matplotlib在子图

Question

我有一个有问题的子图，它有两个数据量表。我想要打破轴，而不是使用对数刻度，因此子图y轴的一半从0到10，另一半从10到100。

import numpy as np
import matplotlib.pyplot as plt
x = np.random.uniform(0, 10, 40)
y = np.concatenate([np.random.uniform(0, 1, 30), np.random.uniform(0, 100, 10)])
y2 = np.random.uniform(0, 1, 40) 
fig, ax = plt.subplots(2, sharex=True)
ax[0].scatter(x, y) # problematic subplot
ax[1].scatter(x, y2)
plt.show()

我试过跟随pyplot的断轴演示，虽然这似乎是错误的。我是否需要创建总共四个子图来执行此操作？这只是一个虚拟的例子，我的真正问题有几个子图，其中许多需要这些断轴。

import numpy as np
import matplotlib.pyplot as plt
x = np.random.uniform(0, 10, 40)
y = np.concatenate([np.random.uniform(0, 1, 30), np.random.uniform(0, 100, 10)])
y2 = np.random.uniform(0, 1, 40) 

fig, ax = plt.subplots(4, sharex=True)

# Create broken axis with first two subplots
ax[0].scatter(x, y)
ax[1].scatter(x, y)
ax[0].set_ylim(1, 100)
ax[1].set_ylim(0, 1)
ax[0].spines['bottom'].set_visible(False)
ax[1].spines['top'].set_visible(False)

# From https://matplotlib.org/examples/pylab_examples/broken_axis.html
d = .015  # how big to make the diagonal lines in axes coordinates
# arguments to pass to plot, just so we don't keep repeating them
kwargs = dict(transform=ax[0].transAxes, color='k', clip_on=False)
ax[0].plot((-d, +d), (-d, +d), **kwargs)        # top-left diagonal
ax[0].plot((1 - d, 1 + d), (-d, +d), **kwargs)  # top-right diagonal

kwargs.update(transform=ax[1].transAxes)  # switch to the bottom axes
ax[0].plot((-d, +d), (1 - d, 1 + d), **kwargs)  # bottom-left diagonal
ax[0].plot((1 - d, 1 + d), (1 - d, 1 + d), **kwargs)  # bottom-right diagonal



# Try my best to fix bottom two plots so they look like one plot
ax[2].scatter(x, y2)
ax[3].scatter(x, y2)
ax[2].set_ylim(.5, 1.0)
ax[3].set_ylim(0, .5)
ax[2].spines['bottom'].set_visible(False)
ax[3].spines['top'].set_visible(False)
plt.savefig('ex.pdf')

Answer 1

我可能建议只使用两个子图，一个在顶部，一个在底部。然后，通过mpl_toolkits.axes_grid1.make_axes_locatable将上面的一个分为两个。

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


x = np.random.uniform(0, 10, 40)
y = np.concatenate([np.random.uniform(0, 1, 30), np.random.uniform(0, 100, 10)])
y2 = np.random.uniform(0, 1, 40) 

fig, axes = plt.subplots(nrows=2, sharex=True)

ax = axes[0]
divider = make_axes_locatable(ax)
ax2 = divider.new_vertical(size="100%", pad=0.1)
fig.add_axes(ax2)

ax.scatter(x, y)
ax.set_ylim(0, 1)
ax.spines['top'].set_visible(False)
ax2.scatter(x, y)
ax2.set_ylim(10, 100)
ax2.tick_params(bottom="off", labelbottom='off')
ax2.spines['bottom'].set_visible(False)


# From https://matplotlib.org/examples/pylab_examples/broken_axis.html
d = .015  # how big to make the diagonal lines in axes coordinates
# arguments to pass to plot, just so we don't keep repeating them
kwargs = dict(transform=ax2.transAxes, color='k', clip_on=False)
ax2.plot((-d, +d), (-d, +d), **kwargs)        # top-left diagonal
ax2.plot((1 - d, 1 + d), (-d, +d), **kwargs)  # top-right diagonal

kwargs.update(transform=ax.transAxes)  # switch to the bottom axes
ax.plot((-d, +d), (1 - d, 1 + d), **kwargs)  # bottom-left diagonal
ax.plot((1 - d, 1 + d), (1 - d, 1 + d), **kwargs)  # bottom-right diagonal


#create bottom subplot as usual
axes[1].scatter(x, y2)


plt.show()