John Hunter wrote:
> On Mon, Dec 29, 2008 at 8:44 AM, Michael Droettboom wrote:
>
>> This is now fixed in SVN HEAD.
>>
>> Two changes were made:
>>
>> a) Be more conservative about when segments are simplified based on their
>> length
>>
>> b) Honor the (already existing) path.simplify rcParam
On Mon, Dec 29, 2008 at 8:44 AM, Michael Droettboom wrote:
> This is now fixed in SVN HEAD.
>
> Two changes were made:
>
> a) Be more conservative about when segments are simplified based on their
> length
>
> b) Honor the (already existing) path.simplify rcParam in the *Agg backends.
I didn't se
This is now fixed in SVN HEAD.
Two changes were made:
a) Be more conservative about when segments are simplified based on
their length
b) Honor the (already existing) path.simplify rcParam in the *Agg backends.
John's suggested patch is also a valid workaround, if you don't want to
track SVN.
You can hold off on updating. I am actually able to see it now, even on
SVN HEAD. I'll look further and see if I can find a workaround.
Cheers,
Mike
Kaushik Ghose wrote:
> Hi Mike,
>
> I'm using 0.98.3 with the TkAgg backend on Mac OS X.
>
> I will update matplotlib from the site and try again
Hi Mike,
I'm using 0.98.3 with the TkAgg backend on Mac OS X.
I will update matplotlib from the site and try again. My attempt to use GtkAgg
failed presumably because I don't have things set up with GTk on my Mac.
best
-Kaushik
Michael Droettboom wrote:
> I'm not able to reproduce this on matp
I'm not able to reproduce this on matplotlib SVN head with the GtkAgg
backend. Which version and backend are you using?
Mike
Kaushik Ghose wrote:
> PS. In the code just disregard the line N = 1000 - it does nothing.
>
> Ghose, Kaushik wrote:
>
>> Hi John,
>>
>> OK. I've managed to pare it do
PS. In the code just disregard the line N = 1000 - it does nothing.
Ghose, Kaushik wrote:
> Hi John,
>
> OK. I've managed to pare it down to the following pattern:
>
> import pylab
>
> N = 1000
> x = pylab.zeros(200)
> x[1] = .5
> x[2:24] = 1.0
> x[24] = .5
> x[26] = -.5
> x[27:49] = -1.0
> x[4
Hi John,
OK. I've managed to pare it down to the following pattern:
import pylab
N = 1000
x = pylab.zeros(200)
x[1] = .5
x[2:24] = 1.0
x[24] = .5
x[26] = -.5
x[27:49] = -1.0
x[49] = -.5
x = pylab.tile(x, 100)
pylab.plot(x)
The above code is sufficient to repeat the glitch (just resize the wind
On Sat, Dec 27, 2008 at 10:29 AM, Kaushik Ghose
wrote:
> Hi Gang,
>
> I was plotting some data collected from an ADC and noticed an odd aliasing
> issue. Please see the images on the following site.
>
> http://assorted-experience.blogspot.com/2008/12/odd-aliasing-issue-with-matplotlib.html
>
> I w
Hi Gang,
I was plotting some data collected from an ADC and noticed an odd aliasing
issue. Please see the images on the following site.
http://assorted-experience.blogspot.com/2008/12/odd-aliasing-issue-with-matplotlib.html
I wonder if there is any way to avoid this kind of aliasing. I vaguely
10 matches
Mail list logo
