有没有办法在 Matplotlib 中制作不连续的轴？

Question

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如下内容：

（值）—-//—-（后面的值）

其中 // 表示您要跳过 (values) 和 (later values) 之间的所有内容。

我还没有找到这方面的任何例子，所以我想知道它是否有可能。我知道您可以加入不连续的数据，例如财务数据，但我想让轴上的跳跃更明确。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张图上。

原文由 Justin S 发布，翻译遵循 CC BY-SA 4.0 许可协议

Answer 1

保罗的回答是一个非常好的方法。

但是，如果您不想进行自定义转换，则可以只使用两个子图来创建相同的效果。

Paul Ivanov 在 matplotlib 示例中有一个很好的例子， 而不是从头开始组合一个例子（它只在当前的 git 提示中，因为它是几个月前才提交的。它还没有在网页上。） .

这只是此示例的简单修改，具有不连续的 x 轴而不是 y 轴。 （这就是为什么我将这篇文章设为 CW）

基本上，您只需执行以下操作：

 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()

要添加断开的轴线 // 效果，我们可以这样做（同样，从 Paul Ivanov 的示例修改而来）：

 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()

原文由 Joe Kington 发布，翻译遵循 CC BY-SA 3.0 许可协议

Answer 2

我看到很多关于此功能的建议，但没有迹象表明它已被实施。这是暂时可行的解决方案。它对 x 轴应用阶跃函数变换。这是很多代码，但它相当简单，因为其中大部分是样板自定义比例的东西。我没有添加任何图形来指示中断的位置，因为这是样式问题。祝你好运完成工作。

 from matplotlib import pyplot as plt
from matplotlib import scale as mscale
from matplotlib import transforms as mtransforms
import numpy as np

def CustomScaleFactory(l, u):
    class CustomScale(mscale.ScaleBase):
        name = 'custom'

        def __init__(self, axis, **kwargs):
            mscale.ScaleBase.__init__(self)
            self.thresh = None #thresh

        def get_transform(self):
            return self.CustomTransform(self.thresh)

        def set_default_locators_and_formatters(self, axis):
            pass

        class CustomTransform(mtransforms.Transform):
            input_dims = 1
            output_dims = 1
            is_separable = True
            lower = l
            upper = u
            def __init__(self, thresh):
                mtransforms.Transform.__init__(self)
                self.thresh = thresh

            def transform(self, a):
                aa = a.copy()
                aa[a>self.lower] = a[a>self.lower]-(self.upper-self.lower)
                aa[(a>self.lower)&(a<self.upper)] = self.lower
                return aa

            def inverted(self):
                return CustomScale.InvertedCustomTransform(self.thresh)

        class InvertedCustomTransform(mtransforms.Transform):
            input_dims = 1
            output_dims = 1
            is_separable = True
            lower = l
            upper = u

            def __init__(self, thresh):
                mtransforms.Transform.__init__(self)
                self.thresh = thresh

            def transform(self, a):
                aa = a.copy()
                aa[a>self.lower] = a[a>self.lower]+(self.upper-self.lower)
                return aa

            def inverted(self):
                return CustomScale.CustomTransform(self.thresh)

    return CustomScale

mscale.register_scale(CustomScaleFactory(1.12, 8.88))

x = np.concatenate((np.linspace(0,1,10), np.linspace(9,10,10)))
xticks = np.concatenate((np.linspace(0,1,6), np.linspace(9,10,6)))
y = np.sin(x)
plt.plot(x, y, '.')
ax = plt.gca()
ax.set_xscale('custom')
ax.set_xticks(xticks)
plt.show()

原文由 Paul 发布，翻译遵循 CC BY-SA 3.0 许可协议