绘制x和y辅助轴时缺少一个辅助标签 [英] One secondary label missing when plotting x and y secondary axis

问题描述

我来自这个问题 Matplotlib:两个x轴和两个y轴，我学会了如何在同一图上绘制两个x和y轴.

I'm coming from this question Matplotlib: two x axis and two y axis where I learned how to plot two x and y axis on the same plot.

这里是MWE:

import matplotlib.pyplot as plt
import numpy as np

# Generate random data.    
x1 = np.random.randn(50)
y1 = np.linspace(0, 1, 50)
x2 = np.random.randn(20)+15.
y2 = np.linspace(10, 20, 20)

# Plot both curves.
fig = plt.figure()

ax1 = fig.add_subplot(111)
ax1.set_xlabel('x_1')
ax1.set_ylabel('y_1')
plt.plot(x1, y1, c='r')

ax2 = ax1.twinx().twiny()
ax2.set_xlabel('x_2')
ax2.set_ylabel('y_2')
plt.ylim(min(y2), max(y2))
ax2.plot(x2, y2, c='b')

plt.show()

这是输出:

右侧y轴和顶部x轴对应于蓝色曲线.

The right y axis and the top x axis correspond to the blue curve.

如您所见，即使定义了第二个y标签，也将丢失它.我尝试了许多不同的方法，但无法显示出来.我在做错什么吗?

As you can see, the second y label is missing even though it is defined. I've tried a number of different approaches but I can't get it to show. Am I doing something wrong?

添加:

显然这行有问题:

ax2 = ax1.twinx().twiny()

如果我这样反转它:

ax2 = ax1.twiny().twinx()

然后是第二个x标签，将不会显示.

then it's the second x label that will not show.

推荐答案

基本上，正在发生的事情是创建了第三个坐标轴对象，您当前未保留对其的引用. ax2的可见y轴实际上属于此第三轴对象.

Basically, what's happening is that there's a third axes object created that you're not currently retaining a reference to. ax2's visible y-axis actually belongs to this third axes object.

您有两种选择.

1. 保留对隐藏"轴对象的引用，并设置其y标签.
2. 不要使用twinx和twiny，而是在与第一个相同的位置创建轴.

1. Retain a reference to the "hidden" axes object and set its y-label.
2. Don't use twinx and twiny and instead create an axes in the same position as the first.

第二个选项稍显冗长，但是具有第二个图的y轴限制将自动缩放的优点.您无需像当前一样手动设置它们.

The second option is a touch more verbose, but has the advantage that the y-axis limits on the second plot will autoscale as you'd expect. You won't need to manually set them as you're currently doing.

无论如何，这是第一种选择的示例:

At any rate, here's an example of the first option:

import matplotlib.pyplot as plt
import numpy as np

# Generate random data.
x1 = np.random.randn(50)
y1 = np.linspace(0, 1, 50)
x2 = np.random.randn(20)+15.
y2 = np.linspace(10, 20, 20)

# Plot both curves.
fig, ax1 = plt.subplots()

ax1.set(xlabel='x_1', ylabel='y_1')
ax1.plot(x1, y1, c='r')

tempax = ax1.twinx()
ax2 = tempax.twiny()
ax2.plot(x2, y2, c='b')
ax2.set(xlabel='x_2', ylim=[min(y2), max(y2)])
tempax.set_ylabel('y_2', rotation=-90)

plt.show()

...这是第二个选项的示例:

...And here's an example of the second option:

import matplotlib.pyplot as plt
import numpy as np

def twinboth(ax):
    # Alternately, we could do `newax = ax._make_twin_axes(frameon=False)`
    newax = ax.figure.add_subplot(ax.get_subplotspec(), frameon=False)
    newax.xaxis.set(label_position='top')
    newax.yaxis.set(label_position='right', offset_position='right')
    newax.yaxis.get_label().set_rotation(-90) # Optional...
    newax.yaxis.tick_right()
    newax.xaxis.tick_top()
    return newax

# Generate random data.
x1 = np.random.randn(50)
y1 = np.linspace(0, 1, 50)
x2 = np.random.randn(20)+15.
y2 = np.linspace(10, 20, 20)

# Plot both curves.
fig, ax1 = plt.subplots()

ax1.set(xlabel='x_1', ylabel='y_1')
ax1.plot(x1, y1, c='r')

ax2 = twinboth(ax1)
ax2.set(xlabel='x_2', ylabel='y_2')
ax2.plot(x2, y2, c='b')

plt.show()

两者都产生相同的输出:

Both produce identical output:

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