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

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

Questions:

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.

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.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

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

x = np.arange(-2*np.pi, 2*np.pi, 0.1)
fig = plt.figure(1)
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))
ax.grid('on')
fig.savefig('samplefigure', bbox_extra_artists=(lgd,), bbox_inches='tight')

This produces: Questions:

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:

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.

Questions:

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

fig = plt.figure(figsize=(2,2))
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

padTop    = ( 1 - ax.get_position().y0 - ax.get_position().height ) * fig.get_size_inches()
padRight  = ( 1 - ax.get_position().x0 - ax.get_position().width ) * fig.get_size_inches()
dpi       = fig.get_dpi()

widthAx = 3 #inches
heightAx = 3 #inches

# resize ipython window (optional)
posScreenX = 1366/2-10 #pixel
posScreenY = 0 #pixel
canvasBottom = 40 #pixel