Moving matplotlib legend outside of the axis makes it cutoff by the figure box

Question

I'm familiar with the following questions:

Matplotlib savefig with a legend outside the plot

How to put the legend out of the plot

It seems that the answers in these questions have the luxury of being able to fiddle with the exact shrinking of the axis so that the legend fits.

Shrinking the axes, however, is not an ideal solution because it makes the data smaller making it actually more difficult to interpret; particularly when its complex and there are lots of things going on ... hence needing a large legend

The example of a complex legend in the documentation demonstrates the need for this because the legend in their plot actually completely obscures multiple data points.

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

What I would like to be able to do is dynamically expand the size of the figure box to accommodate the expanding figure legend.

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')

Notice how the final label 'Inverse tan' is actually outside the figure box (and looks badly cutoff - not publication quality!)

Finally, I've been told that this is normal behaviour in R and LaTeX, so I'm a little confused why this is so difficult in python... Is there a historical reason? Is Matlab equally poor on this matter?

I have the (only slightly) longer version of this code on pastebin http://pastebin.com/grVjc007

Answer 1

Sorry EMS, but I actually just got another response from the matplotlib mailling list (Thanks goes out to Benjamin Root).

The code I am looking for is adjusting the savefig call to:

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

This is apparently similar to calling tight_layout, but instead you allow savefig to consider extra artists in the calculation. This did in fact resize the figure box as desired.

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')

This produces:

[edit] The intent of this question was to completely avoid the use of arbitrary coordinate placements of arbitrary text as was the traditional solution to these problems. Despite this, numerous edits recently have insisted on putting these in, often in ways that led to the code raising an error. I have now fixed the issues and tidied the arbitrary text to show how these are also considered within the bbox_extra_artists algorithm.

[edit] Some of the comments below note that since 2019, the command has been simplified. plt.savefig('x.png', bbox_inches='tight') was sufficient. Thanks for sharing. – mateuszb Jun 27, 2019

Answer 2

Added: I found something that should do the trick right away, but the rest of the code below also offers an alternative.

Use the subplots_adjust() function to move the bottom of the subplot up:

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

Then play with the offset in the legend bbox_to_anchor part of the legend command, to get the legend box where you want it. Some combination of setting the figsize and using the subplots_adjust(bottom=...) should produce a quality plot for you.

Alternative: I simply changed the line:

fig = plt.figure(1)

to:

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

and changed

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

to

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

and it shows up fine on my screen (a 24-inch CRT monitor).

Here figsize=(M,N) sets the figure window to be M inches by N inches. Just play with this until it looks right for you. Convert it to a more scalable image format and use GIMP to edit if necessary, or just crop with the LaTeX viewport option when including graphics.

Answer 3

Here is another, very manual solution. You can define the size of the axis and paddings are considered accordingly (including legend and tickmarks). Hope it is of use to somebody.

Example (axes size are the same!):

Code:

#==================================================
# 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()
#--------------------------------------------------
#==================================================

Answer 4

I tried a very simple way, just make the figure a bit wider:

fig, ax = plt.subplots(1, 1, figsize=(a, b))

adjust a and b to a proper value such that the legend is included in the figure

Answer 5

Since google lead me here for a similar question it's probably worth noting that now you only need to use plt.savefig('myplot.png', bbox_inches='tight') to expand the canvas to fit a legend.

Most of the answers here are obsolete.

Answer 6

i agree with John Thomas, if you are using a plt. based figure then all you probably need is: plt.savefig('myplot.png', bbox_inches='tight')

Answer 7

The layout="constrained" option (available since Matplotlib 3.7.0) allows a pretty straightforward implementation:

import matplotlib.pyplot as plt
import numpy as np

if __name__ == "__main__":
    x = np.arange(-2 * np.pi, 2 * np.pi, 0.1)
    fig, ax = plt.subplots(1, 1, layout="constrained")
    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()
    ax.legend(handles, labels, loc='upper center', bbox_to_anchor=(0.5, -0.1))
    text = ax.text(-0.2, 1.05, "Arbitrary text", transform=ax.transAxes)
    ax.set_title("Trigonometry")
    ax.grid('on')
    fig.savefig('samplefigure.png')
    fig.show()

One of the benefits (compared with the original answer by jonathanbsyd) is that fig.show() works just as well.