I'm currently evaluating different python plotting libraries. Right now I'm trying matplotlib and I'm quite disappointed with the performance. The following example is modified from SciPy examples and gives me only ~ 8 frames per second!

Any ways of speeding this up or should I pick a different plotting library?

```
from pylab import *
import time
ion()
fig = figure()
ax1 = fig.add_subplot(611)
ax2 = fig.add_subplot(612)
ax3 = fig.add_subplot(613)
ax4 = fig.add_subplot(614)
ax5 = fig.add_subplot(615)
ax6 = fig.add_subplot(616)
x = arange(0,2*pi,0.01)
y = sin(x)
line1, = ax1.plot(x, y, 'r-')
line2, = ax2.plot(x, y, 'g-')
line3, = ax3.plot(x, y, 'y-')
line4, = ax4.plot(x, y, 'm-')
line5, = ax5.plot(x, y, 'k-')
line6, = ax6.plot(x, y, 'p-')
# turn off interactive plotting - speeds things up by 1 Frame / second
plt.ioff()
tstart = time.time() # for profiling
for i in arange(1, 200):
line1.set_ydata(sin(x+i/10.0)) # update the data
line2.set_ydata(sin(2*x+i/10.0))
line3.set_ydata(sin(3*x+i/10.0))
line4.set_ydata(sin(4*x+i/10.0))
line5.set_ydata(sin(5*x+i/10.0))
line6.set_ydata(sin(6*x+i/10.0))
draw() # redraw the canvas
print 'FPS:' , 200/(time.time()-tstart)
```

## Best Solution

First off, (though this won't change the performance at all) consider cleaning up your code, similar to this:

With the above example, I get around 10fps.

Just a quick note, depending on your exact use case, matplotlib may not be a great choice. It's oriented towards publication-quality figures, not real-time display.

However, there are a lot of things you can do to speed this example up.

There are two main reasons why this is as slow as it is.

1) Calling

`fig.canvas.draw()`

redrawseverything. It's your bottleneck. In your case, you don't need to re-draw things like the axes boundaries, tick labels, etc.2) In your case, there are a lot of subplots with a lot of tick labels. These take a long time to draw.

Both these can be fixed by using blitting.

To do blitting efficiently, you'll have to use backend-specific code. In practice, if you're really worried about smooth animations, you're usually embedding matplotlib plots in some sort of gui toolkit, anyway, so this isn't much of an issue.

However, without knowing a bit more about what you're doing, I can't help you there.

Nonetheless, there is a gui-neutral way of doing it that is still reasonably fast.

This gives me ~200fps.

To make this a bit more convenient, there's an

`animations`

module in recent versions of matplotlib.As an example: