Hi,
I have some performance problems when plotting several lines and would
appreciate some comments. My application plots lots of lines (~5000)
of different sizes. The performance bottleneck lies in the following
code snippet:
for s in data.layout.segment:
x = []
y = []
for p in s.part:
for px, py in p.curve_points():
x.append(px)
y.append(py)
axes.plot(x, y, 'g', label = '_nolegend_')
Profiling showed that half of the time was spent in parsing the plot
arguments and most of the other half was spent in
Axes._set_artist_props.
I could speed up the application by using Line2D and
Axes.add_lines. But the only way to come around the time spent in
Axes._set_artist_props that I could come up with is this ugly hack
where I only call Axes.add_line for the first line and after that use
copies that are added directly to Axes.lines.
org_line = None
for s in data.layout.segment:
x = []
y = []
for p in s.part:
for px, py in p.curve_points():
x.append(px)
y.append(py)
if not org_line:
org_line = matplotlib.lines.Line2D(numpy.array(x), numpy.array(y),
color='green', label = '_nolegend_')
axis.add_line(org_line)
else:
line = copy.copy(org_line)
line.set_xdata(numpy.array(x))
line.set_ydata(numpy.array(y))
axis.lines.append(line)
Is there a cleaner way to do this?
Also, my feelings is that matplotlib 1.0 is slower with my original
code than previous version. But I have no numbers to back it up with.
regards
Ulf Larsson
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