>
>
>> 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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