**I recently migrated to Python(x,y) and noticed my matplotlib graphs are resizing unacceptably slowly when I use the pan/zoom button.** I’m quite a fan of numpy, scipy, matplotlib, the python imaging library (PIL), and GUI platforms like Tk/TkInter, pyGTK, and pyQT, but getting them all to play nicely is a sometimes pain. I’m considering migrating entirely to Python(x,y) because, as a single distribution, it’s designed to install all these libraries (and many more) in a compatible way out of the box. However, when I did, I noticed matplotlib graphs would resize, rescale, and drag around the axes very slowly. After a lot of digging on the interweb, I figured out what was going wrong. I’ll show you by plotting 20 random data points the slow way (left) then the fast way (right).

**THE PROBLEM:** See the difference between the two plots? The one on the left (SLOW!) uses the Qt4Agg backend, which renders the matplotlib plot on a QT4 canvas. This is slower than the one on the right, which uses the more traditional TkAgg backend to draw the plot on a Tk canvas with tkinter (FASTER!). Check out matplotlib’s official description of what a backend is and which ones you can use. When you just install Python and matplotlib, Tk is used by default.

import numpy import matplotlib matplotlib.use('TkAgg') # <-- THIS MAKES IT FAST! import pylab pylab.plot(numpy.random.random_integers(0,100,20)) pylab.title("USING: "+matplotlib.get_backend()) pylab.show()

**THE FIX:** Tell matplotlib to stop using QT to draw the plot, and let it plot with Tk. This can be done immediately after importing matplotlib, but must be done before importing pylab using the line `matplotlib.use('TkAgg')`

. Here’s the full example I used to generate the demonstration plots above. Change TkAgg to Qt4Agg (or comment-out the ‘use’ line if you’re using PythonXY) and you will see performance go down the tube. Alternatively, make a change to the matplotlib rc file to customize default behavior when the package is loaded.

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