib.use("TkAgg")
import matplotlib.pyplot as plt
import matplotlib.animation as manim
from matplotlib.collections import PatchCollection

Nx = 30
Ny = 20
numtimes = 100

size = 0.5

x, y = np.ogrid[-1:1:30j, -1:1:20j]

data = np.zeros((numtimes, Nx, Ny))

for i in range(numtimes):
    data[i] = (x-i*0.02+1)**2 + y**2

colors = plt.cm.rainbow(data)

fig, ax = plt.subplots()

rects = []
for (i,j),c in np.ndenumerate(data[0]):
    rect = plt.Rectangle([i - size / 2, j - size / 2],size, size)
    rects.append(rect)

collection = PatchCollection(rects, animated=True)

ax.add_collection(collection)
ax.autoscale_view(True)


def animate(tind):
    c = colors[tind].reshape(-1, 4)
    collection.set_facecolors(c)    
    return (collection,)

ani = manim.FuncAnimation(fig, animate, frames=numtimes,
        interval=10, blit=True, repeat=False)

plt.show()        

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