缓慢而流畅的绘制线条 python matplotlib

Question

我正在使用 matplotlib 绘制平滑线等图形。对我来说画画没问题，但我在动画方面有问题。

import numpy as np
import random as random
from matplotlib import pyplot as plt
from matplotlib import animation

所以，我有数组，比如：

a = [0,1,2,4,5,8,9,12,14,18,22,17,30,37,29,45]

我需要点对点平滑地绘制它。 现在我有了这个字符串：

for i in range(len(a)-1):
    desty = np.append(desty,np.linspace(a[i],a[i+1],n))
    destx = np.append(destx,np.linspace(i,i+1,n))

这让我可以毫无问题地画线（和动画:)）

完整代码：

# First set up the figure, the axis, and the plot element we want to animate
fig = plt.figure()
ax = plt.axes(xlim=(0, 50), ylim=(0, 50))
line, = ax.plot([], [], lw=2)
global x
global y
global n
global a
n=5
a = [0,1,2,4,5,8,9,12,14,18,22,17,30,37,29,45]

global desty,destx
desty = np.linspace(0,0,n)
destx = np.linspace(0,0,n)
for i in range(len(a)-1):
    desty = np.append(desty,np.linspace(a[i],a[i+1],n))
    destx = np.append(destx,np.linspace(i,i+1,n))
# initialization function: plot the background of each frame
def init():
    line.set_data([], [])
    return line,
# animation function.  This is called sequentially
def animate(i):
    global destx
    global desty
    x=destx
    y=desty
    line.set_data(x, y)
    return line,

# call the animator.  blit=True means only re-draw the parts that have changed.
anim = animation.FuncAnimation(fig, animate, init_func=init,
                               frames=200, interval=20, blit=True)
plt.show()

只允许我绘制它，但我如何才能缓慢而平滑地点对点地绘制它？

我使用的是 python 2.7、centos 7。

Answer 1

您可以将您的绘图程序精简到：

import numpy as np
import random as random
from matplotlib import pyplot as plt
from matplotlib import animation
# First set up the figure, the axis, and the plot element we want to animate
fig = plt.figure()
ax = plt.axes(xlim=(0, 50), ylim=(0, 50))
line, = ax.plot([], [], lw=2)

n=5
a = [0,1,2,4,5,8,9,12,14,18,22,17,30,37,29,45]

x = []
y = []

# initialization function: plot the background of each frame
def init():
    line.set_data([], [])
    return line,

# animation function.  This is called sequentially
def animate(i):
    x.append(np.linspace(i,i+1,n))
    y.append(np.linspace(a[i],a[i+1],n))
    line.set_data(x,y)

    return line,

# call the animator.  blit=True means only re-draw the parts that have changed.
anim = animation.FuncAnimation(fig, animate, np.arange(0,len(a)-1) ,init_func=init, 
                               interval=200, blit=True, repeat=False)

plt.show()

注意事项：

• 你不需要global 变量来工作
• 您不想在动画之外设置 x 和 y 以便情节发展（在您的情况下，您已经设置了完整的 x 和 y 以便动画只会绘制整个图表）
• 您需要将一个interable 传递给animate（i）；这是FuncAnimation - np.arange(0,len(a)-1) 的第三个输入。
• 将repeat=False 设置为在一次运行后停止并避免“关闭”曲线
• 我增加了interval，让剧情发展更慢

Answer 2

您需要在您的动画函数中使用i，否则它只会一遍又一遍地绘制相同的东西，这就是您所看到的。

import numpy as np
import random as random
from matplotlib import pyplot as plt
from matplotlib import animation

# First set up the figure, the axis, and the plot element we want to animate
fig = plt.figure()
ax = plt.axes(xlim=(0, 50), ylim=(0, 50))
line, = ax.plot([], [], lw=2)
global x
global y
global n
global a
n=5
a = [0,1,2,4,5,8,9,12,14,18,22,17,30,37,29,45]

global desty,destx
desty = np.linspace(0,0,n)
destx = np.linspace(0,0,n)
plot_me_x = []
plot_me_y = []
for i in range(len(a)-1):
    desty = np.append(desty, np.linspace(a[i],a[i+1],n))
    destx = np.append(destx, np.linspace(i,i+1,n))
    plot_me_x.append(np.array(destx))   # keep a copy of the intermediaries to plot later
    plot_me_y.append(np.array(desty))
# initialization function: plot the background of each frame
def init():
    line.set_data([], [])
    return line,
# animation function.  This is called sequentially
def animate(i):
    global destx
    global desty
    x=plot_me_x[i]
    y=plot_me_y[i]
    line.set_data(x, y)
    return line,

# call the animator.  blit=True means only re-draw the parts that have changed.
anim = animation.FuncAnimation(fig, animate, init_func=init,
                               frames=len(a)-1, interval=20, blit=True)
plt.show()

为了使每个动画的情节看起来不同，i 必须索引在每次迭代中看起来不同的东西。虽然desty和destx是在构建它们的迭代中逐渐附加的，但最终的结果不是这个逐渐构建而是一个单一的东西，所以你需要节省这个构建的中介。我用plot_me_x 和y 做到了这一点。

我写了这个答案，对 OP 的代码进行了最小的更改，以明确错误在哪里。总体而言，@Schorsch 所做的更改导致了整体上更清洁的方法（例如，没有全局变量）。