Hmm, how can I test this change the easiest way?

Clone the master and replace with your changes? or can I directly clone your experimental branch?


On Wed, May 16, 2012 at 8:52 AM, Michael Droettboom <mdroe@stsci.edu> wrote:
I have a proposed solution here:

https://github.com/matplotlib/matplotlib/pull/872

Git bisect found that the first commit where this happens was here:

https://github.com/matplotlib/matplotlib/commit/4cd75cdf

This is the script I used to reproduce -- I assume it's the same thing you're seeing:

from matplotlib import pyplot as plt
import numpy as np

x = np.linspace(0, 3.14 * 2, 3000)
y = np.sin(x)
x[::100] = np.nan
plt.plot(x, y)
plt.ylim(-0.25, 0.25)
plt.show()

Mike


On 05/16/2012 10:44 AM, Gökhan Sever wrote:
Hi Mike,

Could you inform me about your progress? I can test your sample script. I was thinking to test from v1.1.x branch downwards to spot the source of the issue, but I just don't know how to clone at particular commit in git.

Thank you.

On Wed, May 16, 2012 at 6:51 AM, Michael Droettboom <mdroe@stsci.edu> wrote:
Nevermind -- I've got something to reproduce this and am looking into it now.

Mike


On 05/16/2012 08:13 AM, Michael Droettboom wrote:
On 05/15/2012 07:57 PM, Gökhan Sever wrote:
Hello,

I have encountered a weird plotting issue recently using a recent mpl clone. See the linked pdfs for better demonstration of the issue:

http://atmos.uwyo.edu/~gsever/data/vocals_RF04_NU05_oldmpl.pdf


newmpl file is created using the latest master branch (cloned and setup today)

Scroll down to page 4 in each file and you will see the wrong plotted behavior of alwp_lcl (black line) variable on newmpl file comparing to the correct version that is shown on oldmpl. 

I was trying to figure out a way to correct this and I raised y-axis max to 2400 and then the line looks fine. However I have other data that show similar wrong behaviors, so I decided to try earlier mpl versions since I know that those plots were looking correct earlier (at least a few months back). Trying v1.1.x branch gave me the same results. Note that these data contain "nans". Are nan handling changed in recent mpl code or the way the data is plotted out of margins? I can't reproduce this with synthetic data.

There have been changes to that code lately.  Is there any way you can pack up a small script and data to reproduce this?  Then I can poke at it and see what I find (it would also make a good regression test).

Mike


------------------------------------------------------------------------------
Live Security Virtual Conference
Exclusive live event will cover all the ways today's security and 
threat landscape has changed and how IT managers can respond. Discussions 
will include endpoint security, mobile security and the latest in malware 
threats. http://www.accelacomm.com/jaw/sfrnl04242012/114/50122263/


_______________________________________________
Matplotlib-users mailing list
Matplotlib-users@lists.sourceforge.net
https://lists.sourceforge.net/lists/listinfo/matplotlib-users


------------------------------------------------------------------------------
Live Security Virtual Conference
Exclusive live event will cover all the ways today's security and
threat landscape has changed and how IT managers can respond. Discussions
will include endpoint security, mobile security and the latest in malware
threats. http://www.accelacomm.com/jaw/sfrnl04242012/114/50122263/
_______________________________________________
Matplotlib-users mailing list
Matplotlib-users@lists.sourceforge.net
https://lists.sourceforge.net/lists/listinfo/matplotlib-users




--
Gökhan




--
Gökhan