Python/Matplotlib - 有沒有辦法讓一個不連續的軸？

Question

我試圖創建一個使用pyplot具有不連續的x軸的情節。這是畫的常用方法是，軸將是這樣的：

（值）---- ---- //（後值）

其中//表示你跳過（值）和（後面的值）之間的所有內容。

我一直沒能找到任何這樣的例子，所以我想知道如果它甚至是可能的。我知道你可以通過不連續的方式加入數據，例如財務數據，但是我想讓這個軸的跳轉更加明確。目前我只是使用子圖，但我最終希望最終能夠在同一張圖上顯示所有內容。

Answer 1

保羅的回答是這樣的一個完全正常的方法。

但是，如果您不想進行自定義轉換，則可以使用兩個子圖創建相同的效果。

matplotlib示例中只有一個an excellent example of this written by Paul Ivanov（僅在當前的git提示中，因爲它僅在幾個月前提交，尚未在網頁上提供），而不是從頭開始構建示例。

這只是本示例的一個簡單修改，它具有不連續的x軸而不是y軸。 （這就是爲什麼我在做這個職位的CW）

基本上，你只是做這樣的事情：

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() 

要添加破軸線//效果，我們可以做這（再次，從保羅·伊萬諾夫的例子修改）：

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() 

Answer 2

我看到這個功能的許多建議，但沒有表明它已經實施。這是一個可行的解決方案。它對x軸應用了階躍函數變換。這是很多代碼，但它很簡單，因爲它大部分都是樣板自定義比例。我沒有添加任何圖形來指示休息的位置，因爲這是一個風格問題。祝你好運完成這份工作。

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() 

Answer 3

關於弗雷德裏克諾德的問題如何在使用比例不等於1：1的網格時允許對角「斷裂」線的平行定向，根據保羅伊萬諾夫和喬金頓的建議進行以下更改可能會有幫助。寬度比率可以使用變量n和m來改變。

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() 

Answer 4

檢查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')