matplotlib-users Mailing List for matplotlib (Page 21)

On Wed, Jul 1, 2009 at 16:55, David Paulsen<dav...@du...> wrote:
>   from mpl_toolkits.mplot3d import Axes3D
> ImportError: No module named mplot3d
>
> I am using the enthought package with matplotlib version 0.98.5.2
>
> Any help on how to recover mplot 3d would be appreciated.
mplot3d still lives only under SVN and was not released yet (note that
there is a new version of matplotlib, 0.98.5.3).
So either you take the version from SVN (there's a guide on
matplotlib.sf.net) or wait for the next release :)
Regards,
-- 
Sandro Tosi (aka morph, morpheus, matrixhasu)
My website: http://matrixhasu.altervista.org/
Me at Debian: http://wiki.debian.org/SandroTosi

Mark Larsen wrote:
> Hopefully a really simple question. How do I remove the "box" (the
> black rectangle) around the plot.
> 
> I tried
> 
> ax = fig.add_subplot(111)
> ax.patch.set_visible(False)
> 
> but this makes the entire patch invisible.
> 
> Thanks,
> 
> Mark
> 
> ------------------------------------------------------------------------------
I think what you want is
ax = fig.add_subplot(111,frameon=False)
JLS

Dear List,
I tried running examples of 3D plots given from the matplotlib webpage, but
encountered the following error:
 from mpl_toolkits.mplot3d import Axes3D
ImportError: No module named mplot3d
I am using the enthought package with matplotlib version 0.98.5.2
Any help on how to recover mplot 3d would be appreciated.
Thanks,
david
David Paulsen, M.S.
Graduate Student
Department of Psychology & Neuroscience
Duke University

Hopefully a really simple question. How do I remove the "box" (the
black rectangle) around the plot.
I tried
ax = fig.add_subplot(111)
ax.patch.set_visible(False)
but this makes the entire patch invisible.
Thanks,
Mark

Him
I had a stem plot. Now I want to make the Y axis of log scale. But I do not
want to use semilogy since I prefer the bars in stem plot. Do you know how
to only scale the Y axis?
Cheers,
Forrest
Forrest Sheng Bao, BSEE, Graduate Student
Dept. of Computer Science/Electrical Engineering
Texas Tech University, Lubbock, Texas
http://narnia.cs.ttu.edu

Hallöchen!
I have a grid in my plot, but additionally I'd like to highlight the
"zero" axes, where x=0 or y=0, e.g. by showing them in red, or with
thicker lines. How is this possible?
Tschö,
Torsten.
-- 
Torsten Bronger, aquisgrana, europa vetus
 Jabber ID: tor...@ja...
 or http://bronger-jmp.appspot.com

On Tue, Jun 30, 2009 at 14:12, Fabrice Silva<si...@lm...> wrote:
> Le lundi 29 juin 2009 à 16:11 -0400, Jae-Joon Lee a écrit :
>> In the svn version of matplotlib, there are some helper classes to
>> ease this job a bit.
> Thanks for your pointer. Sadly the mpl.toolkits.axes_grid is not shipped
> by debian package, and downloading it requires other stuff. So I adapted
I'm the debian maintainer for matplotlib: if you need something
missing in Debian, get in touch with us, for example reporting a bug
against matplotlib requesting this toolkit.
I didn't check further, but probably it was not release because of
this phrase: "In the svn version of matplotlib".
Regards,
-- 
Sandro Tosi (aka morph, morpheus, matrixhasu)
My website: http://matrixhasu.altervista.org/
Me at Debian: http://wiki.debian.org/SandroTosi

Hi,
On Mon, Jun 1, 2009 at 10:20 PM, Fernando Perez<fpe...@gm...> wrote:
> The time for the Scipy'09 conference is rapidly approaching, and we
> would like to both announce the plan for tutorials and solicit
> feedback from everyone on topics of interest.
rather than rehash much here, where it's not easy to paste a table,
I've posted a note with the poll results here:
http://fdoperez.blogspot.com/2009/06/scipy-advanced-tutorials-results.html
The short and plain-text-friendly version is the final topic ranking:
1	Advanced topics in matplotlib use
2	Advanced numpy
3	Designing scientific interfaces with Traits
4	Mayavi/TVTK
5	Cython
6	Symbolic computing with sympy
7	Statistics with Scipy
8	Using GPUs with PyCUDA
9	Testing strategies for scientific codes
10	Parallel computing in Python and mpi4py
11	Sparse Linear Algebra with Scipy
12	Structured and record arrays in numpy
13	Design patterns for efficient iterator-based scientific codes
14	Sage
15	The TimeSeries scikit
16	Hermes: high order Finite Element Methods
17	Graph theory with NetworkX
We're currently contacting speakers, and we'll let you know once a
final list is made with confirmed speakers.
Cheers,
f

On Jun 30, 2009, at 7:54 PM, Tommy Grav wrote:
> That is what I was assuming, but it still seems a little odd that
> matplotlib generates
> that large of a memory footprint. Loading the fits file into the
> program using pyfits,
> with the code only uses 19MB of real memory and 600MB of virtual
> memory (strangly
> adding the line img = hdu[1].data, increases this to 208MB/800MB).
>
The reason for this is that pyfits doesn't actually load the data 
until you 'touch' the data attribute (to minimize memory, particularly 
if you just are interested in the header information).
As for the memory footprint of matplotlib, in order to be able to 
resize and handle interactive updates, it has to retain references to 
the original image, perhaps as well to intermediate products (and 
these references won't be memory collected until you clear the figure 
(e.g., clf()). It's one of the prices for flexibility and generality. 
It probably would take a lot of complexity to optimize it for large 
images (but John is better suited to answer this conclusively).
Perry
> Displaying images of various sizes I get these
> numbers from Activity Monitor
>
> Size Real Mem Virtual
> 3k x 3k 0.68GB 1.57GB
> 4k x 4k		0.92GB 1.80GB
> 5k x 5k		1.20GB	 2.10GB
> 5.5k x 5.5k 1.38GB 2.28GB
>
> And the limit seems to be somewhere just above 5.5k by 5.5k (darn :( )
>
> Cheers
> Tommy
>