Hi,
I am writing a small module to easily load images and interact with
them. I now face a memory usage +CPU problem, which may in fact be the
result of a normal behaviour of matplotlib (but more certainly something
I am not doing right), but I really like to be sure here because if
confirmed, it would mean that this module is almost not usable for large
images...
Below is a (very simplified) example of what I am doing, that script
showing a similar behaviour than the real full module.
Questions and problems:
1/ when I load the image the first time (first imshow command in the
script), I see that, although the amount of data is very small,
ipython/matplotlib already uses more than 40 Mb of memory!
Is that normal?
2/ When I now use the mouse_move event, it can go up to 150 Mb of memory
usage!! Again: is that normal?
3/ When I do successive cycles of connect, disconnect, imshow (reloading
the figure), the memory usage seems to grow slightly, but more
importantly the capturing event (writing the data intensity on the top
right corner of the figure) gets REALLY SLOW.. Try a series of 10 imshow
with the same data..
So maybe I should "clean" some structure somewhere to allow a fast
interaction, or maybe the code below is rubish. But so far I haven't
found a cure, and this is very annoying to say the least.
Thanks for any tip on this problem.
Eric
P.S.: and for the sake of tempering the questions above (certainly
caused by my ignorance): matplotlib + numpy is just an amazing
combination!!!
P.P.S: set up for me is:
Suse 10.1
matplotlib version 0.87.7
numerix numpy 1.0.2.dev3491
Python 2.4.2 (#1, May 2 2006, 08:13:46)
IPython 0.7.4.svn.r2010 -- An enhanced Interactive Python.
## ===============================
## Below is a small script to illustrate memory and CPU pbs
## It loads some rand data, with coordinates given in xy
## and the mouse event allows to write the intensity of
## the image in the top right corner of the figure
## ===============================
import numpy as num
import matplotlib as mpl
import matplotlib.pylab as plab
data = num.random.rand(200,200)
xy = [num.arange(0.,20,.1), num.arange(0.,20,.1)]
fig = plab.figure()
canvas = fig.canvas
plab.imshow(data, extent=[0.,20.,0.,20.])
ftext = plab.figtext(0.9,0.9,"")
def whichpix_inframe(coord) :
indw = num.zeros(2, num.int32)
if len(coord) == 2 :
indw[0] = num.sort(xy[0]).searchsorted(coord[0])
indw[1] = num.sort(xy[1]).searchsorted(coord[1])
return indw
def mouse_move(event) :
if event.inaxes :
ax = event.inaxes
ftext.set_text(str(data[tuple(whichpix_inframe([event.xdata,
event.ydata]))]))
canvas.draw()
id = canvas.mpl_connect('motion_notify_event', mouse_move)
## then we can go on and cycle through the next 3 lines...: it shows the
CPU+memory pb getting worse
# canvas.mpl_disconnect(id)
# plab.imshow(data, extent=[0.,20.,0.,20.])
# id = canvas.mpl_connect('motion_notify_event', mouse_move)