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 <gokhanse...@gmail.com> wrote:
From: Gökhan Sever <gokhanse...@gmail.com>
Subject: Re: [Matplotlib-users] Accelerating PDF saved plots
To: "Michiel de Hoon" <mjldeh...@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 <mjldeh...@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 <gokhanse...@gmail.com> wrote:
From: Gökhan Sever <gokhanse...@gmail.com>
Subject: Re: [Matplotlib-users] Accelerating PDF saved plots
To: "Benjamin Root" <ben.r...@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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