我正在尝试使用具有不连续 x 轴的 pyplot 创建一个绘图。通常的绘制方式是轴将具有以下内容：
（值）----//----（后来的值）
其中 // 表示您正在跳过 (values) 和 (later values) 之间的所有内容。
我找不到任何这样的例子，所以我想知道这是否可能。我知道您可以将不连续的数据连接起来，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，但我真的很希望最终将所有内容都放在同一张图上。
【问题讨论】：
标签： python matplotlib
查看brokenaxes 包：
import matplotlib.pyplot as plt
from brokenaxes import brokenaxes
import numpy as np
fig = plt.figure(figsize=(5,2))
bax = brokenaxes(
xlims=((0, .1), (.4, .7)),
ylims=((-1, .7), (.79, 1)),
hspace=.05
)
x = np.linspace(0, 1, 100)
bax.plot(x, np.sin(10 * x), label='sin')
bax.plot(x, np.cos(10 * x), label='cos')
bax.legend(loc=3)
bax.set_xlabel('time')
bax.set_ylabel('value')
【讨论】：
from brokenaxes import brokenaxes。 @ben.dichter
pip install brokenaxes==0.2安装固定版本的代码。
对于那些感兴趣的人，我已经扩展了 @Paul 的答案并将其添加到 matplotlib 包装器 proplot。可以做轴"jumps", "speedups", and "slowdowns"。
目前没有办法像乔的回答那样添加表示离散跳跃的“十字架”，但我计划在未来添加这个。我还计划添加一个默认的“tick locator”，根据CutoffScale 参数设置合理的默认tick 位置。
【讨论】：
一个非常简单的技巧是
对我来说就像一个魅力：
# FAKE BROKEN AXES
# plot a white rectangle on the x-axis-spine to "break" it
xpos = 10 # x position of the "break"
ypos = plt.gca().get_ylim()[0] # y position of the "break"
plt.scatter(xpos, ypos, color='white', marker='s', s=80, clip_on=False, zorder=100)
# draw "//" on the same place as text
plt.text(xpos, ymin-0.125, r'//', fontsize=label_size, zorder=101, horizontalalignment='center', verticalalignment='center')
【讨论】：
解决 Frederick Nord 的问题，如何在使用比例不等于 1:1 的 gridspec 时启用对角线“断线”的平行方向，根据 Paul Ivanov 和 Joe Kingtons 的建议进行以下更改可能会有所帮助。可以使用变量 n 和 m 来改变宽度比。
import matplotlib.pylab as plt
import numpy as np
import matplotlib.gridspec as gridspec
x = np.r_[0:1:0.1, 9:10:0.1]
y = np.sin(x)
n = 5; m = 1;
gs = gridspec.GridSpec(1,2, width_ratios = [n,m])
plt.figure(figsize=(10,8))
ax = plt.subplot(gs[0,0])
ax2 = plt.subplot(gs[0,1], sharey = ax)
plt.setp(ax2.get_yticklabels(), visible=False)
plt.subplots_adjust(wspace = 0.1)
ax.plot(x, y, 'bo')
ax2.plot(x, y, 'bo')
ax.set_xlim(0,1)
ax2.set_xlim(10,8)
# 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()
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)
on = (n+m)/n; om = (n+m)/m;
ax.plot((1-d*on,1+d*on),(-d,d), **kwargs) # bottom-left diagonal
ax.plot((1-d*on,1+d*on),(1-d,1+d), **kwargs) # top-left diagonal
kwargs.update(transform=ax2.transAxes) # switch to the bottom axes
ax2.plot((-d*om,d*om),(-d,d), **kwargs) # bottom-right diagonal
ax2.plot((-d*om,d*om),(1-d,1+d), **kwargs) # top-right diagonal
plt.show()
【讨论】：
我看到了很多关于此功能的建议，但没有迹象表明它已实施。这是一个暂时可行的解决方案。它将阶跃函数变换应用于 x 轴。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规模的东西。我没有添加任何图形来指示中断的位置，因为这是风格问题。祝你工作顺利。
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()
【讨论】：
InvertedCustomTransform 的def transform 中有一个小错字，应该写成self.upper 而不是upper。不过，谢谢你的好例子！
transform_non_affine 而不是transform。在stackoverflow.com/a/34582476/1214547 上查看我的补丁。
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变换，则可以只使用两个子图来创建相同的效果。
matplotlib 示例中有an excellent example of this written by Paul Ivanov，而不是从头开始编写一个示例（它仅在当前的 git 提示中，因为它仅在几个月前提交。它还没有在网页上。）。
这只是对这个示例的简单修改，使用不连续的 x 轴而不是 y 轴。 （这就是我将这篇文章设为 CW 的原因）
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()
要添加折断轴线// 效果，我们可以这样做（同样，修改自 Paul Ivanov 的示例）：
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()
【讨论】：
// 效果的制作方法似乎只有在子图形的比例为 1:1 时才有效。你知道如何使它与引入的任何比率一起工作，例如GridSpec(width_ratio=[n,m])?
/ 效果不会抑制正常的刻度，这在美学上是不和谐的