Python/Matplotlib - 有没有办法制作不连续的轴? [英] Python/Matplotlib - Is there a way to make a discontinuous axis?

问题描述

我正在尝试使用具有不连续 x 轴的 pyplot 创建绘图.绘制它的通常方式是轴将具有以下内容:

I'm trying to create a plot using pyplot that has a discontinuous x-axis. The usual way this is drawn is that the axis will have something like this:

(值)----//----(后来的值)

(values)----//----(later values)

其中//表示您正在跳过(值)和(后面的值)之间的所有内容.

where the // indicates that you're skipping everything between (values) and (later values).

我找不到任何这样的例子，所以我想知道这是否可能.我知道您可以在不连续性上加入数据，例如财务数据，但我想让轴上的跳跃更加明确.目前我只是在使用子图，但我真的很想让所有的东西最终都出现在同一个图表上.

I haven't been able to find any examples of this, so I'm wondering if it's even possible. I know you can join data over a discontinuity for, eg, financial data, but I'd like to make the jump in the axis more explicit. At the moment I'm just using subplots but I'd really like to have everything end up on the same graph in the end.

推荐答案

Paul 的回答是一个完美的解决方案.

Paul's answer is a perfectly fine method of doing this.

但是，如果您不想进行自定义变换，则可以仅使用两个子图来创建相同的效果.

However, if you don't want to make a custom transform, you can just use two subplots to create the same effect.

不是从头开始拼凑一个例子，而是一个优秀的Paul Ivanov 在 matplotlib 示例中编写的示例(仅在当前的 git 提示中，因为它仅在几个月前提交.还没有在网页上.).

Rather than put together an example from scratch, there's an excellent example of this written by Paul Ivanov in the matplotlib examples (It's only in the current git tip, as it was only committed a few months ago. It's not on the webpage yet.).

这只是对该示例的一个简单修改，使其具有不连续的 x 轴而不是 y 轴.(这就是我将这篇文章设为 CW 的原因)

This is just a simple modification of this example to have a discontinuous x-axis instead of the y-axis. (Which is why I'm making this post a CW)

基本上，您只需执行以下操作:

Basically, you just do something like this:

import matplotlib.pylab as plt
import numpy as np

# If you're not familiar with np.r_, don't worry too much about this. It's just 
# a series with points from 0 to 1 spaced at 0.1, and 9 to 10 with the same spacing.
x = np.r_[0:1:0.1, 9:10:0.1]
y = np.sin(x)

fig,(ax,ax2) = plt.subplots(1, 2, sharey=True)

# plot the same data on both axes
ax.plot(x, y, 'bo')
ax2.plot(x, y, 'bo')

# zoom-in / limit the view to different portions of the data
ax.set_xlim(0,1) # most of the data
ax2.set_xlim(9,10) # outliers only

# hide the spines between ax and ax2
ax.spines['right'].set_visible(False)
ax2.spines['left'].set_visible(False)
ax.yaxis.tick_left()
ax.tick_params(labeltop='off') # don't put tick labels at the top
ax2.yaxis.tick_right()

# Make the spacing between the two axes a bit smaller
plt.subplots_adjust(wspace=0.15)

plt.show()

要添加断轴//效果，我们可以这样做(同样，从Paul Ivanov的例子中修改):

To add the broken axis lines // effect, we can do this (again, modified from Paul Ivanov's example):

import matplotlib.pylab as plt
import numpy as np

# If you're not familiar with np.r_, don't worry too much about this. It's just 
# a series with points from 0 to 1 spaced at 0.1, and 9 to 10 with the same spacing.
x = np.r_[0:1:0.1, 9:10:0.1]
y = np.sin(x)

fig,(ax,ax2) = plt.subplots(1, 2, sharey=True)

# plot the same data on both axes
ax.plot(x, y, 'bo')
ax2.plot(x, y, 'bo')

# zoom-in / limit the view to different portions of the data
ax.set_xlim(0,1) # most of the data
ax2.set_xlim(9,10) # outliers only

# hide the spines between ax and ax2
ax.spines['right'].set_visible(False)
ax2.spines['left'].set_visible(False)
ax.yaxis.tick_left()
ax.tick_params(labeltop='off') # don't put tick labels at the top
ax2.yaxis.tick_right()

# Make the spacing between the two axes a bit smaller
plt.subplots_adjust(wspace=0.15)

# This looks pretty good, and was fairly painless, but you can get that
# cut-out diagonal lines look with just a bit more work. The important
# thing to know here is that in axes coordinates, which are always
# between 0-1, spine endpoints are at these locations (0,0), (0,1),
# (1,0), and (1,1). Thus, we just need to put the diagonals in the
# appropriate corners of each of our axes, and so long as we use the
# right transform and disable clipping.

d = .015 # how big to make the diagonal lines in axes coordinates
# arguments to pass plot, just so we don't keep repeating them
kwargs = dict(transform=ax.transAxes, color='k', clip_on=False)
ax.plot((1-d,1+d),(-d,+d), **kwargs) # top-left diagonal
ax.plot((1-d,1+d),(1-d,1+d), **kwargs) # bottom-left diagonal

kwargs.update(transform=ax2.transAxes) # switch to the bottom axes
ax2.plot((-d,d),(-d,+d), **kwargs) # top-right diagonal
ax2.plot((-d,d),(1-d,1+d), **kwargs) # bottom-right diagonal

# What's cool about this is that now if we vary the distance between
# ax and ax2 via f.subplots_adjust(hspace=...) or plt.subplot_tool(),
# the diagonal lines will move accordingly, and stay right at the tips
# of the spines they are 'breaking'

plt.show()

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