matplotlib-devel Mailing List for matplotlib

Does some other setting needs to be made to allow me to change the status of
bug reports in sourceforge? I can't close reports or assign them to myself.
Thanks,
Ben Root

On Tue, Nov 30, 2010 at 4:53 PM, Benjamin Root <ben...@ou...> wrote:
> On Wednesday, November 17, 2010, Benjamin Root <ben...@ou...> wrote:
> > On Tue, Nov 16, 2010 at 5:20 PM, J P <jp...@gm...> wrote:
> >
> >
> > Hi all, here's my first patch for matplotlib. Someone noticed at Stack
> Overflow that the plot_surface function in mplot3d wasn't especially fast
> for a lot of points (and small rstrides/cstrides) and using shading and a
> single color. I found some parts of the code that weren't vectorized. These
> are my changes so far.
> >
> > Summary of changes:
> > 1. Changed from double looping over aranges to using xrange
> > 2. Made the normalization of the normals and their dot product with the
> vector [-1,-1,0.5] to find the shading a vectorized operation.
> > 3. Changed a list comprehension which calculated the colors using an
> iterative approach to using the already built-in vectorization of the
> Normalization class and using the np.outer function. The result is a numpy
> array rather than a list which actually speeds up things down the line.
> > 4. removed the corners array from plot_surface which wasn't ever used or
> returned. It didn't really slow things down, but I'm thinking that it is
> cruft.
> >
> > For change number two, I made a separate function that generates the
> shades, but feel free to move that around if you prefer.. or maybe it should
> be a function that begins with a _ because it shouldn't be used externally.
> These changes give varying levels of speed improvement depending on the
> number of points and the rstrides/cstrides arguments. With larger numbers of
> points and small rstrides/cstrides, these changes can more than halve the
> running time. I have found no difference in output after my changes.
> >
> > I know there is more work to be done within the plot_surface function and
> I'll submit more changes soon.
> >
> > Justin
> >
> >
> > Justin,
> >
> > Thank you for your efforts to improve the performance of mplot3d. I will
> take a look at the patches you have submitted and test them out. What I am
> probably going to do is break down the patches into smaller pieces and
> incorporate them piece-by-piece.
> >
> > I tried refactoring plot_surface once before to mixed results. The key
> feature I was trying to gain was compatibility with masked arrays. I wanted
> to duplicate the behavior of contourf and pcolor where masked out portions
> of the surface would not be created. I managed to get it to work for that
> particular use-case, but I broke a bunch of other use-cases along the way.
> I am curious to see if your patches make this easier/harder to do.
> >
> > Thank you for your efforts and thank you for using matplotlib!
> >
> > Ben Root
> >
> >
>
> I have looked through the patches, and there are definite
> improvements. I have broken the work into four separate patches. The
> first patch is essentially code cleanup and utilization of xrange
> (plot_surface_cleanup.patch). This patch can be back-ported without
> concern (although it doesn't fix any bug per se).
>
> The second patch does the vectorization of the shades. The original
> patch that was submitted had some edge cases, but I have found that
> just simply converting that for-loop that creates the shades into a
> list comprehension (and then pass into np.array) yielded almost
> identical speedups without changing the current code path. (Note: I
> am a minimalist when it comes to patches). This is in
> plot_surface_vectshading.patch.
>
> The third patch improves the calculation of the normals in
> plot_surface by pre-allocating the arrays for calculating the vectors
> and doing a final call to np.cross rather than appending on a list. I
> deviated slightly from the original patch by calling "which" as
> "which_pt", adding a couple of comments, and also added an else
> condition to create a "normal" with an empty list. This last part is
> to keep the code path as similar as it was before. It was
> theoretically possible to utilize a variable called normal elsewhere
> without all the same conditions holding, so this guarantees that
> normal exists, which was the case before. This patch is
> plot_surface_vectnorm.patch.
>
> Finally, the fourth patch utilizes numpy array functionality for
> calculating the vertexes. This patch also forgoes the use of
> transposed arrays. I took the original patch a step further and
> eliminated the array transpose line earlier in the plot_surface
> function. The array transpose was not being properly utilized here,
> and I saw no speed penalty/speedup either way, so in favor of simpler
> code, I eliminated its use. This patch is
> plot_surface_vectvertex.patch.
>
> Of the four patches, the speedups are in mostly found in the second
> patch (100% speedup). The first patch does provide noticeable
> improvements. There is also a slight improvement with the third
> patch. I am up in the air regarding speed improvements with the
> fourth patch, but I wonder if there might be some memory improvements
> here, or if any speedup is being hidden by the for-loop that the
> vectorization is done in.
>
> I will let these patches be mulled over before applying them. Thanks
> to JP for submitting the original patch.
>
> Ben Root
>
Re-pinging, as I haven't heard any opinions on this. The key question is
should any of these patch be put into the maintenance branch or should it
only be in the development branch?
Thanks,
Ben Root

I believe this was recently introduced when I refactored the annotation code.
Attached is a preliminary fix. So, please test it if you can.
Since the change during the refactoring was rather significant, I'm
not 100% sure if this will restore the old behavior without affecting
the new functionality. The examples I tried (including yours) seem to
work fine. I'll test this myself a few more days, and commit to the
svn.
I personally think it is better to use "offset points" for these cases
which makes the internal logic much simpler.
Regards,
-JJ
On Sat, Dec 11, 2010 at 6:07 AM, Stan West <sta...@nr...> wrote:
> Hi. The docs for Annotation [1] say that negative coordinates given for [
> figure | axes ] [ points | pixels ] xycoords are to be interpreted relative
> to the top-right corner, but I found that they act relative to the
> bottom-left corner as for positive coordinates. This can be seen in the
> attached script and in the annotation_demo.py example [2], where the string
> "bottom right (points)" bleeds off the left edge of the figure.
>
> [1]
> http://matplotlib.sourceforge.net/api/artist_api.html#matplotlib.text.Annotation
>
> [2]
> http://matplotlib.sourceforge.net/examples/pylab_examples/annotation_demo.html
>
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Hello all,
I finally, after much pain and anguish, succeeded at compiling MPL with
GTKAgg on windows and I thought people might be interested to know how I
managed it. Of course, your mileage may vary...
 1. Install Python (I used the Python (x,y) distribution of 2.6:
 http://code.google.com/p/pythonxy/wiki/Downloads
 2. Install the GTK bits and pieces. For some reason using the newest
 ones causes some problems with paths. I can confirm that this set works on
 at least two computers:
 Glade3.6.7withGTK+.exe<http://ftp.gnome.org/pub/GNOME/binaries/win32/glade3/3.6/glade3-3.6.7-with-GTK+.exe>
 (Install this to c:\GTK so that MPL build can find it without playing with
 paths)
 pygtk-2.12.1-3.win32-py2.6.exe<http://ftp.gnome.org/pub/GNOME/binaries/win32/pygtk/2.12/pygtk-2.12.1-3.win32-py2.6.exe>
 pycairo-1.4.12-2.win32-py2.6.exe<http://ftp.gnome.org/pub/GNOME/binaries/win32/pycairo/1.4/pycairo-1.4.12-2.win32-py2.6.exe>
 pygobject-2.14.2-2.win32-py2.6.exe<http://ftp.gnome.org/pub/GNOME/binaries/win32/pygobject/2.14/pygobject-2.14.2-2.win32-py2.6.exe>
 3. Install libpng<http://downloads.sourceforge.net/gnuwin32/libpng-1.2.37-setup.exe>and
 freetype <http://gnuwin32.sourceforge.net/downlinks/freetype.php> using
 the installers (also includes the zlib dll). They will default to install
 to c:\Program Files\GnuWin32 . This is fine, but note the location.
 4. Either checkout the MPL source from subversion, or download a
tarball<http://matplotlib.svn.sourceforge.net/viewvc/matplotlib/trunk/matplotlib/?view=tar>and
unpack it
 5. Download the win32_static
files<http://matplotlib.sourceforge.net/win32_static_vs.tar.gz>,
 and unpack them to the place where your MPL source is, you should then have
 a folder in the MPL source folder called win32_static
 6. Open the file setupext.py in the MPL source folder
 7. Edit line 51 (or so) to read:: 'win32' : ['win32_static','c:\\Program
 Files\\GnuWin32']
 8. Install Visual Studio 2008
Express<http://www.microsoft.com/express/Downloads/#2008-Visual-CPP>if
you do not have a version of Visual Studio 2008 on your computer
 9. Open a console and type:: python setup.py build --compiler=msvc
 bdist_wininst
 10. Wait for it to finish, then:: python setup.py install to install MPL
 11. If everything worked right, you should be able to open a python shell
 and type
 >> import matplotlib
 >> matplotlib.use('GTKAgg')
 >> import pylab
 >> pylab.plot(0,4,'s')
 >> pylab.show()
 12. And you should see a GTK rendered plot window with a small square in
 the center hopefully
Improvements to this procedure would be greatly appreciated - but this
finally seems to work..
Regards,
Ian
----
Ian Bell
Graduate Research Assistant
Herrick Labs
Purdue University
email: ib...@pu...
cell: (607)227-7626