Python 多个 imshow-subplots，每个都有颜色条
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Multiple imshow-subplots, each with colorbar
提问by michael
I want to have a figure consisting of, let's say, four subplots. Two of them are usual line-plots, two of them imshow-images.
我想要一个由四个子图组成的图形。其中两个是通常的线图，其中两个是 imshow-images。
I can format the imshow-images to proper plots itself, because every single one of them needs its own colorbar, a modified axis and the other axis removed. This, however, seems to be absolutely useless for the subplotting. Can anyone help me with that?
我可以将 imshow-images 格式化为适当的绘图本身，因为它们中的每一个都需要自己的颜色条，修改后的轴并删除另一个轴。然而，这对于子图似乎绝对没用。任何人都可以帮助我吗？
I use this for displaying the data of the "regular" plots above as a colormap (by scaling the input-array ito [ i, i, i, i, i, i ]for 2D and calling imshow()with it).
我用这个用于显示“常规”地块上述数据作为颜色表（由缩放输入阵列i，以[ i, i, i, i, i, i ]用于2D和主叫imshow()用它）。
The following code first displays what I need as a subplot and the second one shows all I can do, which is not sufficient.
下面的代码首先显示了我需要的子图，第二个显示了我能做的所有事情，这还不够。
#!/usr/bin/env python
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
s = { 't':1, 'x':[1,2,3,4,5,6,7,8], 'D':[0.3,0.5,0.2,0.3,0.5,0.5,0.3,0.4] }
width = 40
# how I do it in just one plot
tot = []
for i in range(width):
tot.append(s['D'])
plt.imshow(tot, norm=LogNorm(vmin=0.001, vmax=1))
plt.colorbar()
plt.axes().axes.get_xaxis().set_visible(False)
plt.yticks([0, 2, 4, 6], [s['x'][0], s['x'][2], s['x'][4], s['x'][6]])
plt.show()
f = plt.figure(figsize=(20,20))
plt.subplot(211)
plt.plot(s['x'], s['D'])
plt.ylim([0, 1])
#colorplot
sp = f.add_subplot(212)
#reshape (just necessary to see something)
tot = []
for i in range(width):
tot.append(s['D'])
sp.imshow(tot, norm=LogNorm(vmin=0.001, vmax=1))
#what I can't do now but needs to be done:
#sp.colorbar()
#sp.axes().axes.get_xaxis().set_visible(False)
#sp.yticks([0, 200, 400, 600, 800, 1000], [s['x'][0], s['x'][200], s['x'][400], s['x'][600], s['x'][800], s['x'][1000]])
plt.show()
回答by sodd
You can make use of matplotlibs object oriented interface rather than the state-machine interace in order to get better control over each axes. Also, to get control over the height/width of the colorbar you can make use of the AxesGridtoolkit of matplotlib.
您可以使用 matplotlibs 面向对象的接口而不是状态机接口，以便更好地控制每个轴。此外，要控制颜色条的高度/宽度，您可以使用matplotlib的AxesGrid工具包。
For example:
例如：
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.axes_grid1 import make_axes_locatable
from matplotlib.colors import LogNorm
from matplotlib.ticker import MultipleLocator
s = {'t': 1,
'x': [1, 2, 3, 4, 5, 6, 7, 8],
'T': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8],
'D': [0.3, 0.5, 0.2, 0.3, 0.5, 0.5, 0.3, 0.4]}
width = 40
tot = np.repeat(s['D'],width).reshape(len(s['D']), width)
tot2 = np.repeat(s['T'],width).reshape(len(s['D']), width)
fig, (ax1, ax2, ax3, ax4) = plt.subplots(1,4)
fig.suptitle('Title of figure', fontsize=20)
# Line plots
ax1.set_title('Title of ax1')
ax1.plot(s['x'], s['T'])
ax1.set_ylim(0,1)
ax2.set_title('Title of ax2')
ax2.plot(s['x'], s['D'])
# Set locations of ticks on y-axis (at every multiple of 0.25)
ax2.yaxis.set_major_locator(MultipleLocator(0.25))
# Set locations of ticks on x-axis (at every multiple of 2)
ax2.xaxis.set_major_locator(MultipleLocator(2))
ax2.set_ylim(0,1)
ax3.set_title('Title of ax3')
# Display image, `aspect='auto'` makes it fill the whole `axes` (ax3)
im3 = ax3.imshow(tot, norm=LogNorm(vmin=0.001, vmax=1), aspect='auto')
# Create divider for existing axes instance
divider3 = make_axes_locatable(ax3)
# Append axes to the right of ax3, with 20% width of ax3
cax3 = divider3.append_axes("right", size="20%", pad=0.05)
# Create colorbar in the appended axes
# Tick locations can be set with the kwarg `ticks`
# and the format of the ticklabels with kwarg `format`
cbar3 = plt.colorbar(im3, cax=cax3, ticks=MultipleLocator(0.2), format="%.2f")
# Remove xticks from ax3
ax3.xaxis.set_visible(False)
# Manually set ticklocations
ax3.set_yticks([0.0, 2.5, 3.14, 4.0, 5.2, 7.0])
ax4.set_title('Title of ax4')
im4 = ax4.imshow(tot2, norm=LogNorm(vmin=0.001, vmax=1), aspect='auto')
divider4 = make_axes_locatable(ax4)
cax4 = divider4.append_axes("right", size="20%", pad=0.05)
cbar4 = plt.colorbar(im4, cax=cax4)
ax4.xaxis.set_visible(False)
# Manually set ticklabels (not ticklocations, they remain unchanged)
ax4.set_yticklabels([0, 50, 30, 'foo', 'bar', 'baz'])
plt.tight_layout()
# Make space for title
plt.subplots_adjust(top=0.85)
plt.show()
You can change the locations and labels of the ticks on either axis with the set_ticksand set_ticklabelsmethods as in the example above.
您可以使用上面示例中的set_ticks和set_ticklabels方法更改任一轴上刻度的位置和标签。
As for what the make_axes_locatablefunction does, from the matplotlib site about the AxesGrid toolkit:
至于make_axes_locatable函数的作用，来自matplotlib 站点关于 AxesGrid 工具包：
The axes_divider module provides a helper function make_axes_locatable, which can be useful. It takes a existing axes instance and create a divider for it.
ax = subplot(1,1,1) divider = make_axes_locatable(ax)make_axes_locatable returns an instance of the AxesLocator class, derived from the Locator. It provides append_axes method that creates a new axes on the given side of (“top”, “right”, “bottom” and “left”) of the original axes.
axis_divider 模块提供了一个辅助函数 make_axes_locatable，它很有用。它采用现有的轴实例并为其创建分隔线。
ax = subplot(1,1,1) divider = make_axes_locatable(ax)make_axes_locatable 返回从 Locator 派生的 AxesLocator 类的实例。它提供了 append_axes 方法，该方法在原始轴的（“顶部”、“右侧”、“底部”和“左侧”）的给定侧创建一个新轴。
