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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