matplotlib-devel Mailing List for matplotlib

Hi all,
The file texmanager.py up until matplotlib 0.83.2 throws an exception 
when the current directory is on a different filesystem from the 
.matplotlib/tex.cache directory:
OSError: [Errno 18] Invalid cross-device link
This patch helps:
124c124
< os.rename(dvibase, dvifile)
---
 > shutil.move(dvibase, dvifile)
The shutil.move() is somewhat more robust than os.rename(). For more 
information see 
http://mail.python.org/pipermail/python-list/2005-February/266553.html 
and /266632.html.

On Wed, 2005年09月07日 at 12:12 -0500, John Hunter wrote:
> >>>>> "Nicholas" == Nicholas Young <N.P...@wa...> writes:
> Nicholas> My patch contained memory leaks which I've fixed in the
> Nicholas> attachment - but I'm not that experienced in c/c++ so
> Nicholas> there could be more I haven't noticed.
>
> You should see little or no leak if everything checks out.
Thanks for the suggestion John. After following it I've found another
two leaks (revised patch attached) and as a result on testing with your
suggestions get following output (at the start):
0 52604 18235
1 52608 18236
2 52608 18235
3 52608 18235
4 52608 18236
5 52608 18235
6 52608 18235
7 52608 18235
8 52612 18235
9 52612 18235
10 52612 18235
11 52612 18235
12 52612 18235
13 52612 18236
14 52612 18235
15 52616 18235
16 52616 18235
17 52616 18235
18 52616 18236
19 52616 18235
20 52616 18235
21 52616 18236
22 52616 18236
23 52616 18235
24 52616 18235
25 52616 18235
26 52616 18235
27 52616 18235
28 52616 18235
29 52616 18236
30 52616 18235
Am I correct in thinking the occasional slight increase in memory is due
to python not me?
Nick

>>>>> "Nicholas" == Nicholas Young <N.P...@wa...> writes:
 Nicholas> On Wed, 2005年09月07日 at 16:15 +0100, Nicholas Young wrote:
 >> I've attached a patch to CVS with the necessary changes below.
 >> There are some issues here:
 Nicholas> My patch contained memory leaks which I've fixed in the
 Nicholas> attachment - but I'm not that experienced in c/c++ so
 Nicholas> there could be more I haven't noticed.
 Nicholas> Nick
You might want to test with the following script
import os
def report_memory(i):
 pid = os.getpid()
 a2 = os.popen('ps -p %d -o rss,sz' % pid).readlines()
 print i, ' ', a2[1],
 return int(a2[1].split()[1])
for i in range(100):
 your_code_here()
 report_memory(i)
You should see little or no leak if everything checks out.
JDH

On Wed, 2005年09月07日 at 16:15 +0100, Nicholas Young wrote:
> I've attached a patch to CVS with the necessary changes below. There
> are some issues here:
My patch contained memory leaks which I've fixed in the attachment - but
I'm not that experienced in c/c++ so there could be more I haven't
noticed.
Nick

Hi,
I've recently come across a need to plot images for which I have
irregular sample points. As far as I can see the way to do this in
current mpl CVS is either pcolor or contourf (which is sometimes much
faster).
I've implemented a third way with a subclass of AxisImage called
NonUniformImage which creates an axes image using a custom extension to
the PyCXX _image module. The NonUniformImage class first turns all data
to a MxNx4 UInt8 on initialisation as a cache. The make_image function
is replaced to call the extension code on each call with the x and y
axes, the RGBA image data, the size of the image to output and the view
limits. This code uses nearest neighbour interpolation to determine the
closest colour and create the output. By putting the heavy calculations
into C++, by avoiding dealing with sample points that aren't rendered
and by only calculating the sample point to pixel map once per call this
code allows easy viewing and scrolling on fairly high resolution data.
On my laptop (1GB memory) 2.56 million points are handled fairly easily
(test script below:
I've attached a patch to CVS with the necessary changes below. There
are some issues here:
- I'm not sure what the axes.Axes function to access this should be
called so I haven't made one.
- I'm not sure how to handle image boundaries; I currently have no
boundaries and just choose the nearest sample point - however far away
that is.
- To cope with large images the original array data is not stored and
thus cmap and norm cannot be changed once set_data has been called.
test code:
---
from pylab import *
from Numeric import NewAxis
from matplotlib.image import NonUniformImage
x = arange(-4, 4, 0.005)
y = arange(-4, 4, 0.005)
print 'Size %d points' % (len(x) * len(y))
z = sqrt(x[NewAxis,:]**2 + y[:,NewAxis]**2)
im = NonUniformImage(gca())
im.set_data(x, y, z)
gca().images.append(im)
show()
x2 = x**3
im = NonUniformImage(gca())
im.set_data(x2, y, z)
gca().images.append(im)
show()
---
Hope this is useful,
Nick