matplotlib-devel Mailing List for matplotlib

John Hunter wrote:
> For Fernando's test case, I was able to reduce the typical run time
> from 1.02s to 0.2s for PNGs and 1.35s to 0.26s for PS . Here are my
> notes on the various changes and the gains they brought. With
> collections, I think we could halve this again for PS, or at the very
> least get this down to agg speeds.
This is great, many thanks for working on this. For reference, here's what I 
was getting before:
[logtest]> ./logtest.py
Loading data...
pylab times...
plot: 3.38 s
png : 2.57 s
eps : 3.45 s
gnuplot...
plot: 0.16 s
eps : 0.06 s
Plot sizes:
-rw-r--r-- 1 fperez wavelet 27196 Feb 5 17:19 fig_gnuplot.eps
-rw-r--r-- 1 fperez wavelet 575701 Feb 5 17:19 fig_pylab.eps
-rw-r--r-- 1 fperez wavelet 33057 Feb 5 17:19 fig_pylab.png
and these are the current results:
[logtest]> ./logtest.py
Loading data...
pylab times...
plot: 0.70 s
png : 0.46 s
eps : 0.74 s
gnuplot...
plot: 0.02 s
eps : 0.21 s
Plot file info:
-rw-r--r-- 1 fperez wavelet 27196 Feb 6 15:19 fig_gnuplot.eps
-rw-r--r-- 1 fperez wavelet 417598 Feb 6 15:19 fig_pylab.eps
-rw-r--r-- 1 fperez wavelet 31550 Feb 6 15:19 fig_pylab.png
Note also how the pylab filesizes went down a bit, probably thanks to not 
having all the extra minor ticks.
For interactive work (which measures the time for the show() call, hence is 
higher than above), the improvement is also very noticeable:
In [6]: timing(fun2.plot_line,ptype='error') # pylab-based
Out[6]: 2.5686090000000004
In [7]: timing(fun2.plot_line_gp,ptype='error') # gnuplot-based
Out[7]: 0.56791400000000003
We're now within a factor of 5 of gnuplot (yesterday it was about 10x)
> and here is the final profiler output of the newly optimized code
> 
> 
> 58247 function calls (58081 primitive calls) in 0.980 CPU seconds
Wow, that's a lot of function calls... Given the cost of python function 
calls, I suspect that a major further improvement would require some manual 
inlining of some of this to reduce this number significantly. I'm not sure 
how worth this would be doing, given the cost in readability/architecture it 
may have. Just as a reference for you, here's the time for 58000 empty calls 
with one argument on my laptop (timings() is in IPython.genutils):
In [17]: def nothing(x):
 ....: pass
 ....:
In [18]: timings(58000,nothing,5)
Out[18]: (0.21196699999999957, 3.6546034482758548e-06)
 ^^ total time ^^ ^^ time per call ^^
But as far as I'm concerned, this is already much, much better than just a few 
days ago. Between the major fixes to correctness of log plots, and these 
performance improvements, I am very happy.
Many thanks for this effort. For those of us who spend a lot of time making 
log plots, this is great.
Best,
f

Fernando sent me some test scripts and profile information which set
me on a path to enhance log plot performance. The biggest hit was
coming from the large number of tick labels which were formatted as
mathtext. mathtext is faster now than it used to be, but it is still
slow. I emailed the pyparsing Paul Mcguire and he's going to try and
take another look for some performance enhancements.
But superscript layout for mathtext is relatively easy, eg 10ドル^{-4}$
and so I special-cased this in the text module using a regex and did
the layout "by hand". I also changed the default behavior of the subs
property in the log tick locator. The value None now means to
autosub. If the number of decades is small, you'll get full subbing
for minor ticks as before. If the number of decades is large, you'll
get no minor ticks. You can override the default by explicitly
setting subs in semilogx and friends.
I also inlined some of the num -> string conversions in backend ps.
These were being done by a function that called another function and
the profiler indicated we were paying a big hit.
There are more big gains to be had -- eg backend ps could implement
the various collection draw methods and the lines module could use
collections for drawing markers. But that is for another day.
For Fernando's test case, I was able to reduce the typical run time
from 1.02s to 0.2s for PNGs and 1.35s to 0.26s for PS . Here are my
notes on the various changes and the gains they brought. With
collections, I think we could halve this again for PS, or at the very
least get this down to agg speeds.
Baseline
 plot: 1.15 s
 png : 1.02 s
 eps : 1.35 s
Skipping layout of empty strings
 plot: 0.8 s
 png : 0.67 s
 eps : 1.02 s
Using dot rather than Matrix
 plot: 0.74 s
 png : 0.62 s
 eps : 0.97 s
Optimizing super script drawing by skipping mathtext parser
 png : 0.46 s
 eps : 0.62 s
Implemented autosubbing to reduce the number of subticks for large
decade ranges
 png : 0.21 s
 eps : 0.36 s
Inline num2str backend ps draw_lines and draw_line
 png : 0.20 s
 eps : 0.26 s
and here is the final profiler output of the newly optimized code
 58247 function calls (58081 primitive calls) in 0.980 CPU seconds
 Ordered by: standard name
 ncalls tottime percall cumtime percall filename:lineno(function)
 3/1 0.000 0.000 0.980 0.980 <string>:1(?)
 1 0.000 0.000 0.000 0.000 ArrayPrinter.py:7(?)
 1 0.000 0.000 0.000 0.000 FFT.py:21(?)
 1 0.000 0.000 0.000 0.000 LinearAlgebra.py:14(LinAlgError)
 1 0.010 0.010 0.010 0.010 LinearAlgebra.py:6(?)
 1 0.000 0.000 0.000 0.000 MLab.py:9(?)
 1 0.010 0.010 0.020 0.020 Matrix.py:1(?)
 1 0.000 0.000 0.000 0.000 Matrix.py:66(Matrix)
 710 0.000 0.000 0.000 0.000 Numeric.py:130(asarray)
 131 0.000 0.000 0.000 0.000 Numeric.py:146(repeat)
 1 0.000 0.000 0.000 0.000 Numeric.py:492(Unpickler)
 1 0.000 0.000 0.000 0.000 Numeric.py:499(Pickler)
 131 0.000 0.000 0.000 0.000 Numeric.py:561(nonzero)
 127 0.010 0.000 0.010 0.000 Numeric.py:593(ones)
 1 0.010 0.010 0.030 0.030 Numeric.py:85(?)
 5 0.000 0.000 0.000 0.000 Precision.py:15(_get_precisions)
 1 0.000 0.000 0.000 0.000 Precision.py:21(_fill_table)
 1 0.000 0.000 0.000 0.000 Precision.py:28(PrecisionError)
 23 0.000 0.000 0.000 0.000 Precision.py:31(_lookup)
 1 0.000 0.000 0.000 0.000 Precision.py:7(?)
 1 0.000 0.000 0.000 0.000 RandomArray.py:1(?)
 1 0.000 0.000 0.000 0.000 RandomArray.py:13(ArgumentError)
 1 0.000 0.000 0.000 0.000 RandomArray.py:16(seed)
 1 0.000 0.000 0.000 0.000 StringIO.py:30(?)
 1 0.000 0.000 0.000 0.000 StringIO.py:38(StringIO)
 1 0.000 0.000 0.000 0.000 UTC.py:1(?)
 1 0.000 0.000 0.000 0.000 UTC.py:5(UTC)
 1 0.000 0.000 0.000 0.000 UserArray.py:1(?)
 1 0.000 0.000 0.000 0.000 UserArray.py:4(UserArray)
 2 0.000 0.000 0.000 0.000 UserDict.py:19(__getitem__)
 9 0.000 0.000 0.000 0.000 UserDict.py:41(has_key)
 1 0.000 0.000 0.000 0.000 __future__.py:48(?)
 1 0.000 0.000 0.000 0.000 __future__.py:66(_Feature)
 3 0.000 0.000 0.000 0.000 __future__.py:67(__init__)
 1 0.000 0.000 0.000 0.000 __init__.py:0(?)
 5 0.000 0.000 0.050 0.010 __init__.py:1(?)
 1 0.000 0.000 0.000 0.000 __init__.py:12(?)
 1 0.000 0.000 0.050 0.050 __init__.py:15(?)
 1 0.000 0.000 0.000 0.000 __init__.py:187(Verbose)
 1 0.000 0.000 0.000 0.000 __init__.py:206(__init__)
 1 0.000 0.000 0.000 0.000 __init__.py:21(?)
 2 0.000 0.000 0.000 0.000 __init__.py:211(set_level)
 2279 0.010 0.000 0.020 0.000 __init__.py:220(report)
 1 0.000 0.000 0.000 0.000 __init__.py:243(wrap)
 4 0.000 0.000 0.000 0.000 __init__.py:253(wrapper)
 2279 0.010 0.000 0.010 0.000 __init__.py:264(ge)
 1 0.000 0.000 0.000 0.000 __init__.py:27(timezone)
 2 0.000 0.000 0.000 0.000 __init__.py:271(get_home)
 4 0.000 0.000 0.000 0.000 __init__.py:290(_get_data_path)
 1 0.010 0.010 0.010 0.010 __init__.py:30(?)
 9 0.000 0.000 0.000 0.000 __init__.py:333(validate_bool)
 12 0.000 0.000 0.000 0.000 __init__.py:341(validate_float)
 1 0.000 0.000 0.000 0.000 __init__.py:347(validate_int)
 1 0.000 0.000 0.000 0.000 __init__.py:353(validate_numerix)
 1 0.000 0.000 0.000 0.000 __init__.py:361(validate_toolbar)
 1 0.000 0.000 0.000 0.000 __init__.py:372(validate_nseq_float)
 1 0.000 0.000 0.000 0.000 __init__.py:373(__init__)
 1 0.000 0.000 0.000 0.000 __init__.py:375(__call__)
 15 0.000 0.000 0.000 0.000 __init__.py:384(validate_color)
 196 0.000 0.000 0.000 0.000 __init__.py:40(dot)
 5 0.000 0.000 0.000 0.000 __init__.py:414(validate_comma_sep_str)
 4 0.000 0.000 0.000 0.000 __init__.py:428(validate_fontsize)
 1 0.000 0.000 0.000 0.000 __init__.py:438(validate_verbose)
 1 0.000 0.000 0.000 0.000 __init__.py:443(validate_verbose_fileo)
 1 0.000 0.000 0.000 0.000 __init__.py:458(validate_verbose_erro)
 4 0.000 0.000 0.000 0.000 __init__.py:48(draw_if_interactive)
 1 0.000 0.000 0.000 0.000 __init__.py:49(show)
 1 0.000 0.000 0.000 0.000 __init__.py:578(matplotlib_fname)
 1 0.000 0.000 0.000 0.000 __init__.py:6(?)
 1 0.010 0.010 0.010 0.010 __init__.py:615(rc_params)
 1 0.000 0.000 0.000 0.000 __init__.py:63(_munge_zone)
 2 0.000 0.000 0.000 0.000 __init__.py:683(rc)
 2 0.000 0.000 0.000 0.000 __init__.py:803(get_backend)
 1 0.000 0.000 0.000 0.000 __init__.py:839(MPLError)
 1 0.000 0.000 0.000 0.000 __init__.py:9(?)
 1 0.000 0.000 0.000 0.000 _contour.py:1(?)
 1 0.000 0.000 0.000 0.000 _image.py:1(?)
 1 0.000 0.000 0.000 0.000 _mathtext_data.py:3(?)
 1 0.000 0.000 0.000 0.000 _nc_imports.py:1(?)
 1 0.000 0.000 0.000 0.000 _nc_imports.py:5(_TypeNamespace)
 1 0.000 0.000 0.000 0.000 _pylab_helpers.py:1(?)
 1 0.000 0.000 0.000 0.000 _pylab_helpers.py:11(get_fig_manager)
 1 0.000 0.000 0.000 0.000 _pylab_helpers.py:37(get_all_fig_managers)
 12 0.000 0.000 0.000 0.000 _pylab_helpers.py:46(get_active)
 1 0.000 0.000 0.000 0.000 _pylab_helpers.py:52(set_active)
 1 0.000 0.000 0.000 0.000 _pylab_helpers.py:6(Gcf)
 1 0.000 0.000 0.000 0.000 _transforms.py:1(?)
 1 0.000 0.000 0.000 0.000 afm.py:257(AFM)
 1 0.000 0.000 0.000 0.000 afm.py:35(?)
 1 0.000 0.000 0.000 0.000 artist.py:1(?)
 2 0.000 0.000 0.000 0.000 artist.py:146(get_label)
 2 0.000 0.000 0.000 0.000 artist.py:148(set_label)
 182 0.000 0.000 0.000 0.000 artist.py:166(update_from)
 1 0.000 0.000 0.000 0.000 artist.py:23(Artist)
 281 0.000 0.000 0.040 0.000 artist.py:30(__init__)
 3 0.000 0.000 0.000 0.000 artist.py:42(is_transform_set)
 233 0.000 0.000 0.000 0.000 artist.py:46(set_transform)
 3 0.000 0.000 0.000 0.000 artist.py:55(get_transform)
 284 0.000 0.000 0.000 0.000 artist.py:66(set_figure)
 46 0.000 0.000 0.000 0.000 artist.py:74(set_clip_box)
 177 0.000 0.000 0.000 0.000 artist.py:90(get_visible)
 468 0.000 0.000 0.000 0.000 artist.py:94(get_clip_on)
 1 0.010 0.010 0.080 0.080 axes.py:1(?)
 1 0.000 0.000 0.000 0.000 axes.py:113(_process_plot_var_args)
 3 0.000 0.000 0.680 0.227 axes.py:1237(draw)
 2 0.000 0.000 0.000 0.000 axes.py:127(__init__)
 2 0.000 0.000 0.000 0.000 axes.py:131(_clear_color_cycle)
 2 0.000 0.000 0.000 0.000 axes.py:146(__call__)
 1 0.000 0.000 0.000 0.000 axes.py:1505(get_ylim)
 2 0.000 0.000 0.000 0.000 axes.py:151(set_lineprops)
 2 0.000 0.000 0.000 0.000 axes.py:1525(grid)
 2 0.000 0.000 0.000 0.000 axes.py:1552(hold)
 1 0.000 0.000 0.000 0.000 axes.py:170(is_filled)
 1 0.000 0.000 0.000 0.000 axes.py:1706(legend)
 2 0.000 0.000 0.000 0.000 axes.py:1771(get_handles)
 1 0.000 0.000 0.000 0.000 axes.py:195(_plot_2_args)
 2 0.000 0.000 0.010 0.005 axes.py:2227(plot)
 1 0.000 0.000 0.000 0.000 axes.py:235(_plot_3_args)
 4 0.000 0.000 0.000 0.000 axes.py:260(_grab_next_args)
 1 0.000 0.000 0.000 0.000 axes.py:2694(semilogy)
 2 0.000 0.000 0.000 0.000 axes.py:2768(_send_xlim_event)
 2 0.000 0.000 0.000 0.000 axes.py:2772(_send_ylim_event)
 2 0.000 0.000 0.000 0.000 axes.py:2777(set_xlim)
 2 0.000 0.000 0.000 0.000 axes.py:2860(set_ylim)
 1 0.000 0.000 0.000 0.000 axes.py:2872(set_yscale)
 4 0.000 0.000 0.000 0.000 axes.py:289(makeValue)
 1 0.010 0.010 0.010 0.010 axes.py:297(Axes)
 2 0.000 0.000 0.000 0.000 axes.py:2971(ishold)
 1 0.000 0.000 0.010 0.010 axes.py:313(__init__)
 1 0.000 0.000 0.000 0.000 axes.py:3299(SubplotBase)
 1 0.000 0.000 0.000 0.000 axes.py:3314(__init__)
 1 0.000 0.000 0.000 0.000 axes.py:3369(Subplot)
 1 0.000 0.000 0.010 0.010 axes.py:3383(__init__)
 1 0.000 0.000 0.000 0.000 axes.py:3389(PolarAxes)
 1 0.000 0.000 0.010 0.010 axes.py:344(_init_axis)
 1 0.000 0.000 0.000 0.000 axes.py:349(set_figure)
 1 0.000 0.000 0.000 0.000 axes.py:3704(PolarSubplot)
 1 0.000 0.000 0.000 0.000 axes.py:375(_set_lim_and_transforms)
 1 0.000 0.000 0.010 0.010 axes.py:408(axhline)
 1 0.000 0.000 0.000 0.000 axes.py:50(_process_plot_format)
 2 0.000 0.000 0.000 0.000 axes.py:592(has_data)
 3 0.000 0.000 0.000 0.000 axes.py:601(_set_artist_props)
 1 0.000 0.000 0.000 0.000 axes.py:608(cla)
 2 0.000 0.000 0.000 0.000 axes.py:681(update_datalim)
 2 0.010 0.005 0.010 0.005 axes.py:689(add_line)
 1 0.000 0.000 0.000 0.000 axes.py:705(_get_verts_in_data_coords)
 2 0.000 0.000 0.000 0.000 axes.py:729(autoscale_view)
 69 0.000 0.000 0.000 0.000 axis.py:125(get_loc)
 69 0.000 0.000 0.160 0.002 axis.py:129(draw)
 69 0.000 0.000 0.000 0.000 axis.py:158(set_label1)
 69 0.000 0.000 0.000 0.000 axis.py:165(set_label2)
 220 0.000 0.000 0.000 0.000 axis.py:173(_set_artist_props)
 1 0.000 0.000 0.000 0.000 axis.py:183(XTick)
 12 0.000 0.000 0.020 0.002 axis.py:189(_get_text1)
 12 0.010 0.001 0.010 0.001 axis.py:215(_get_text2)
 1 0.000 0.000 0.000 0.000 axis.py:23(Tick)
 12 0.000 0.000 0.010 0.001 axis.py:236(_get_tick1line)
 12 0.000 0.000 0.000 0.000 axis.py:251(_get_tick2line)
 12 0.000 0.000 0.000 0.000 axis.py:266(_get_gridline)
 27 0.000 0.000 0.010 0.000 axis.py:282(update_position)
 27 0.000 0.000 0.000 0.000 axis.py:294(get_view_interval)
 1 0.000 0.000 0.060 0.060 axis.py:3(?)
 1 0.000 0.000 0.000 0.000 axis.py:303(YTick)
 32 0.000 0.000 0.000 0.000 axis.py:311(_get_text1)
 32 0.000 0.000 0.000 0.000 axis.py:333(_get_text2)
 32 0.000 0.000 0.020 0.001 axis.py:353(_get_tick1line)
 32 0.000 0.000 0.000 0.000 axis.py:367(_get_tick2line)
 32 0.000 0.000 0.000 0.000 axis.py:381(_get_gridline)
 42 0.000 0.000 0.000 0.000 axis.py:399(update_position)
 42 0.000 0.000 0.000 0.000 axis.py:412(get_view_interval)
 1 0.000 0.000 0.000 0.000 axis.py:421(Ticker)
 1 0.000 0.000 0.000 0.000 axis.py:426(Axis)
 2 0.000 0.000 0.010 0.005 axis.py:436(__init__)
 44 0.000 0.000 0.070 0.002 axis.py:44(__init__)
 4 0.000 0.000 0.010 0.003 axis.py:453(cla)
 6 0.000 0.000 0.000 0.000 axis.py:484(_set_artist_props)
 6 0.010 0.002 0.240 0.040 axis.py:488(draw)
 36 0.000 0.000 0.000 0.000 axis.py:595(_copy_tick_props)
 4 0.000 0.000 0.000 0.000 axis.py:610(get_major_locator)
 6 0.000 0.000 0.040 0.007 axis.py:626(get_major_ticks)
 6 0.000 0.000 0.020 0.003 axis.py:645(get_minor_ticks)
 4 0.000 0.000 0.000 0.000 axis.py:659(grid)
 5 0.000 0.000 0.000 0.000 axis.py:680(set_major_formatter)
 4 0.000 0.000 0.000 0.000 axis.py:689(set_minor_formatter)
 5 0.000 0.000 0.000 0.000 axis.py:699(set_major_locator)
 5 0.000 0.000 0.000 0.000 axis.py:709(set_minor_locator)
 1 0.000 0.000 0.000 0.000 axis.py:760(XAxis)
 12 0.000 0.000 0.040 0.003 axis.py:763(_get_tick)
 1 0.000 0.000 0.000 0.000 axis.py:766(_get_label)
 3 0.000 0.000 0.000 0.000 axis.py:801(_update_label_postion)
 11 0.000 0.000 0.000 0.000 axis.py:848(get_view_interval)
 8 0.000 0.000 0.000 0.000 axis.py:852(get_data_interval)
 1 0.000 0.000 0.000 0.000 axis.py:858(YAxis)
 32 0.000 0.000 0.030 0.001 axis.py:861(_get_tick)
 1 0.000 0.000 0.000 0.000 axis.py:865(_get_label)
 3 0.000 0.000 0.000 0.000 axis.py:902(_update_label_postion)
 14 0.000 0.000 0.000 0.000 axis.py:949(get_view_interval)
 11 0.000 0.000 0.000 0.000 axis.py:953(get_data_interval)
 1 0.000 0.000 0.000 0.000 backend_agg.py:100(__init__)
 1940 0.110 0.000 0.120 0.000 backend_agg.py:140(draw_line)
 78 0.010 0.000 0.020 0.000 backend_agg.py:185(draw_text)
 43 0.000 0.000 0.020 0.000 backend_agg.py:206(get_text_width_height)
 36 0.000 0.000 0.000 0.000 backend_agg.py:223(get_canvas_width_height)
 121 0.020 0.000 0.030 0.000 backend_agg.py:228(_get_agg_font)
 84 0.000 0.000 0.000 0.000 backend_agg.py:249(points_to_pixels)
 1 0.000 0.000 0.000 0.000 backend_agg.py:274(new_figure_manager)
 1 0.000 0.000 0.000 0.000 backend_agg.py:286(FigureCanvasAgg)
 2 0.000 0.000 0.320 0.160 backend_agg.py:297(draw)
 2 0.060 0.030 0.760 0.380 backend_agg.py:326(print_figure)
 1 0.010 0.010 0.020 0.020 backend_agg.py:70(?)
 1 0.000 0.000 0.000 0.000 backend_agg.py:93(RendererAgg)
 1 0.000 0.000 0.000 0.000 backend_bases.py:16(RendererBase)
 84 0.000 0.000 0.000 0.000 backend_bases.py:20(open_group)
 84 0.000 0.000 0.000 0.000 backend_bases.py:26(close_group)
 312 0.000 0.000 0.000 0.000 backend_bases.py:332(new_gc)
 1 0.000 0.000 0.000 0.000 backend_bases.py:339(GraphicsContextBase)
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 1037 0.000 0.000 0.000 0.000 backend_bases.py:397(get_dashes)
 1 0.000 0.000 0.000 0.000 backend_bases.py:4(?)
 1037 0.000 0.000 0.000 0.000 backend_bases.py:407(get_joinstyle)
 1037 0.000 0.000 0.000 0.000 backend_bases.py:420(get_linewidth)
 1076 0.000 0.000 0.000 0.000 backend_bases.py:426(get_rgb)
 36 0.000 0.000 0.000 0.000 backend_bases.py:43(flipy)
 276 0.000 0.000 0.000 0.000 backend_bases.py:433(set_alpha)
 201 0.000 0.000 0.000 0.000 backend_bases.py:440(set_antialiased)
 201 0.000 0.000 0.000 0.000 backend_bases.py:449(set_capstyle)
 144 0.000 0.000 0.000 0.000 backend_bases.py:458(set_clip_rectangle)
 256 0.000 0.000 0.000 0.000 backend_bases.py:464(set_dashes)
 42 0.000 0.000 0.000 0.000 backend_bases.py:47(points_to_pixels)
 468 0.000 0.000 0.020 0.000 backend_bases.py:472(set_foreground)
 60 0.000 0.000 0.000 0.000 backend_bases.py:493(set_joinstyle)
 393 0.000 0.000 0.000 0.000 backend_bases.py:502(set_linewidth)
 192 0.000 0.000 0.000 0.000 backend_bases.py:508(set_linestyle)
 1 0.000 0.000 0.000 0.000 backend_bases.py:521(Event)
 1 0.000 0.000 0.000 0.000 backend_bases.py:534(LocationEvent)
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 2 0.000 0.000 0.000 0.000 backend_bases.py:664(__init__)
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 1 0.000 0.000 0.000 0.000 backend_bases.py:751(mpl_connect)
 1 0.000 0.000 0.000 0.000 backend_bases.py:787(FigureManagerBase)
 1 0.000 0.000 0.000 0.000 backend_bases.py:795(__init__)
 1 0.000 0.000 0.000 0.000 backend_bases.py:821(Cursors)
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 1037 0.000 0.000 0.000 0.000 backend_ps.py:111(set_linewidth)
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>>>>> "Abraham" == Abraham Schneider <ab...@cn...> writes:
 Abraham> This might work as a compromise. Instead of using the
 Abraham> traits class, I wrote a quick and dirty Traits Lite(tm)
 Abraham> class. I think the traits class provided a lot more
 Abraham> functionality than was needed, and what was needed
 Abraham> required very little code.
OK, this is looking much more promising. I have given a quick glance
over and will test it more extensively this afternoon.
Quick comments. I'd like to continue using the matplotlib naming
convention (UpperCase) for class names, rather than underscore
separated
class trait_color(Trait):
also, 
I don't think we should use the name Trait or HasTrait, since that
will confuse people into thinking we are using enthought traits
(Property and HasProperty ?) 
Finally, another feature I would like to be able to support, which
won't be hard, is to enable the use of an RC class or something like
it in Artist constructors
Here is what we currently have
 def __init__(self, xdata, ydata,
 linewidth = None, # default to rc
 linestyle = None, # default to rc
 color = None, # default to rc
...snip..
 ):
This overloads None which leads to strange usages like
linestyle='None' to turn off lines (note the quotes). I would rather
do something like
 def __init__(self, xdata, ydata,
 linewidth = RC('lines.linewidth'),
 linestyle = RC('lines.linestyle')
...snip...
 ):
 if isinstance(linewidth, RC): linewidth=linewidth()
Note that this is different than
 def __init__(self, xdata, ydata,
 linewidth = rc.lines.linewidth,
 linestyle = rc.lines.linestyle,
...snip...
 ):
The syntax above is not the point. What I want to emphasize is that I
want a self documenting way of saying this parameter defaults to an rc
param, and that param should be evaluated *at init time*. If you can
do it with the second syntax, that looks better, but if I read your
code correctly, something like rc.lines.linewidth evaluates to a float
at module load time and so cannot be used as a sentinel at module init
time. Is this correct?
In any case, it looks very promising. Two final comments. Like
Fernando, I'm not too concerned about being able to parse existing rc
files. It's a minor burden to require people simply to take the new
format and customize it. Also, do you need to extend the fontsize
train to support fontsizes in points, eg 12.0 ?
 Abraham> Sorry for sending in such a large file once again. I
 Abraham> wanted to be able to parse the entire RC file to make
 Abraham> sure that things were going to work okay.
No problem, I think we can handle it.
 Abraham> It still needs some work, but it's pretty functional for
 Abraham> a proof of concept. It's capable of parsing the RC file,
 Abraham> validating the type-assignment, and uses the
 Abraham> 'rc.lines.color = xxx' format. It's also fairly modular,
 Abraham> so additions should be easy.
Well if it already parses existing rc, no reason to kill it.
I look forward to looking more closely at it -- thanks!
JDH

As noted here previously, PS/EPS files are 25 times larger than they
need to be in many cases because we embed the entire freetype font
into them. I think this is a problem that needs to be addressed, so
I'm posting here in hopes to motivate Paul, Jochen, or anyonw else
wanting to kill an afternoon to look into it.
Last time Paul looked at this (when he implemented freetype fonts for
PS mathtext), he came to the conclusion that it was not possible to
extract individual glyphs fro ps file, and had to resort to dumping in
the entire file. I don't believe this is the case. See, eg
agg22/font_freetype/agg_font_freetype.cpp in the matplotlib src distro
and the routine decompose_ft_outline in which a freetype glyph outline
is converted to an agg path (at least that is my read of it). We
could port this code to ft2font, and use it to embed the individual
glyph information in postscript, me thinks (caveat, I know nothing
about postscript font embedding).
The other idea, which I think is worth doing independently of the
embedding issue, is to support an rc option so that postscript uses
postscript fonts (eg what matplotlib used to do before we started
embedding using the supplied afm files for metrics and the AFM class
to parse them). For scripts that don't use mathtext, this is a
perfectly viable solution which would produce the minimal files, since
nothing has to be embedded.
But I think the embedding problem is the top priority. We can see
where we are vis-a-vis file sizes after that.
Any takers?
JDH`