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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