Hi,

> What kind of outputs can these backends create?

The Mac OS X backend can create PDFs, but it simply uses the pdf backend to do so, so that wouldn't help you.
The cairo backend can create PDFs by using cairo, so that could be worth trying.

> Could make a simple speed comparison between these backends
> and the original script that uses the PDF backend.

That would be useful, but keep in mind that there would be three options to compare:
1) The current PDF backend;
2) A modified PDF backend;
3) The cairo backend creating PDFs.
Since we don't have 2) yet, we cannot do the full comparison yet, but still it would be good to know if it is faster to create PDFs by using cairo compared to the current PDF backend.

> I am assuming the changes you mention require quite some work
> to make the PDFbackend running faster.

I think it is not so bad, since it's mainly a matter of removing the stuff from the PDF backend that is no longer needed. Do we have a maintainer for the PDF backend? Because I would rather rely on him/her to make the changes to this backend. Otherwise, I can give it a try, but probably I won't be able to find the time for it within this month.

Best,
-Michiel.



--- On Sat, 7/7/12, Gökhan Sever <gokhansever@gmail.com> wrote:

From: Gökhan Sever <gokhansever@gmail.com>
Subject: Re: [Matplotlib-users] Accelerating PDF saved plots
To: "Michiel de Hoon" <mjldehoon@yahoo.com>
Cc: matplotlib-users@lists.sourceforge.net
Date: Saturday, July 7, 2012, 9:05 PM

Hi,

What kind of outputs can these backends create? I don't use MAC, so my question is particularly for the Cairo backend. Could make a simple speed comparison between these backends and the original script that uses the PDF backend. I am assuming the changes you mention require quite some work to make the PDFbackend running faster.

Thanks.

On Sat, Jul 7, 2012 at 9:40 AM, Michiel de Hoon <mjldehoon@yahoo.com> wrote:
One reason behind the lengthy plot creation times is likely the PDF backend itself.

Whereas the Mac OS X and the Cairo backends make use of new_gc and gc.restore to keep track of the graphics context, the PDF backend uses check_gc and an internal stack of graphics contexts. Since nowadays matplotlib has gc.restore functionality, I don't think that that is needed any more.

See this revision for when gc.restore was added to matplotlib:

http://matplotlib.svn.sourceforge.net/viewvc/matplotlib?view=revision&revision=7112

In the same revision the Mac OS X and Cairo backends were modified to make use of gc.restore. The PDF backend (and the postscript backend also, btw) can be simplified in the same way to speed up these backends, as well as to reduce the output file sizes.

Best,
-Michiel.

--- On Thu, 7/5/12, Gökhan Sever <gokhansever@gmail.com> wrote:

From: Gökhan Sever <gokhansever@gmail.com>
Subject: Re: [Matplotlib-users] Accelerating PDF saved plots
To: "Benjamin Root" <ben.root@ou.edu>
Cc: matplotlib-users@lists.sourceforge.net
Date: Thursday, July 5, 2012, 2:11 PM



38 * 16 = 608
80 / 608 = 0.1316 seconds per plot

At this point, I doubt you are going to get much more speed-ups.  Glad to be of help!

Fabrice -- Good suggestion!  I should have thought of that given how much I use that technique in doing animation.

Ben Root


I am including profiled runs for the records --only first 10 lines to keep e-mail shorter. Total times are longer comparing to the raw run -p executions. I believe profiled run has its own call overhead.

I1 run -p test_speed.py
 171889738 function calls (169109959 primitive calls) in 374.311 seconds

   Ordered by: internal time

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
  4548012   34.583    0.000   34.583    0.000 {numpy.core.multiarray.array}
  1778401   21.012    0.000   46.227    0.000 path.py:86(__init__)
   521816   17.844    0.000   17.844    0.000 artist.py:74(__init__)
  2947090   15.432    0.000   15.432    0.000 weakref.py:243(__init__)
  1778401    9.515    0.000    9.515    0.000 {method 'all' of 'numpy.ndarray' objects}
 13691669    8.654    0.000    8.654    0.000 {getattr}
  1085280    8.550    0.000   17.629    0.000 core.py:2749(_update_from)
  1299904    7.809    0.000   76.060    0.000 markers.py:115(_recache)
       38    7.378    0.194    7.378    0.194 {gc.collect}
 13564851    6.768    0.000    6.768    0.000 {isinstance}




I1 run -p test_speed3.py
 61658708 function calls (60685172 primitive calls) in 100.934 seconds

   Ordered by: internal time

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
   937414    6.638    0.000    6.638    0.000 {numpy.core.multiarray.array}
   374227    4.377    0.000    7.500    0.000 path.py:198(iter_segments)
  6974613    3.866    0.000    3.866    0.000 {getattr}
   542640    3.809    0.000    7.900    0.000 core.py:2749(_update_from)
   141361    3.665    0.000    7.136    0.000 transforms.py:99(invalidate)
324688/161136    2.780    0.000   27.747    0.000 transforms.py:1729(transform)
    64448    2.753    0.000   64.921    0.001 lines.py:463(draw)
   231195    2.748    0.000    7.072    0.000 path.py:86(__init__)
684970/679449    2.679    0.000    3.888    0.000 backend_pdf.py:128(pdfRepr)
    67526    2.651    0.000    7.522    0.000 backend_pdf.py:1226(pathOperations)



--
Gökhan

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