I am graphing several time series together on the same graph,
and so it is important to have a legend. I am also stacking two graphs
on top of one another, because some of the data is of a different type.
I wrote the graphing code as follows (pardon the newbie code, this is my first matplotlib day…)
fig = plt.figure()
axU = fig.add_subplot(211)
axL = fig.add_subplot(212)
axL.set_ylabel(‘size, shs’, color=‘b’)
stepPlotsU = 
stepPlotsL = 
for i in range( len( plotvalues ) ) :
if axisSide[i] == ‘U’ :
stepPlotsU.append(axU.step( T,plotvalues[i], where=‘post’ ))
stepPlotsL.append(axL.step( T,plotvalues[i], where=‘post’ ))
lU = axU.legend(tuple(stepPlotsU),tuple(legendArrayU),loc=‘upper right’)
lL = axL.legend( tuple(stepPlotsL),tuple(legendArrayL),loc=‘upper right’)
axU.axis([float(TMin),float(TMax),1.1 * minValueU -0.1 * maxValueU,1.1maxValueU - 0.1minValueU])
My code works, and produces an almost perfect graph, BUT…
My problem is that the legend is quite large, and covers up a lot of the graph.
The solution seems to be in the user guide, here
where it shows how to call legend with the kwarg bbox_to_anchor=(1.05,1), and in the
example, the box is nicely moved.
In my case, though, I am calling
axU.legend(tuple(stepPlotsU),tuple(legendArrayU),loc=‘upper right’, bbox_to_anchor=(1.05,1))
but it says this is an “unexpected keyword)”, I assume it is saying that axis.legend() doesn’t use this
keyword, even if legend does.
lL = axL.legend( tuple(stepPlotsL),tuple(legendArrayL),loc=‘upper right’,bbox_to_anchor=(1.05,1))
File “/etg/source/Linux/pkg/Python-2.6.2/lib/python2.6/site-packages/matplotlib/axes.py”, line 3823, in legend
self.legend_ = mlegend.Legend(self, handles, labels, **kwargs)
TypeError: init() got an unexpected keyword argument ‘bbox_to_anchor’
Does anyone out there know what I can do to get the legend off my graph? And is there a way
to shrink the legend font size down? It is way too large, and I can’t find anything in the docs about this,
that axis.legend() will accept…
PS: I am using Python 2.6.2, matplotlib 0.98.5.3, numpy 1.3.0, scipy 0.7.1
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