Boris Barbour wrote:
Eric and John,
Thanks for the information. You are right that this probably would have been a
premature optimisation, even if it weren't rendered useless by matplotlib
using doubles internally (which I hadn't realised). The thought just occurred
to me as I was writing the data-scaling part of my script.
The script is intended to be somewhat interactive. Initial tests suggest that
plotting or updating several subplots from memory does take a quite
noticeable time (e.g. 1.2 -- 1.5 seconds for 3 subplots of 10000 points) that
will probably become annoying in routine use. As you indicated, basically all
that time is spent within matplotlib. I'm just using standard default calls:
for i in subplot
subplot
plot
xlabel
ylabel
title
Each of these calls seems to take roughly the same time (60--100ms). If
It sounds like you have interactive mode on, in which case each pylab
function redraws the figure. The solution is to use the object-oriented
interface for almost everything. See the attached example.
anybody has pointers on speeding things up significantly, I'm all ears.
(Predefining data limits? Using lower-level commands? Use of a non-default
backend?)
If the suggestion above is not enough, we will need to know more about
what your script looks like, the environment in which it is running
(e.g., ipython? embedded in wx? straight command line? what operating
system? what backend?), your constraints, and what you are trying to
accomplish. The best thing would be if you could post a very short
self-contained script, typically using fake random data, that shows your
present approach and that illustrates the speed problem; then we can try
to figure out what the bottlenecks are, and whether there are simple
ways to speed up the script or to modify mpl for better speed.
Eric
import time
import numpy as np
import matplotlib.pyplot as plt
npts = 10000
xx = np.arange(npts)
plt.ion()
t0 = time.time()
fig1 = plt.figure()
ax = fig1.add_subplot(1,1,1)
# Try again, commenting out the following three lines;
# you will see no significant difference in the plotting time.
ax.set_title('A title')
ax.set_xlabel('This is X')
ax.set_ylabel('This is Y')
lines = ax.plot(xx, np.random.rand(npts))
plt.draw()
t1 = time.time()
print t1-t0
for ii in range(10):
t0 = time.time()
lines[0].set_data(xx, np.random.rand(npts))
plt.draw()
t1 = time.time()
print t1-t0
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