According to lsprofcalltree, the slowness appears to be entirely in the
units code by a wide margin -- which is unfortunately code I understand
very little about. The difference in timing before and after adding the
line to the axes appears to be because the unit conversion is not
invalidated
Hey all,
I hope this is the right list for this sort of thing, but here goes.
My installation of matplotlib (via macports) bombed out with this
error:
Traceback (most recent call last):
File "setup.py", line 125, in
if check_for_tk() or (options['build_tkagg'] is True):
File
"/opt/local
On Tue, Oct 7, 2008 at 9:18 AM, Michael Droettboom <[EMAIL PROTECTED]> wrote:
> According to lsprofcalltree, the slowness appears to be entirely in the
> units code by a wide margin -- which is unfortunately code I understand very
> little about. The difference in timing before and after adding th
This isn't quite what I was suggesting (and seems to be equivalent to
the code as before). In the common case where there are no units in the
data, this will still traverse the entire list.
I think replacing the whole loop with:
converter = self.get_converter(iter(x).next())
would be even b
Eric Firing wrote:
> 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(20)
> yy = np.random.rand(20)
> #plot(x
On Tue, Oct 7, 2008 at 11:26 AM, Michael Droettboom <[EMAIL PROTECTED]> wrote:
> This isn't quite what I was suggesting (and seems to be equivalent to
> the code as before). In the common case where there are no units in the
> data, this will still traverse the entire list.
>
> I think replacing t
Sorry. I didn't read carefully enough. That's right -- the "if
converter: break" was replaced with "return converter".
You're right. This is fine.
Mike
John Hunter wrote:
> On Tue, Oct 7, 2008 at 11:26 AM, Michael Droettboom <[EMAIL PROTECTED]> wrote:
>
>> This isn't quite what I was sugg
Michael Droettboom wrote:
Eric Firing wrote:
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(20)
yy = np.random.rand(200
