如何为所有子图设置一个颜色条 [英] How to have one colorbar for all subplots

问题描述

我花了很长时间研究如何让两个子图共享相同的 y 轴，并且在 Matplotlib 中两者之间共享一个颜色条.

I've spent entirely too long researching how to get two subplots to share the same y-axis with a single colorbar shared between the two in Matplotlib.

发生的事情是，当我在 subplot1 或 subplot2 中调用 colorbar() 函数时，它会自动缩放绘图，使得颜色条加上绘图将适合子绘图"边界框，从而导致两个并排绘图的大小截然不同.

What was happening was that when I called the colorbar() function in either subplot1 or subplot2, it would autoscale the plot such that the colorbar plus the plot would fit inside the 'subplot' bounding box, causing the two side-by-side plots to be two very different sizes.

为了解决这个问题，我尝试创建第三个子图，然后我对其进行了黑客攻击，以仅呈现颜色条不渲染任何图.唯一的问题是，现在两个地块的高度和宽度不均匀，我想不出如何让它看起来不错.

To get around this, I tried to create a third subplot which I then hacked to render no plot with just a colorbar present. The only problem is, now the heights and widths of the two plots are uneven, and I can't figure out how to make it look okay.

这是我的代码:

from __future__ import division
import matplotlib.pyplot as plt
import numpy as np
from matplotlib import patches
from matplotlib.ticker import NullFormatter

# SIS Functions
TE = 1 # Einstein radius
g1 = lambda x,y: (TE/2) * (y**2-x**2)/((x**2+y**2)**(3/2)) 
g2 = lambda x,y: -1*TE*x*y / ((x**2+y**2)**(3/2))
kappa = lambda x,y: TE / (2*np.sqrt(x**2+y**2))

coords = np.linspace(-2,2,400)
X,Y = np.meshgrid(coords,coords)
g1out = g1(X,Y)
g2out = g2(X,Y)
kappaout = kappa(X,Y)
for i in range(len(coords)):
    for j in range(len(coords)):
        if np.sqrt(coords[i]**2+coords[j]**2) <= TE:
            g1out[i][j]=0
            g2out[i][j]=0

fig = plt.figure()
fig.subplots_adjust(wspace=0,hspace=0)

# subplot number 1
ax1 = fig.add_subplot(1,2,1,aspect='equal',xlim=[-2,2],ylim=[-2,2])
plt.title(r"$gamma_{1}$",fontsize="18")
plt.xlabel(r"x ($	heta_{E}$)",fontsize="15")
plt.ylabel(r"y ($	heta_{E}$)",rotation='horizontal',fontsize="15")
plt.xticks([-2.0,-1.5,-1.0,-0.5,0,0.5,1.0,1.5])
plt.xticks([-2.0,-1.5,-1.0,-0.5,0,0.5,1.0,1.5])
plt.imshow(g1out,extent=(-2,2,-2,2))
plt.axhline(y=0,linewidth=2,color='k',linestyle="--")
plt.axvline(x=0,linewidth=2,color='k',linestyle="--")
e1 = patches.Ellipse((0,0),2,2,color='white')
ax1.add_patch(e1)

# subplot number 2
ax2 = fig.add_subplot(1,2,2,sharey=ax1,xlim=[-2,2],ylim=[-2,2])
plt.title(r"$gamma_{2}$",fontsize="18")
plt.xlabel(r"x ($	heta_{E}$)",fontsize="15")
ax2.yaxis.set_major_formatter( NullFormatter() )
plt.axhline(y=0,linewidth=2,color='k',linestyle="--")
plt.axvline(x=0,linewidth=2,color='k',linestyle="--")
plt.imshow(g2out,extent=(-2,2,-2,2))
e2 = patches.Ellipse((0,0),2,2,color='white')
ax2.add_patch(e2)

# subplot for colorbar
ax3 = fig.add_subplot(1,1,1)
ax3.axis('off')
cbar = plt.colorbar(ax=ax2)

plt.show()

推荐答案

只需将颜色条放在它自己的轴上并使用 subplots_adjust 为其腾出空间.

Just place the colorbar in its own axis and use subplots_adjust to make room for it.

举个简单的例子:

import numpy as np
import matplotlib.pyplot as plt

fig, axes = plt.subplots(nrows=2, ncols=2)
for ax in axes.flat:
    im = ax.imshow(np.random.random((10,10)), vmin=0, vmax=1)

fig.subplots_adjust(right=0.8)
cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7])
fig.colorbar(im, cax=cbar_ax)

plt.show()

请注意，即使值的范围是由 vmin 和 vmax.例如，如果另一个图具有更高的最大值，则值高于 im 最大值的点将以统一颜色显示.

Note that the color range will be set by the last image plotted (that gave rise to im) even if the range of values is set by vmin and vmax. If another plot has, for example, a higher max value, points with higher values than the max of im will show in uniform color.

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