如何在 matplotlib 中为颜色条设置动画 [英] How to animate the colorbar in matplotlib

问题描述

我有一个动画，其中数据的范围变化很​​大.我想要一个 colorbar 来跟踪数据的最大值和最小值(即我不希望它被修复).问题是如何做到这一点.

I have an animation where the range of the data varies a lot. I would like to have a colorbar which tracks the max and the min of the data (i.e. I would like it not to be fixed). The question is how to do this.

理想情况下，我希望 colorbar 位于自己的轴上.

Ideally I would like the colorbar to be on its own axis.

我尝试了以下四件事

问题:每个帧都有一个新的颜色条

The problem: A new colorbar is plottet for each frame

#!/usr/bin/env python
"""
An animated image
"""
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation

fig = plt.figure()
ax = fig.add_subplot(111)


def f(x, y):
    return np.exp(x) + np.sin(y)

x = np.linspace(0, 1, 120)
y = np.linspace(0, 2 * np.pi, 100).reshape(-1, 1)

frames = []

for i in range(10):
    x       += 1
    curVals  = f(x, y)
    vmax     = np.max(curVals)
    vmin     = np.min(curVals)
    levels   = np.linspace(vmin, vmax, 200, endpoint = True)
    frame    = ax.contourf(curVals, vmax=vmax, vmin=vmin, levels=levels)
    cbar     = fig.colorbar(frame)
    frames.append(frame.collections)

ani = animation.ArtistAnimation(fig, frames, blit=False)

plt.show()

2.添加到图像

将上面的for循环改为

2. Adding to the images

Changing the for loop above to

initFrame = ax.contourf(f(x,y)) 
cbar      = fig.colorbar(initFrame)
for i in range(10):
    x       += 1
    curVals  = f(x, y)
    vmax     = np.max(curVals)      
    vmin     = np.min(curVals)      
    levels   = np.linspace(vmin, vmax, 200, endpoint = True)
    frame    = ax.contourf(curVals, vmax=vmax, vmin=vmin, levels=levels)
    cbar.set_clim(vmin = vmin, vmax = vmax)
    cbar.draw_all()
    frames.append(frame.collections + [cbar])

问题:这引发了

AttributeError: 'Colorbar' object has no attribute 'set_visible'

3.在自己的轴上绘图

问题:colorbar 没有更新.

 #!/usr/bin/env python
 """
 An animated image
 """
 import numpy as np
 import matplotlib.pyplot as plt
 import matplotlib.animation as animation

 fig = plt.figure()
 ax1 = fig.add_subplot(121)
 ax2 = fig.add_subplot(122)


 def f(x, y):
     return np.exp(x) + np.sin(y)

 x = np.linspace(0, 1, 120)
 y = np.linspace(0, 2 * np.pi, 100).reshape(-1, 1)

 frames = []

 for i in range(10):
     x       += 1
     curVals  = f(x, y)
     vmax     = np.max(curVals)
     vmin     = np.min(curVals)
     levels   = np.linspace(vmin, vmax, 200, endpoint = True)
     frame    = ax1.contourf(curVals, vmax=vmax, vmin=vmin, levels=levels)
     cbar     = fig.colorbar(frame, cax=ax2) # Colorbar does not update
     frames.append(frame.collections)

 ani = animation.ArtistAnimation(fig, frames, blit=False)

 plt.show()

2. 和 4 的组合.

问题:colorbar 是恒定的.

此处，但看起来 OP 对固定的 colorbar 感到满意.

A similar question is posted here, but it looks like the OP is satisfied with a fixed colorbar.

推荐答案

虽然我不确定如何专门使用 ArtistAnimation 来做到这一点，但使用 FuncAnimation 是非常坦率的.如果我对您的天真"版本 1 进行以下修改，它会起作用.

While I'm not sure how to do this specifically using an ArtistAnimation, using a FuncAnimation is fairly straightforward. If I make the following modifications to your "naive" version 1 it works.

修订版 1

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from mpl_toolkits.axes_grid1 import make_axes_locatable

fig = plt.figure()
ax = fig.add_subplot(111)

# I like to position my colorbars this way, but you don't have to
div = make_axes_locatable(ax)
cax = div.append_axes('right', '5%', '5%')

def f(x, y):
    return np.exp(x) + np.sin(y)

x = np.linspace(0, 1, 120)
y = np.linspace(0, 2 * np.pi, 100).reshape(-1, 1)

frames = []
for i in range(10):
    x       += 1
    curVals  = f(x, y)
    frames.append(curVals)

cv0 = frames[0]
cf = ax.contourf(cv0, 200)
cb = fig.colorbar(cf, cax=cax)
tx = ax.set_title('Frame 0')

def animate(i):
    arr = frames[i]
    vmax     = np.max(arr)
    vmin     = np.min(arr)
    levels   = np.linspace(vmin, vmax, 200, endpoint = True)
    cf = ax.contourf(arr, vmax=vmax, vmin=vmin, levels=levels)
    cax.cla()
    fig.colorbar(cf, cax=cax)
    tx.set_text('Frame {0}'.format(i))

ani = animation.FuncAnimation(fig, animate, frames=10)

plt.show()

主要区别在于我在函数中进行级别计算和轮廓绘制，而不是创建艺术家列表.颜色条有效，因为您可以清除前一帧的坐标区并在每一帧重做.

The main difference is that I do the levels calculations and contouring in a function instead of creating a list of artists. The colorbar works because you can clear the axes from the previous frame and redo it every frame.

在使用contour 或contourf 时，必须执行此重做，因为您不能仅动态更改数据.但是，由于您绘制了如此多的轮廓级别并且结果看起来很平滑，我认为您最好使用 imshow 代替 - 这意味着您实际上可以使用相同的艺术家并更改数据，并且颜色条会自动更新.它也更快！

Doing this redo is necessary when using contour or contourf, because you can't just dynamically change the data. However, as you have plotted so many contour levels and the result looks smooth, I think you may be better off using imshow instead - it means you can actually just use the same artist and change the data, and the colorbar updates itself automatically. It's also much faster!

更好的版本

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from mpl_toolkits.axes_grid1 import make_axes_locatable

fig = plt.figure()
ax = fig.add_subplot(111)

# I like to position my colorbars this way, but you don't have to
div = make_axes_locatable(ax)
cax = div.append_axes('right', '5%', '5%')

def f(x, y):
    return np.exp(x) + np.sin(y)

x = np.linspace(0, 1, 120)
y = np.linspace(0, 2 * np.pi, 100).reshape(-1, 1)

# This is now a list of arrays rather than a list of artists
frames = []
for i in range(10):
    x       += 1
    curVals  = f(x, y)
    frames.append(curVals)

cv0 = frames[0]
im = ax.imshow(cv0, origin='lower') # Here make an AxesImage rather than contour
cb = fig.colorbar(im, cax=cax)
tx = ax.set_title('Frame 0')

def animate(i):
    arr = frames[i]
    vmax     = np.max(arr)
    vmin     = np.min(arr)
    im.set_data(arr)
    im.set_clim(vmin, vmax)
    tx.set_text('Frame {0}'.format(i))
    # In this version you don't have to do anything to the colorbar,
    # it updates itself when the mappable it watches (im) changes

ani = animation.FuncAnimation(fig, animate, frames=10)

plt.show()

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