Mike, John,

Because path simplification does not work with anything but a continuous 
line, it is turned off if there are any nans in the path.  The result is 
that if one does this:

import numpy as np
xx = np.arange(200000)
yy = np.random.rand(200000)
#plot(xx, yy)
yy[1000] = np.nan
plot(xx, yy)

the plot fails with an incomplete rendering and general 
unresponsiveness; apparently some mysterious agg limit is quietly 
exceeded.  With or without the nan, this test case also shows the 
bizarre slowness of add_line that I asked about in a message yesterday, 
and that has me completely baffled.

Both of these are major problems for real-world use.

Do you have any thoughts on timing and strategy for solving this 
problem?  A few weeks ago, when the problem with nans and path 
simplification turned up, I tried to figure out what was going on and 
how to fix it, but I did not get very far.  I could try again, but as 
you know I don't get along well with C++.

I am also wondering whether more than straightforward path 
simplification with nan/moveto might be needed.  Suppose there is a 
nightmarish time series with every third point being bad, so it is 
essentially a sequence of 2-point line segments.  The simplest form of 
path simplification fix might be to reset the calculation whenever a 
moveto is encountered, but this would yield no simplification in this 
case.  I assume Agg would still choke. Is there a need for some sort of 
automatic chunking of the rendering operation in addition to path 
simplification?

Thanks.

Eric

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