在轴外移动matplotlib图例会使其被图框切断

Question

我熟悉以下问题:

Matplotlib savefig与情节外的传奇

如何将传说从情节中删除

看起来这些问题的答案很有可能摆脱轴的精确收缩,以便传说适合.

然而,收缩轴并不是一个理想的解决方案,因为它使数据变得更小,使得它实际上更难以解释; 特别是当它的复杂和有很多事情发生时...因此需要一个大的传奇

文档中复杂图例的示例演示了对此的需求,因为其图中的图例实际上完全遮盖了多个数据点.

http://matplotlib.sourceforge.net/users/legend_guide.html#legend-of-complex-plots

我希望能够做的是动态扩展图框的大小以适应不断扩大的图形图例.

import matplotlib.pyplot as plt
import numpy as np

x = np.arange(-2*np.pi, 2*np.pi, 0.1)
fig = plt.figure(1)
ax = fig.add_subplot(111)
ax.plot(x, np.sin(x), label='Sine')
ax.plot(x, np.cos(x), label='Cosine')
ax.plot(x, np.arctan(x), label='Inverse tan')
lgd = ax.legend(loc=9, bbox_to_anchor=(0.5,0))
ax.grid('on')

请注意最终标签"逆棕褐色"实际上是如何在图框之外(看起来严重截止 - 而不是出版质量!)

最后,我被告知这是R和LaTeX中的正常行为,所以我有点困惑为什么在python中这么难...有历史原因吗？Matlab在这件事上同样很差吗？

我在pastebin http://pastebin.com/grVjc007上有这个代码的(仅略微)更长版本

Answer 1

对不起EMS,但实际上我从matplotlib mailling列表中得到了另一个回复(感谢Benjamin Root).

我正在寻找的代码是将savefig调用调整为:

fig.savefig('samplefigure', bbox_extra_artists=(lgd,), bbox_inches='tight')
#Note that the bbox_extra_artists must be an iterable

这显然类似于调用tight_layout,但您允许savefig在计算中考虑额外的艺术家.事实上,这确实根据需要调整了数字框的大小.

import matplotlib.pyplot as plt
import numpy as np

plt.gcf().clear()
x = np.arange(-2*np.pi, 2*np.pi, 0.1)
fig = plt.figure(1)
ax = fig.add_subplot(111)
ax.plot(x, np.sin(x), label='Sine')
ax.plot(x, np.cos(x), label='Cosine')
ax.plot(x, np.arctan(x), label='Inverse tan')
handles, labels = ax.get_legend_handles_labels()
lgd = ax.legend(handles, labels, loc='upper center', bbox_to_anchor=(0.5,-0.1))
text = ax.text(-0.2,1.05, "Aribitrary text", transform=ax.transAxes)
ax.set_title("Trigonometry")
ax.grid('on')
fig.savefig('samplefigure', bbox_extra_artists=(lgd,text), bbox_inches='tight')

这会产生:https: //imgur.com/xzd8G87

Answer 2

补充:我发现了一些可以立即执行此操作的功能,但下面的其余代码也提供了另一种选择.

使用该subplots_adjust()功能向上移动子图的底部:

fig.subplots_adjust(bottom=0.2) # <-- Change the 0.02 to work for your plot.

然后使用bbox_to_anchor图例命令的图例部分中的偏移量进行播放,以获得所需的图例框.设置figsize和使用的一些组合subplots_adjust(bottom=...)应该为您生成高质量的图.

替代方案: 我只是改变了这条线:

fig = plt.figure(1)

至:

fig = plt.figure(num=1, figsize=(13, 13), dpi=80, facecolor='w', edgecolor='k')

并改变了

lgd = ax.legend(loc=9, bbox_to_anchor=(0.5,0))

至

lgd = ax.legend(loc=9, bbox_to_anchor=(0.5,-0.02))

它在我的屏幕上显示得很好(一台24英寸的CRT显示器).

这里figsize=(M,N)将图形窗口设置为M英寸×N英寸.只要玩这个,直到它看起来适合你.将其转换为更具伸缩性的图像格式,并在必要时使用GIMP进行编辑,或者viewport在包含图形时使用LaTeX 选项进行裁剪.

Answer 3

这是另一个非常手动的解决方案.您可以定义轴的大小,并相应地考虑填充(包括图例和刻度).希望它对某人有用.

示例(轴大小相同!):

码:

#==================================================
# Plot table

colmap = [(0,0,1) #blue
         ,(1,0,0) #red
         ,(0,1,0) #green
         ,(1,1,0) #yellow
         ,(1,0,1) #magenta
         ,(1,0.5,0.5) #pink
         ,(0.5,0.5,0.5) #gray
         ,(0.5,0,0) #brown
         ,(1,0.5,0) #orange
         ]


import matplotlib.pyplot as plt
import numpy as np

import collections
df = collections.OrderedDict()
df['labels']        = ['GWP100a\n[kgCO2eq]\n\nasedf\nasdf\nadfs','human\n[pts]','ressource\n[pts]'] 
df['all-petroleum long name'] = [3,5,2]
df['all-electric']  = [5.5, 1, 3]
df['HEV']           = [3.5, 2, 1]
df['PHEV']          = [3.5, 2, 1]

numLabels = len(df.values()[0])
numItems = len(df)-1
posX = np.arange(numLabels)+1
width = 1.0/(numItems+1)

fig = plt.figure(figsize=(2,2))
ax = fig.add_subplot(111)
for iiItem in range(1,numItems+1):
  ax.bar(posX+(iiItem-1)*width, df.values()[iiItem], width, color=colmap[iiItem-1], label=df.keys()[iiItem])
ax.set(xticks=posX+width*(0.5*numItems), xticklabels=df['labels'])

#--------------------------------------------------
# Change padding and margins, insert legend

fig.tight_layout() #tight margins
leg = ax.legend(loc='upper left', bbox_to_anchor=(1.02, 1), borderaxespad=0)
plt.draw() #to know size of legend

padLeft   = ax.get_position().x0 * fig.get_size_inches()[0]
padBottom = ax.get_position().y0 * fig.get_size_inches()[1]
padTop    = ( 1 - ax.get_position().y0 - ax.get_position().height ) * fig.get_size_inches()[1]
padRight  = ( 1 - ax.get_position().x0 - ax.get_position().width ) * fig.get_size_inches()[0]
dpi       = fig.get_dpi()
padLegend = ax.get_legend().get_frame().get_width() / dpi 

widthAx = 3 #inches
heightAx = 3 #inches
widthTot = widthAx+padLeft+padRight+padLegend
heightTot = heightAx+padTop+padBottom

# resize ipython window (optional)
posScreenX = 1366/2-10 #pixel
posScreenY = 0 #pixel
canvasPadding = 6 #pixel
canvasBottom = 40 #pixel
ipythonWindowSize = '{0}x{1}+{2}+{3}'.format(int(round(widthTot*dpi))+2*canvasPadding
                                            ,int(round(heightTot*dpi))+2*canvasPadding+canvasBottom
                                            ,posScreenX,posScreenY)
fig.canvas._tkcanvas.master.geometry(ipythonWindowSize) 
plt.draw() #to resize ipython window. Has to be done BEFORE figure resizing!

# set figure size and ax position
fig.set_size_inches(widthTot,heightTot)
ax.set_position([padLeft/widthTot, padBottom/heightTot, widthAx/widthTot, heightAx/heightTot])
plt.draw()
plt.show()
#--------------------------------------------------
#==================================================