matplotlib-users Mailing List for matplotlib

On Wed, May 16, 2012 at 9:30 AM, Michael Droettboom <md...@st...> wrote:
>
>
> Or, in an existing clone of the main repository, add my fork as a remote
>
> git remote add mdboom git://github.com/mdboom/matplotlib.git
> git fetch mdboom
> git checkout mdboom/clipping-bug
>
Here are my steps following your 2nd suggestion:
1-) Cloned the master:
git clone git://github.com/matplotlib/matplotlib.git
2-) go into matplotlib dir and then execute:
sudo python setupegg.py develop
Tested my existing code and verified that the plotting error I reported in
the first message was still there.
3-) in the matplotlib dir I executed the 3 commands you typed to get your
fork
4-) Removed the build dir in matplotlib folder then re-executed setupegg.py
script
5-) Testing with your change my plot looks fine now, lines are drawn
correctly.
Thanks for easy to follow instructions and quick response.

On 05/16/2012 11:15 AM, Gökhan Sever wrote:
> 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?
You can either clone my fork and then checkout the branch with the change:
 git checkout clipping-bug
Or, in an existing clone of the main repository, add my fork as a remote
 git remote add mdboom git://github.com/mdboom/matplotlib.git
 git fetch mdboom
 git checkout mdboom/clipping-bug
Or, since the diff is only a few lines in path_converters.h, you could 
just apply it manually.
Be sure to remove your build directory before rebuilding: distutils 
doesn't pick up header file changes.
Mike
>
>
> On Wed, May 16, 2012 at 8:52 AM, Michael Droettboom <md...@st... 
> <mailto:md...@st...>> 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
>> <md...@st... <mailto:md...@st...>> 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_newmpl.pdf
>>>> <http://atmos.uwyo.edu/%7Egsever/data/vocals_RF04_NU05_newmpl.pdf>
>>>> http://atmos.uwyo.edu/~gsever/data/vocals_RF04_NU05_oldmpl.pdf
>>>> <http://atmos.uwyo.edu/%7Egsever/data/vocals_RF04_NU05_oldmpl.pdf>
>>>>
>>>>
>>>> newmpl file is created using the latest master branch
>>>> (cloned and setup today)
>>>> oldmpl is created using mpl v1.1.0
>>>> (https://github.com/downloads/matplotlib/matplotlib/matplotlib-1.1.0.tar.gz)
>>>>
>>>> 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
>>> Mat...@li... <mailto:Mat...@li...>
>>> 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
>> Mat...@li...
>> <mailto:Mat...@li...>
>> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>>
>>
>>
>>
>> -- 
>> Gökhan
>
>
>
>
> -- 
> Gökhan

Bisecting is definitely a better idea than my one-by-one setup iteration :)
Thanks for sharing the tip.
On Wed, May 16, 2012 at 8:54 AM, Michael Droettboom <md...@st...> wrote:
> On 05/16/2012 10:44 AM, Gökhan Sever wrote:
>
> 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.
>
>
> Also, to answer this question directly -- "git bisect" is a great way to
> find this:
>
> http://git-scm.com/book/en/Git-Tools-Debugging-with-Git#Binary-Search
>
> Cheers,
> Mike
>
>
>
> Thank you.
>
> On Wed, May 16, 2012 at 6:51 AM, Michael Droettboom <md...@st...>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_newmpl.pdf
>> http://atmos.uwyo.edu/~gsever/data/vocals_RF04_NU05_oldmpl.pdf
>>
>>
>> newmpl file is created using the latest master branch (cloned and setup
>> today)
>> oldmpl is created using mpl v1.1.0 (
>> https://github.com/downloads/matplotlib/matplotlib/matplotlib-1.1.0.tar.gz
>> )
>>
>> 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 lis...@li...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
>> Mat...@li...
>> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>>
>>
>
>
> --
> Gökhan
>
>
>
-- 
Gökhan

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 <md...@st...> 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 <md...@st...>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_newmpl.pdf
>> http://atmos.uwyo.edu/~gsever/data/vocals_RF04_NU05_oldmpl.pdf
>>
>>
>> newmpl file is created using the latest master branch (cloned and setup
>> today)
>> oldmpl is created using mpl v1.1.0 (
>> https://github.com/downloads/matplotlib/matplotlib/matplotlib-1.1.0.tar.gz
>> )
>>
>> 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 lis...@li...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
>> Mat...@li...
>> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>>
>>
>
>
> --
> Gökhan
>
>
>
-- 
Gökhan

On 05/16/2012 10:44 AM, Gökhan Sever wrote:
> 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.
Also, to answer this question directly -- "git bisect" is a great way to 
find this:
http://git-scm.com/book/en/Git-Tools-Debugging-with-Git#Binary-Search
Cheers,
Mike
>
> Thank you.
>
> On Wed, May 16, 2012 at 6:51 AM, Michael Droettboom <md...@st... 
> <mailto:md...@st...>> 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_newmpl.pdf
>>> <http://atmos.uwyo.edu/%7Egsever/data/vocals_RF04_NU05_newmpl.pdf>
>>> http://atmos.uwyo.edu/~gsever/data/vocals_RF04_NU05_oldmpl.pdf
>>> <http://atmos.uwyo.edu/%7Egsever/data/vocals_RF04_NU05_oldmpl.pdf>
>>>
>>>
>>> newmpl file is created using the latest master branch (cloned
>>> and setup today)
>>> oldmpl is created using mpl v1.1.0
>>> (https://github.com/downloads/matplotlib/matplotlib/matplotlib-1.1.0.tar.gz)
>>>
>>> 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
>> Mat...@li... <mailto:Mat...@li...>
>> 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
> Mat...@li...
> <mailto:Mat...@li...>
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>
>
>
>
> -- 
> Gökhan

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 <md...@st... 
> <mailto:md...@st...>> 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_newmpl.pdf
>>> <http://atmos.uwyo.edu/%7Egsever/data/vocals_RF04_NU05_newmpl.pdf>
>>> http://atmos.uwyo.edu/~gsever/data/vocals_RF04_NU05_oldmpl.pdf
>>> <http://atmos.uwyo.edu/%7Egsever/data/vocals_RF04_NU05_oldmpl.pdf>
>>>
>>>
>>> newmpl file is created using the latest master branch (cloned
>>> and setup today)
>>> oldmpl is created using mpl v1.1.0
>>> (https://github.com/downloads/matplotlib/matplotlib/matplotlib-1.1.0.tar.gz)
>>>
>>> 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
>> Mat...@li... <mailto:Mat...@li...>
>> 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
> Mat...@li...
> <mailto:Mat...@li...>
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>
>
>
>
> -- 
> Gökhan

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 <md...@st...> 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_newmpl.pdf
> http://atmos.uwyo.edu/~gsever/data/vocals_RF04_NU05_oldmpl.pdf
>
>
> newmpl file is created using the latest master branch (cloned and setup
> today)
> oldmpl is created using mpl v1.1.0 (
> https://github.com/downloads/matplotlib/matplotlib/matplotlib-1.1.0.tar.gz
> )
>
> 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 lis...@li...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
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>
>
-- 
Gökhan

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_newmpl.pdf 
>> <http://atmos.uwyo.edu/%7Egsever/data/vocals_RF04_NU05_newmpl.pdf>
>> http://atmos.uwyo.edu/~gsever/data/vocals_RF04_NU05_oldmpl.pdf 
>> <http://atmos.uwyo.edu/%7Egsever/data/vocals_RF04_NU05_oldmpl.pdf>
>>
>>
>> newmpl file is created using the latest master branch (cloned and 
>> setup today)
>> oldmpl is created using mpl v1.1.0 
>> (https://github.com/downloads/matplotlib/matplotlib/matplotlib-1.1.0.tar.gz)
>>
>> 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
>
>
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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_newmpl.pdf 
> <http://atmos.uwyo.edu/%7Egsever/data/vocals_RF04_NU05_newmpl.pdf>
> http://atmos.uwyo.edu/~gsever/data/vocals_RF04_NU05_oldmpl.pdf 
> <http://atmos.uwyo.edu/%7Egsever/data/vocals_RF04_NU05_oldmpl.pdf>
>
>
> newmpl file is created using the latest master branch (cloned and 
> setup today)
> oldmpl is created using mpl v1.1.0 
> (https://github.com/downloads/matplotlib/matplotlib/matplotlib-1.1.0.tar.gz)
>
> 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

On Tuesday, May 15, 2012, 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_newmpl.pdf
> http://atmos.uwyo.edu/~gsever/data/vocals_RF04_NU05_oldmpl.pdf
>
>
> newmpl file is created using the latest master branch (cloned and setup
> today)
> oldmpl is created using mpl v1.1.0 (
> https://github.com/downloads/matplotlib/matplotlib/matplotlib-1.1.0.tar.gz
> )
>
> 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.
>
> Any ideas as to what could be going wrong here?
>
> Thanks.
>
> --
> Gökhan
>
I do recall some changes were made for v1.1.x with regards to autoscaling.
 Another change was also made with respect to Bbox clipping. I can't
recall enough details to know if they are a part of this issue.
Ben Root