matplotlib-devel Mailing List for matplotlib (Page 2)

On 22 March 2013 18:06, Michael Droettboom <md...@st...> wrote:

>  On 03/22/2013 12:45 PM, Nelle Varoquaux wrote:
>
>
>   I'm hoping to host a matplotlib sprint during the final two days of
>> Scipy 2013 this year, and I hope to see as many as possible of you there.
>> I think it's also really important to bring new developers into sprints,
>> because it's such an efficient way to get people familiar with the code
>> base.
>>
>
>  Being in Europe, it is very unlikely I'll come to Scipy this year, but I
> think it is a great idea to organize a sprint during this period.
>
>
> I'll also try to set up some sort of remote communication tool (such as
> Google Hangouts, or even just IRC) so that people can "participate" in the
> sprint remotely.
>

There's a #matplotlib chanel on irc, where a dozen people hang out. That
could be a nice place to gather people during the sprint.


>
>
>
>
>>  It might be helpful to start brainstorming now about which projects we
>> may want to tackle so that we can have as much in place as possible by then
>> and hit the ground running.
>>
>
>  One of the things I think would be great to tackle is the replacement of
> the Travis bot. Ideally, I'd like to see a pep8 check of each patch and a
> daily build of the documentation on master in addition of the tests.
>
>
> Agreed.  That's definitely on my todo list.  We've got some funding
> through my employer to pay for continuous integration hosting -- it's just
> taking a while to work its way through the system.  Once we have some sort
> of paid hosting system, we should be able to do a lot more than Travis
> currently can.
>
> Mike
>
>
>
>
>>
>> I've set up a wiki page here:
>>
>> https://github.com/matplotlib/matplotlib/wiki/Scipy-2013
>>
>> Mike
>>
>>
>> ------------------------------------------------------------------------------
>> Everyone hates slow websites. So do we.
>> Make your web apps faster with AppDynamics
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>> _______________________________________________
>> Matplotlib-devel mailing list
>> Mat...@li...
>> https://lists.sourceforge.net/lists/listinfo/matplotlib-devel
>>
>>
>
>

On 03/22/2013 12:45 PM, Nelle Varoquaux wrote:
>
>     I'm hoping to host a matplotlib sprint during the final two days
>     of Scipy 2013 this year, and I hope to see as many as possible of
>     you there.  I think it's also really important to bring new
>     developers into sprints, because it's such an efficient way to get
>     people familiar with the code base.
>
>
> Being in Europe, it is very unlikely I'll come to Scipy this year, but 
> I think it is a great idea to organize a sprint during this period.

I'll also try to set up some sort of remote communication tool (such as 
Google Hangouts, or even just IRC) so that people can "participate" in 
the sprint remotely.

>     It might be helpful to start brainstorming now about which
>     projects we may want to tackle so that we can have as much in
>     place as possible by then and hit the ground running.
>
>
> One of the things I think would be great to tackle is the replacement 
> of the Travis bot. Ideally, I'd like to see a pep8 check of each patch 
> and a daily build of the documentation on master in addition of the tests.

Agreed.  That's definitely on my todo list.  We've got some funding 
through my employer to pay for continuous integration hosting -- it's 
just taking a while to work its way through the system.  Once we have 
some sort of paid hosting system, we should be able to do a lot more 
than Travis currently can.

Mike

>
>     I've set up a wiki page here:
>
>     https://github.com/matplotlib/matplotlib/wiki/Scipy-2013
>
>     Mike
>
>     ------------------------------------------------------------------------------
>     Everyone hates slow websites. So do we.
>     Make your web apps faster with AppDynamics
>     Download AppDynamics Lite for free today:
>     http://p.sf.net/sfu/appdyn_d2d_mar
>     _______________________________________________
>     Matplotlib-devel mailing list
>     Mat...@li...
>     <mailto:Mat...@li...>
>     https://lists.sourceforge.net/lists/listinfo/matplotlib-devel
>
>

>  I'm hoping to host a matplotlib sprint during the final two days of Scipy
> 2013 this year, and I hope to see as many as possible of you there.  I
> think it's also really important to bring new developers into sprints,
> because it's such an efficient way to get people familiar with the code
> base.
>

Being in Europe, it is very unlikely I'll come to Scipy this year, but I
think it is a great idea to organize a sprint during this period.


> It might be helpful to start brainstorming now about which projects we may
> want to tackle so that we can have as much in place as possible by then and
> hit the ground running.
>

One of the things I think would be great to tackle is the replacement of
the Travis bot. Ideally, I'd like to see a pep8 check of each patch and a
daily build of the documentation on master in addition of the tests.


>
> I've set up a wiki page here:
>
> https://github.com/matplotlib/matplotlib/wiki/Scipy-2013
>
> Mike
>
>
> ------------------------------------------------------------------------------
> Everyone hates slow websites. So do we.
> Make your web apps faster with AppDynamics
> Download AppDynamics Lite for free today:
> http://p.sf.net/sfu/appdyn_d2d_mar
> _______________________________________________
> Matplotlib-devel mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-devel
>
>

I'm hoping to host a matplotlib sprint during the final two days of 
Scipy 2013 this year, and I hope to see as many as possible of you 
there.  I think it's also really important to bring new developers into 
sprints, because it's such an efficient way to get people familiar with 
the code base.

It might be helpful to start brainstorming now about which projects we 
may want to tackle so that we can have as much in place as possible by 
then and hit the ground running.

I've set up a wiki page here:

https://github.com/matplotlib/matplotlib/wiki/Scipy-2013

Mike

On 15 March 2013 17:18, Nathaniel Smith <nj...@po...> wrote:

> On 15 Mar 2013 16:50, "Todd" <tod...@gm...> wrote:
> > Within the axes constructor, the constructor would run through each of
> these modules and store them as attributes with the same name as the
> function and the function itself being the contents.  At least if me
> understanding is correct, this would make them behave as methods (since
> they are already set up to take an axes instance as their first argument).
>
> This won't work as described - the function -> unbound method -> bound
> method magic only happens for attributes that aren't found in the object
> dict, when  lookup falls back on the type dict. You'll want to inject the
> functions into the Axes type object, either via a metaclass constructor or
> just by hand after the type is created.
>

You can manually create bound methods using types.MethodType, if necessary.
However, I think adding them to the class is preferable.

Thomas

On 15 Mar 2013 16:50, "Todd" <tod...@gm...> wrote:
> Within the axes constructor, the constructor would run through each of
these modules and store them as attributes with the same name as the
function and the function itself being the contents.  At least if me
understanding is correct, this would make them behave as methods (since
they are already set up to take an axes instance as their first argument).

This won't work as described - the function -> unbound method -> bound
method magic only happens for attributes that aren't found in the object
dict, when  lookup falls back on the type dict. You'll want to inject the
functions into the Axes type object, either via a metaclass constructor or
just by hand after the type is created.

-n

On Thu, Mar 14, 2013 at 1:50 AM, Pierre Barbier de Reuille <
pi...@ba...> wrote:

> Hello,
>
> for my own research, I have implemented a function plotting datasets as
> violin plots ( http://en.wikipedia.org/wiki/Violin_plot ). I attach an
> example output that I am using.
>
> It is fairly stand-alone, with the expection of a scipy dependency for
> computation of the estimated density of probability of the shown. Before
> distribution, I will need to document the function and classes associated.
>
> My question is: what is the process if there a place I can make the code
> available for consideration?
>
> Thanks,
>
>
Pierre,

The scipy dependency is unfortunately a deal breaker for matplotlib. I'm
not a core dev, but typically you have a work around the dependency in some
way (e.g., make the user specify the KDE for each dataset).

Also, the statsmodels project has this functionality:
http://statsmodels.sourceforge.net/devel/generated/statsmodels.graphics.boxplots.violinplot.html#statsmodels.graphics.boxplots.violinplot

Perhaps your ideas and approach could be used to improve
their implementation.
-paul

Currently, many of the plot types reside in axes.  This makes sense from a
structural standpoint, since the plots are generally a part of an axes.
However, it makes things more difficult from a practical standpoint.
First, it means that the axes class is huge, with both core axes-related
methods and plotting-related methods.  Second, it means that boilerplate.py
has to be told explicitly which methods to use, rather than just grabbing
everything from a module.  Third, it makes it impossible to break the plot
types into smaller groups of related plots.

The solution, I think, is to move the plots into one or more modules and
implement them as functions.  They would have the same call signature as
now, we would just replace "self" with "ax" or something like that.  This
would allow you to, theoretically, import and call them without axes, but
this would probably not be common.

Within the axes constructor, the constructor would run through each of
these modules and store them as attributes with the same name as the
function and the function itself being the contents.  At least if me
understanding is correct, this would make them behave as methods (since
they are already set up to take an axes instance as their first argument).

The axes would just be left with the methods related to manipulating the
axes themselves.

This would also simplify boilerplate.py, since it would simply need to know
the names of the modules rather than the name of every plot type.  It would
then just run through the functions in the modules (or even just run
through all modules in a particular directory).  We would probably even be
able to do away with that portion of boilerplate.py completely, creating
the plotting functions in on-the-fly in pyplot at runtime, but that is a
separate issue.

What does everyone think of this approach?

Hi Phil,

On Wed, Mar 13, 2013 at 5:14 AM, Phil Elson <pel...@gm...> wrote:

> Joe,
>
> Do you have any feeling for performance of mpldatacursor? I'm interested
> to know if implementing this functionality by default (with an rcParam
> switch to disable) on all Axes would have a significant impact on
> performance?
>

It should be quite fast.  In most cases only one extra annotation object
per-axes will be created.  The annotation is updated, rather than a new one
being created, unless that's specifically what's desired (e.g. The
display='multiple' option).


>
>
> > If it is something that other people find useful, I'd be happy to submit
> a pull request to incorporate it into matplotlib.
>
> Personally, I find this kind of extension better as a completely separate
> piece of work which one could "pip install" - it means you can be more
> flexible with release cycles, especially in the early days of the code when
> regular updates are most likely. Though I should note, I'm not against it
> being included in mpl as an extension, if you would prefer that.
>

Good point! Especially in its current state, flexibility is a good thing!


>
>
> Thanks again for sharing!
>

Thanks! Glad to!

Cheers!
-Joe





On 13 March 2013 10:08, Phil Elson <pel...@gm...> wrote:

> Thanks for this Joe, mpldatacursor looks like an excellent piece of work -
> I for one will be installing and using it regularly.
>
> Thanks for sharing!
>
>
>
>
>
> On 13 March 2013 03:58, Joe Kington <jof...@gm...> wrote:
>
>> I recently got around to polishing up a snippet I've been using for quite
>> awhile.  https://github.com/joferkington/mpldatacursor/  and I was
>> hoping to get some feeding on the current implementation.
>>
>> "mpldatacursor" allows a user to easily click on an artist and display a
>> customizable, interactive pop-up box displaying information about the
>> selected artist (e.g. x & y, label, z for images and collections, etc).
>> It's a stand-alone module (and in pypi), but you could also just download
>> the examples directory from github and copy the mpldatacursor.py file into
>> it to try things out.
>>
>> A few key questions:
>>
>>    1. Is this something that anyone else finds useful?
>>
>>    2. Does it seem intuitive?
>>
>>    3. Does the implementation seem flexible enough for most needs? (Note
>>    that any additional kwargs are passed on to annotate to create the "data
>>    cursor", so the appearance of the box is customizable through annotation
>>    kwargs.)
>>
>>    4. Are there any obvious features missing?
>>
>>    5. Any suggestions? (especially better name suggestions...)
>>
>> If it is something that other people find useful, I'd be happy to submit
>> a pull request to incorporate it into matplotlib.  (If I did, it would
>> probably be best to drop the HighlightDataCursor class, as its limited in
>> what it can do.)
>>
>> Thanks a bunch!
>>
>> -Joe
>>
>>
>> ------------------------------------------------------------------------------
>> Everyone hates slow websites. So do we.
>> Make your web apps faster with AppDynamics
>> Download AppDynamics Lite for free today:
>> http://p.sf.net/sfu/appdyn_d2d_mar
>> _______________________________________________
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>>
>>
>

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Joe,

Do you have any feeling for performance of mpldatacursor? I'm interested to
know if implementing this functionality by default (with an rcParam switch
to disable) on all Axes would have a significant impact on performance?


> If it is something that other people find useful, I'd be happy to submit
a pull request to incorporate it into matplotlib.

Personally, I find this kind of extension better as a completely separate
piece of work which one could "pip install" - it means you can be more
flexible with release cycles, especially in the early days of the code when
regular updates are most likely. Though I should note, I'm not against it
being included in mpl as an extension, if you would prefer that.


Thanks again for sharing!





On 13 March 2013 10:08, Phil Elson <pel...@gm...> wrote:

> Thanks for this Joe, mpldatacursor looks like an excellent piece of work -
> I for one will be installing and using it regularly.
>
> Thanks for sharing!
>
>
>
>
>
> On 13 March 2013 03:58, Joe Kington <jof...@gm...> wrote:
>
>> I recently got around to polishing up a snippet I've been using for quite
>> awhile.  https://github.com/joferkington/mpldatacursor/  and I was
>> hoping to get some feeding on the current implementation.
>>
>> "mpldatacursor" allows a user to easily click on an artist and display a
>> customizable, interactive pop-up box displaying information about the
>> selected artist (e.g. x & y, label, z for images and collections, etc).
>> It's a stand-alone module (and in pypi), but you could also just download
>> the examples directory from github and copy the mpldatacursor.py file into
>> it to try things out.
>>
>> A few key questions:
>>
>>    1. Is this something that anyone else finds useful?
>>
>>    2. Does it seem intuitive?
>>
>>    3. Does the implementation seem flexible enough for most needs? (Note
>>    that any additional kwargs are passed on to annotate to create the "data
>>    cursor", so the appearance of the box is customizable through annotation
>>    kwargs.)
>>
>>    4. Are there any obvious features missing?
>>
>>    5. Any suggestions? (especially better name suggestions...)
>>
>> If it is something that other people find useful, I'd be happy to submit
>> a pull request to incorporate it into matplotlib.  (If I did, it would
>> probably be best to drop the HighlightDataCursor class, as its limited in
>> what it can do.)
>>
>> Thanks a bunch!
>>
>> -Joe
>>
>>
>> ------------------------------------------------------------------------------
>> Everyone hates slow websites. So do we.
>> Make your web apps faster with AppDynamics
>> Download AppDynamics Lite for free today:
>> http://p.sf.net/sfu/appdyn_d2d_mar
>> _______________________________________________
>> Matplotlib-users mailing list
>> Mat...@li...
>> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>>
>>
>

In article <513...@st...>,
 Michael Droettboom <md...@st...> 
 wrote:

> On 03/07/2013 06:48 PM, Russell E. Owen wrote:
> > In article <513...@st...>,
> >   Michael Droettboom <md...@st...>
> >   wrote:
> >
> >> We've finally squashed all of the bugs slated for 1.2.1, so I have
> >> tagged a 1.2.1rc1 for testing....
> >>
> >> It can be downloaded from the SF files server here:
> >>
> >> http://sourceforge.net/projects/matplotlib/files/matplotlib/matplotlib-1.2.
> >> 1rc
> >> 1/matplotlib-1.2.1rc1.tar.gz/download
> >>...
> > I uploaded two Mac binaries to SourceForge...
> >
> > They are both built against numpy 1.6.2, which is not ideal, but is the
> > best I can do at the moment.
> >
> I think that's fine for now -- for the rc it's most important to just 
> make sure that working packages *can* be built.  We can reevaluate what 
> Numpy version to use for the final release, and let me know if we should 
> push back the final release date to accomodate rejiggering your 
> development environment etc.

Do you have an opinion on the best numpy to use for matplotlib 1.2 
binaries?

Some considerations:

I think numpy 1.7.x is binary compatible with 1.6.x. If so, surely it is 
OK to build matplotlib against numpy 1.6.x, even if a user wants to use 
numpy 1.7.x? I have not tested this (nor the reverse direction).

numpy 1.7.0 has a serious memory leak (though I don't know how many 
users would ever see it). 1.7.1 is due out fairly soon. Personally I 
would rather skip 1.7.0 on my own machine. Yet it would be wise to let 
1.7.1 be used for a week or two before relying on it and I'd hate to 
hold up release of matplotlib 1.2.1.

-- Russell

On 3/8/2013 2:16 AM, Tejashri Kandolkar wrote:
> Thanks Christoph, thanks for the help.
>
> However I found the issue.
> The issue is that somehow matplotlib_release variant linked to the debug
> lib of libpng.
> And so on a non-developer machine, which dosent have msvcr90d.dll (debug
> crt), DLL Load fails.

Good to know. The zlib, png and freetype libraries should be built as 
"Release, Static Library, Multi-threaded DLL" to be compatible with 
CPython. See also <https://github.com/matplotlib/matplotlib/issues/1717>

>
> So now I have another question:
>
> I have built the static libs of freetype, libpng (and zlib which is
> required by libpng) from source
> And I mention the paths of these static libs in setupext.py (by
> modifying "basedir") while building matplotlib.

No need to modify the source code. You can add the directories 
containing the headers and libraries to the INCLUDE and LIB environment 
variables.

>
> Now when matplotlib builds, and generates a _png.pyd, will it still need
> the static libs to be present in the location where the _png.pyd is present?
> What I understand is that it shouldn't since it is a static library and
> should be built into the binary.

The static libs are not needed at runtime. They are part of the pyd binary.

>
> Why I am asking this is because I am unable to build the libpng DLLs
> (for some reason I dont know).
> So I have to go forward with the static lib approach.

The static approach is recommended. If you link to the DLLs instead you 
will need to ship those with the binaries, which is not done in setup.py.

Christoph

>
> Thanks,
> Regards,
> Tej.
>
> On Thu, Mar 7, 2013 at 10:53 PM, Christoph Gohlke <cg...@uc...
> <mailto:cg...@uc...>> wrote:
>
>     On 3/7/2013 8:39 AM, Christoph Gohlke wrote:
>     > On 3/7/2013 6:00 AM, Tejashri Kandolkar wrote:
>     >> Hi,
>     >>
>     >> I built matplotlib1.2.0 with python3.2 on Windows7 from source.
>     >> I built the libpng and freetype libs and linked them statically to
>     >> matplotlib.
>     >>
>     >> Everything works fine on my machine, I can run the matplotlib examples etc
>     >> But on a new Win7 machine(with the exact same configuration as mine,
>     >> except a few softwares), I get the following error when i try to import
>     >> png module like this:
>     >>
>     >> import matplotlib._png
>     >>
>     >> ImportError: DLL load failed: The application has failed to start
>     >> because its side-by-side configuration is incorrect. Please see the
>     >> application event log or use the command-line sxstrace.exe tool for more
>     >> detail.
>     >>
>     >>
>     >> I used the dependency walker and found that pyd_ DLL was indeed having
>     >> issues during load.
>     >>
>     >> What could be the reason. Surprisingly it works all fine on my machine.
>     >>
>     >>
>     >> Regards,
>     >> Tej
>     >>
>     >
>     > Assuming this is 32 bit Python, install the Microsoft Visual C++ 2008
>     > Redistributable Package (x86) <from
>     >http://www.microsoft.com/en-us/download/details.aspx?id=29>
>     >
>
>     Besides that, look for extra msvcp90.dll or msvcr90.dll files in PATH
>     (for example MikteX is known for that) and resolve conflicts.
>
>     Christoph
>
>     ------------------------------------------------------------------------------
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>
>

On 03/07/2013 06:48 PM, Russell E. Owen wrote:
> In article <513...@st...>,
>   Michael Droettboom <md...@st...>
>   wrote:
>
>> We've finally squashed all of the bugs slated for 1.2.1, so I have
>> tagged a 1.2.1rc1 for testing.  This is a bugfix release, and improves
>> on the overall quality of 1.2.0.  Thanks to everyone who worked hard to
>> fix a multitude of papercuts.  I'll provide a more detailed list of
>> changes in the announcement of the final version.
>>
>> It can be downloaded from the SF files server here:
>>
>> http://sourceforge.net/projects/matplotlib/files/matplotlib/matplotlib-1.2.1rc
>> 1/matplotlib-1.2.1rc1.tar.gz/download
>>
>> Packagers: I don't expect a lot of glitches as nothing significant has
>> changed about the build since 1.2.0, but now is the best time to try to
>> build for your various packaging systems and report back any problems.
>> If all goes well, I plan to make a final release in approximately two
>> weeks on March 25.
> I uploaded two Mac binaries to SourceForge:
> - A 10.3-compatible version built and tested on 10.4
> - A 10.6-compatible version built and tested on 10.8 (I should also test
> it on 10.6 but don't have time right now).
>
> They are both built against numpy 1.6.2, which is not ideal, but is the
> best I can do at the moment.
>
I think that's fine for now -- for the rc it's most important to just 
make sure that working packages *can* be built.  We can reevaluate what 
Numpy version to use for the final release, and let me know if we should 
push back the final release date to accomodate rejiggering your 
development environment etc.

Cheers,
Mike

On Fri, Mar 8, 2013 at 3:46 PM, Tejashri Kandolkar <
tej...@gm...> wrote:

> Thanks Christoph, thanks for the help.
>
> However I found the issue.
> The issue is that somehow matplotlib_release variant linked to the debug
> lib of libpng.
> And so on a non-developer machine, which dosent have msvcr90d.dll (debug
> crt), DLL Load fails.
>
> So now I have another question:
>
> I have built the static libs of freetype, libpng (and zlib which is
> required by libpng) from source
> And I mention the paths of these static libs in setupext.py (by modifying
> "basedir") while building matplotlib.
>
> Now when matplotlib builds, and generates a _png.pyd, will it still need
> the static libs to be present in the location where the _png.pyd is present?
> What I understand is that it shouldn't since it is a static library and
> should be built into the binary.
>
> Why I am asking this is because I am unable to build the libpng DLLs (for
> some reason I dont know).
> So I have to go forward with the static lib approach.
>
> Thanks,
> Regards,
> Tej.
>
>
> On Thu, Mar 7, 2013 at 10:53 PM, Christoph Gohlke <cg...@uc...> wrote:
>
>> On 3/7/2013 8:39 AM, Christoph Gohlke wrote:
>> > On 3/7/2013 6:00 AM, Tejashri Kandolkar wrote:
>> >> Hi,
>> >>
>> >> I built matplotlib1.2.0 with python3.2 on Windows7 from source.
>> >> I built the libpng and freetype libs and linked them statically to
>> >> matplotlib.
>> >>
>> >> Everything works fine on my machine, I can run the matplotlib examples
>> etc
>> >> But on a new Win7 machine(with the exact same configuration as mine,
>> >> except a few softwares), I get the following error when i try to import
>> >> png module like this:
>> >>
>> >> import matplotlib._png
>> >>
>> >> ImportError: DLL load failed: The application has failed to start
>> >> because its side-by-side configuration is incorrect. Please see the
>> >> application event log or use the command-line sxstrace.exe tool for
>> more
>> >> detail.
>> >>
>> >>
>> >> I used the dependency walker and found that pyd_ DLL was indeed having
>> >> issues during load.
>> >>
>> >> What could be the reason. Surprisingly it works all fine on my machine.
>> >>
>> >>
>> >> Regards,
>> >> Tej
>> >>
>> >
>> > Assuming this is 32 bit Python, install the Microsoft Visual C++ 2008
>> > Redistributable Package (x86) <from
>> > http://www.microsoft.com/en-us/download/details.aspx?id=29>
>> >
>>
>> Besides that, look for extra msvcp90.dll or msvcr90.dll files in PATH
>> (for example MikteX is known for that) and resolve conflicts.
>>
>> Christoph
>>
>>
>> ------------------------------------------------------------------------------
>> Symantec Endpoint Protection 12 positioned as A LEADER in The Forrester
>> Wave(TM): Endpoint Security, Q1 2013 and "remains a good choice" in the
>> endpoint security space. For insight on selecting the right partner to
>> tackle endpoint security challenges, access the full report.
>> http://p.sf.net/sfu/symantec-dev2dev
>> _______________________________________________
>> Matplotlib-users mailing list
>> Mat...@li...
>> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>>
>
>

In article <513...@st...>,
 Michael Droettboom <md...@st...> 
 wrote:

> We've finally squashed all of the bugs slated for 1.2.1, so I have 
> tagged a 1.2.1rc1 for testing.  This is a bugfix release, and improves 
> on the overall quality of 1.2.0.  Thanks to everyone who worked hard to 
> fix a multitude of papercuts.  I'll provide a more detailed list of 
> changes in the announcement of the final version.
> 
> It can be downloaded from the SF files server here:
> 
> http://sourceforge.net/projects/matplotlib/files/matplotlib/matplotlib-1.2.1rc
> 1/matplotlib-1.2.1rc1.tar.gz/download
> 
> Packagers: I don't expect a lot of glitches as nothing significant has 
> changed about the build since 1.2.0, but now is the best time to try to 
> build for your various packaging systems and report back any problems.  
> If all goes well, I plan to make a final release in approximately two 
> weeks on March 25.

I uploaded two Mac binaries to SourceForge:
- A 10.3-compatible version built and tested on 10.4
- A 10.6-compatible version built and tested on 10.8 (I should also test 
it on 10.6 but don't have time right now).

They are both built against numpy 1.6.2, which is not ideal, but is the 
best I can do at the moment.

-- Russell

On 3/7/2013 11:30 AM, Michael Droettboom wrote:
> We've finally squashed all of the bugs slated for 1.2.1, so I have
> tagged a 1.2.1rc1 for testing.  This is a bugfix release, and improves
> on the overall quality of 1.2.0.  Thanks to everyone who worked hard to
> fix a multitude of papercuts.  I'll provide a more detailed list of
> changes in the announcement of the final version.
>
> It can be downloaded from the SF files server here:
>
> http://sourceforge.net/projects/matplotlib/files/matplotlib/matplotlib-1.2.1rc1/matplotlib-1.2.1rc1.tar.gz/download
>
> Packagers: I don't expect a lot of glitches as nothing significant has
> changed about the build since 1.2.0, but now is the best time to try to
> build for your various packaging systems and report back any problems.
> If all goes well, I plan to make a final release in approximately two
> weeks on March 25.
>
> Cheers,
> Mike

Thanks. It builds and tests OK on Windows. Should I upload the binaries 
to SF? They are built against numpy 1.7 and therefore require numpy >= 
1.7.0.

Christoph

On Thu, Mar 7, 2013 at 1:30 PM, Michael Droettboom <md...@st...> wrote:
> Packagers: I don't expect a lot of glitches as nothing significant has
> changed about the build since 1.2.0, but now is the best time to try to
> build for your various packaging systems and report back any problems.

Ryan, if you're out there, let me know if you run into snags with
building for the Mac.

-- 
Damon McDougall
http://www.damon-is-a-geek.com
Institute for Computational Engineering Sciences
201 E. 24th St.
Stop C0200
The University of Texas at Austin
Austin, TX 78712-1229

We've finally squashed all of the bugs slated for 1.2.1, so I have 
tagged a 1.2.1rc1 for testing.  This is a bugfix release, and improves 
on the overall quality of 1.2.0.  Thanks to everyone who worked hard to 
fix a multitude of papercuts.  I'll provide a more detailed list of 
changes in the announcement of the final version.

It can be downloaded from the SF files server here:

http://sourceforge.net/projects/matplotlib/files/matplotlib/matplotlib-1.2.1rc1/matplotlib-1.2.1rc1.tar.gz/download

Packagers: I don't expect a lot of glitches as nothing significant has 
changed about the build since 1.2.0, but now is the best time to try to 
build for your various packaging systems and report back any problems.  
If all goes well, I plan to make a final release in approximately two 
weeks on March 25.

Cheers,
Mike

On 03/07/2013 12:45 PM, Damon McDougall wrote:
> I'm really lost when it comes to how to could optimise our rendering, 
> especially with text rendering being extremely slow at present.

Let's put some benchmarks together for this.  There's a lot of 
optimizations to the text rendering in the Agg backend already (caching 
pre-rendered text etc.), so if there's anything more to be done, we 
should get to the bottom of it.

Mike

On Thu, Mar 7, 2013 at 9:24 AM, Benjamin Root <ben...@ou...> wrote:
>
>
> On Thu, Mar 7, 2013 at 10:12 AM, Michael Droettboom <md...@st...> wrote:
>>
>> Ah, yes.  I forgot that my issues with trying to do rendering in the
>> browser shared some similarities here.
>>
>> When I did the last major backend refactoring (which is coming on 6
>> years ago now), I considered exactly this, which is why draw_path takes
>> both a path object (which is unlikely to change) and a transform.  It
>> turned out to not be terribly useful for most the backends we have,
>> since PDF, PS, SVG (at least in the versions of the specs we're using)
>> all scale the stroke width along with the vertices, so you couldn't, for
>> example, zoom in on a line plot without the line width exploding.  The
>> Agg backend is able to perform the transform itself since we wrote it to
>> not scale the stroke using the path's transform, but there's little
>> advantage since it's all software anyway.  But the API still works this
>> way even if nothing takes advantage of it.  Though technically paths are
>> mutable, since they store Numpy arrays which are mutable, in practice
>> they are rarely if never mutated (I'll have to verify this, but
>> certainly in the course of panning and zooming, they wouldn't be), so it
>> should be possible to know whether a path is already on the GPU by
>> checking its id against a table of what's on the GPU.
>>
>> The other wrinkle is that the transform is a combination of affine
>> transforms (which I'm sure OpenGL handles just fine) with arbitrary
>> transforms such as log scaling or polar transformations.  I assume this
>> would all have to be implemented as a vertex shader to get the
>> performance benefits of OpenGL.  We could probably implement all of the
>> transforms built in to matplotlib (which cover a lot of cases), and then
>> just provide some slow "escape route" to handle arbitrary transforms
>> written in Numpy/Python, and for bonus points provide a hook for users
>> to add their own OpenGL transforms (just as it would have been necessary
>> to do with Javascript in the case of web browser rendering).  But I bet
>> a lot of people would be happy as a start to just have the built-in
>> transforms working.
>>
>> The last thing to raise is the case of very large data.  When I was
>> investigating rendering in a web browser, there were pretty low limits
>> to the number of vertices I could keep hold of in the browser.
>> matplotlib has a "path simplification" algorithm to reduce the number of
>> vertices without changing the appearance of the plot, but of course
>> that's dependant on the scale, so it has to be rerun every time the
>> scale changes.  While path simplification may not be as necessary for
>> speed on the GPU, it may be necessary due to memory constraints.
>>
>> Mike
>>
>
> Just to voice what my main interest in an OpenGL backend is a chance to move
> away from the layering approach of the matplotlib renderer to the ability to
> specify three dimensional data to plot.  Performance is a nice side-benefit,
> certainly, but breaking out our low-level assumptions of how we plot would
> be refreshing.

I agree here.  I would certainly benefit from this.  I'm really lost
when it comes to how to could optimise our rendering, especially with
text rendering being extremely slow at present.

I am, however, a little concerned we may be reinventing the wheel here
with useful tools that already exist for 3d rendering.  Paraview and
MayaVi come to mind.

>
> Ben Root
>
>
> ------------------------------------------------------------------------------
> Symantec Endpoint Protection 12 positioned as A LEADER in The Forrester
> Wave(TM): Endpoint Security, Q1 2013 and "remains a good choice" in the
> endpoint security space. For insight on selecting the right partner to
> tackle endpoint security challenges, access the full report.
> http://p.sf.net/sfu/symantec-dev2dev
> _______________________________________________
> Matplotlib-devel mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-devel
>



-- 
Damon McDougall
http://www.damon-is-a-geek.com
Institute for Computational Engineering Sciences
201 E. 24th St.
Stop C0200
The University of Texas at Austin
Austin, TX 78712-1229

On Thu, Mar 7, 2013 at 10:12 AM, Michael Droettboom <md...@st...> wrote:

> Ah, yes.  I forgot that my issues with trying to do rendering in the
> browser shared some similarities here.
>
> When I did the last major backend refactoring (which is coming on 6
> years ago now), I considered exactly this, which is why draw_path takes
> both a path object (which is unlikely to change) and a transform.  It
> turned out to not be terribly useful for most the backends we have,
> since PDF, PS, SVG (at least in the versions of the specs we're using)
> all scale the stroke width along with the vertices, so you couldn't, for
> example, zoom in on a line plot without the line width exploding.  The
> Agg backend is able to perform the transform itself since we wrote it to
> not scale the stroke using the path's transform, but there's little
> advantage since it's all software anyway.  But the API still works this
> way even if nothing takes advantage of it.  Though technically paths are
> mutable, since they store Numpy arrays which are mutable, in practice
> they are rarely if never mutated (I'll have to verify this, but
> certainly in the course of panning and zooming, they wouldn't be), so it
> should be possible to know whether a path is already on the GPU by
> checking its id against a table of what's on the GPU.
>
> The other wrinkle is that the transform is a combination of affine
> transforms (which I'm sure OpenGL handles just fine) with arbitrary
> transforms such as log scaling or polar transformations.  I assume this
> would all have to be implemented as a vertex shader to get the
> performance benefits of OpenGL.  We could probably implement all of the
> transforms built in to matplotlib (which cover a lot of cases), and then
> just provide some slow "escape route" to handle arbitrary transforms
> written in Numpy/Python, and for bonus points provide a hook for users
> to add their own OpenGL transforms (just as it would have been necessary
> to do with Javascript in the case of web browser rendering).  But I bet
> a lot of people would be happy as a start to just have the built-in
> transforms working.
>
> The last thing to raise is the case of very large data.  When I was
> investigating rendering in a web browser, there were pretty low limits
> to the number of vertices I could keep hold of in the browser.
> matplotlib has a "path simplification" algorithm to reduce the number of
> vertices without changing the appearance of the plot, but of course
> that's dependant on the scale, so it has to be rerun every time the
> scale changes.  While path simplification may not be as necessary for
> speed on the GPU, it may be necessary due to memory constraints.
>
> Mike
>
>
Just to voice what my main interest in an OpenGL backend is a chance to
move away from the layering approach of the matplotlib renderer to the
ability to specify three dimensional data to plot.  Performance is a nice
side-benefit, certainly, but breaking out our low-level assumptions of how
we plot would be refreshing.

Ben Root

Ah, yes.  I forgot that my issues with trying to do rendering in the 
browser shared some similarities here.

When I did the last major backend refactoring (which is coming on 6 
years ago now), I considered exactly this, which is why draw_path takes 
both a path object (which is unlikely to change) and a transform.  It 
turned out to not be terribly useful for most the backends we have, 
since PDF, PS, SVG (at least in the versions of the specs we're using) 
all scale the stroke width along with the vertices, so you couldn't, for 
example, zoom in on a line plot without the line width exploding.  The 
Agg backend is able to perform the transform itself since we wrote it to 
not scale the stroke using the path's transform, but there's little 
advantage since it's all software anyway.  But the API still works this 
way even if nothing takes advantage of it.  Though technically paths are 
mutable, since they store Numpy arrays which are mutable, in practice 
they are rarely if never mutated (I'll have to verify this, but 
certainly in the course of panning and zooming, they wouldn't be), so it 
should be possible to know whether a path is already on the GPU by 
checking its id against a table of what's on the GPU.

The other wrinkle is that the transform is a combination of affine 
transforms (which I'm sure OpenGL handles just fine) with arbitrary 
transforms such as log scaling or polar transformations.  I assume this 
would all have to be implemented as a vertex shader to get the 
performance benefits of OpenGL.  We could probably implement all of the 
transforms built in to matplotlib (which cover a lot of cases), and then 
just provide some slow "escape route" to handle arbitrary transforms 
written in Numpy/Python, and for bonus points provide a hook for users 
to add their own OpenGL transforms (just as it would have been necessary 
to do with Javascript in the case of web browser rendering).  But I bet 
a lot of people would be happy as a start to just have the built-in 
transforms working.

The last thing to raise is the case of very large data.  When I was 
investigating rendering in a web browser, there were pretty low limits 
to the number of vertices I could keep hold of in the browser.  
matplotlib has a "path simplification" algorithm to reduce the number of 
vertices without changing the appearance of the plot, but of course 
that's dependant on the scale, so it has to be rerun every time the 
scale changes.  While path simplification may not be as necessary for 
speed on the GPU, it may be necessary due to memory constraints.

Mike

On 03/07/2013 09:50 AM, Nicolas Rougier wrote:
>
> Indeed, you analyzed/explained the overall situation very well in your blog post: http://mdboom.github.com/blog/2012/08/06/matplotlib-client-side/
>
>
> OpenGL can make things very fast as long as there is not too many transfer between CPU/GPU memory. Once the data is within the GPU, most transformations (offset/translate/scale/rotate/interpolation/etc) can be made entirely on the GPU side. In the template backend however, the "draw_path" function (for example) receives a path to be rendered and I would need to ensure it is build only once and only applying transforms for subsequent calls.
>
> At least it is my understanding of the matplotlib machinery but I may be wrong.
>
>
> Nicolas
>
>
>
>
>
>
> On Mar 7, 2013, at 15:36 , Michael Droettboom wrote:
>
>> I'm not aware of any discussion about participating in GSoC this year,
>> though I am open to the idea.  I was involved as a mentor a few years
>> ago, but I wasn't terribly involved in the administrative side, so I
>> don't know what's involved.  I think then we did it under the umbrella
>> of the PSF.
>>
>> I'd be interested in exploring a GL backend further.  What, from 10,000
>> m, is the main impedence mismatch between the current matplotlib backend
>> design and OpenGL rendering?
>>
>> Mike
>>
>> On 03/07/2013 03:39 AM, Nicolas Rougier wrote:
>>> Hi all,
>>>
>>> Are there any ongoing project for GSOC 2013 ? I would like to propose something around a GL backend but I'm not still sure OpenGL "philosophy" is compatible with current matplotlib design and any project would require co-mentoring with a matplotlib devel guru. There is a lot of experienced people around (Luke Campagnola, Cyrille Rossant, Almar Klein to name a few) who can help on the GL part and we're also currently trying to design together a common low-level API that may definitely help for the GL backend. What do you think ? Do we need first to make sure a GL backend make any sense at all before going further ?
>>>
>>>
>>> Here is a small set of GL experiments I did recently that make me thinks it should be possible to come up with something nice and fast: https://github.com/rougier/gl-agg
>>>
>>>
>>> Some movies for those who don't want to test:
>>>
>>> http://www.youtube.com/watch?v=T010zMtorAk
>>> http://www.youtube.com/watch?v=iFwEzV9Pw-4
>>>
>>>
>>>
>>>
>>> Nicolas
>>> ------------------------------------------------------------------------------
>>> Symantec Endpoint Protection 12 positioned as A LEADER in The Forrester
>>> Wave(TM): Endpoint Security, Q1 2013 and "remains a good choice" in the
>>> endpoint security space. For insight on selecting the right partner to
>>> tackle endpoint security challenges, access the full report.
>>> http://p.sf.net/sfu/symantec-dev2dev
>>> _______________________________________________
>>> Matplotlib-devel mailing list
>>> Mat...@li...
>>> https://lists.sourceforge.net/lists/listinfo/matplotlib-devel
>>
>> ------------------------------------------------------------------------------
>> Symantec Endpoint Protection 12 positioned as A LEADER in The Forrester
>> Wave(TM): Endpoint Security, Q1 2013 and "remains a good choice" in the
>> endpoint security space. For insight on selecting the right partner to
>> tackle endpoint security challenges, access the full report.
>> http://p.sf.net/sfu/symantec-dev2dev
>> _______________________________________________
>> Matplotlib-devel mailing list
>> Mat...@li...
>> https://lists.sourceforge.net/lists/listinfo/matplotlib-devel

On 03/06/2013 08:28 PM, Jae-Joon Lee wrote:
> While I cannot say much about the "compound renderering" in Agg as I 
> know little about it, but the upsampling method that Mike mentioned 
> seems quite robust.
> Just a quick question, will it affect the rendered quality of AA'ed 
> artists, like texts? What I mean is, drawing texts w/ AA on (in the 
> upsampled canvas) and downsampling the result may give poor quality? 
> But maybe  there is a way to adjust how AA is done in the first place.

In my quick hack, I render the text at the final output resolution, it 
gets upsampled onto the "larger" canvas, and then downsampled back.  
(All this just because it was the easiest hack).  The end result is 
definitely fuzzier.  So we'd have to explore how we do text rendering in 
the new paradigm, particularly wrt hinting (which already contains a lot 
of cleverness).

>
> Maybe what we can try is to upsample only selected artists, similar to 
> rasterization in vector backends or agg_filter. This will likely 
> consume more memory and maybe more CPU, but may give minimal side 
> effects.

That's an interesting idea.

>
> While it is not directly related to the current issue, I thought it 
> would be good to have, within a backend, multiple layers to render and 
> the final result is a composite of those layers.
> And we may upsample some layers or apply effects selectively. And do 
> the alpha compositing of layers in the end. This will be also useful 
> for animations as we can only update selected layers.

Yes -- I've thought we might need this for a while.  Cairo has a very 
useful API -- "push_group"/"pop_group" and friends -- that push layers 
on and off a stack.  It would solve some of the alpha blending problems 
we have because, for example, an entire contour set could be rendered on 
a single layer and then alpha composited at once on the layer below.  
Another side effect of that API is that it makes "stamping" things (as 
we do in draw_marker) possible without each of the backends having to 
understand so much about what a marker is as we have now.

Cheers,
Mike

>
> Back to the original issue, I am inclined to play with Mike's 
> upsamping method to see if it solves the problem without significant 
> side effects.
>
> Regards,
>
> -JJ
>
>
>
> On Thu, Mar 7, 2013 at 6:54 AM, Michael Droettboom <md...@st... 
> <mailto:md...@st...>> wrote:
>
>     Thanks for bringing this up -- I was just looking at the test
>     images the other day and was reminded how filled contouring
>     doesn't look as good as it could be.
>
>     I had played with the "compound renderer" example in Agg some
>     years ago, but couldn't really understand how it worked, so
>     ultimately gave up on it.  I appreciate the research you've done
>     here, because it illustrates pretty clearly what is required.  I
>     guess in the actual Agg draw_path_collection renderer, we would
>     have to build a style_handler table dynamically based on the
>     collection and then iterate through it as we draw...  At least
>     it's clear now how that could be accomplished.
>
>     I wonder, though, and the SVG article you link to hints at this,
>     if we wouldn't be better off just upsampling our Agg rendering
>     instead.  The advantage of such an approach would be that the
>     structure or ordering of the drawing wouldn't matter at all --
>     allaying most of Eric's concerns.  It seems like it just overall
>     be "easier" to do the right thing.  And it should benefit all
>     things, including other tricky things like quadmeshes,
>     "automatically".
>
>     Of course, the downside is a performance hit.  Assuming 2x
>     oversampling, it means the screen buffer will be 4x the size,
>     rendering will take "up to" four times as long, and then you have
>     to downsample again (which in the best case would happen in
>     hardware).  Compound rendering has a memory penalty, too, of
>     course, since the scanlines for all of the paths have to be stored
>     until finally rendered out simultaneously.  Estimating what that
>     overhead is much harder, of course, and is more content dependent,
>     whereas the performance hit upsampling is straightforward and has
>     an obvious upper bound.
>
>     I put together a very quick hack on my agg-upsampling branch,
>     which hardcodes the upsampling to 2x in each direction.  It
>     currently only works with the GtkAgg backend (even file saving
>     won't work correctly), and I only fixed things up for contour
>     plotting, so images (and probably other things) will be
>     misplaced.  But it should give an idea for the performance hit and
>     quality benefit enough to play around with.
>
>     Mike
>
>
>     On 03/06/2013 03:27 PM, Michael Droettboom wrote:
>>     I'm trying to compile your examples, but it seems perhaps you
>>     forget to include a file -- pixel_formats.hpp?  It's not in the
>>     agg24 source tree.
>>
>>     Mike
>>
>>     On 03/06/2013 12:06 PM, Phil Elson wrote:
>>>     Smart rendering of adjacent, anti-aliased patches is a question
>>>     which has come up a couple of times in various guises in the past.
>>>     It is my understanding that the lack of this functionality led
>>>     us to disable anti-aliasing for contouring and is the reason the
>>>     following image has a white stripe around the circle where there
>>>     should be just a nice blend of the two colors:
>>>
>>>
>>>     import matplotlib.pyplot as plt
>>>     import numpy as np
>>>     import matplotlib.patches as mpatches
>>>     import matplotlib.path as mpath
>>>     import matplotlib.collections as mcol
>>>
>>>
>>>     # create two paths. One a circle, the other
>>>     # a square with the same circle cut out.
>>>     x = np.linspace(0, np.pi * 2, 1000)
>>>
>>>     circle_coords = np.array(zip(*[np.sin(x) * 0.8, np.cos(x) * 0.8]))
>>>     pth_circle = mpath.Path(circle_coords)
>>>
>>>     sqr_codes = np.repeat(mpath.Path.MOVETO, len(circle_coords) + 5)
>>>     sqr_codes[1:5] = mpath.Path.LINETO
>>>     sqr_codes[6:] = mpath.Path.LINETO
>>>     sqr_coords = np.concatenate([[[-1, -1], [-1, 1], [1, 1], [1,
>>>     -1], [-1, -1]],
>>>     circle_coords[::-1]], axis=0)
>>>     sqr_path = mpath.Path(sqr_coords, sqr_codes)
>>>
>>>
>>>     ax = plt.axes()
>>>     patches = [mpatches.PathPatch(pth_circle),
>>>     mpatches.PathPatch(sqr_path)]
>>>     col = mcol.PatchCollection(patches,
>>>     antialiaseds=True,
>>>     edgecolors='none',
>>>     facecolors=[(0, 0.0, 0.0, 0.9), (0.1, 0.1, 0.02, 0.9)])
>>>     ax.add_collection(col)
>>>     ax.set_xlim([-1, 1])
>>>     ax.set_ylim([-1, 1])
>>>     plt.show()
>>>
>>>
>>>
>>>     I know of lots of the workarounds for this (turn off AA, turn on
>>>     lines, extend the path slightly, set a dark background color)
>>>     all of which have down-sides, so I'm keen to find a final
>>>     solution to the problem.
>>>
>>>     When the two patches marry up perfectly with full anti-aliasing,
>>>     the antigrain (AGG) community call this "flash" or compound
>>>     rendering, and this capability was added to Agg 2.4 (which we
>>>     already ship with mpl).
>>>
>>>     In order to make full use of the compound rendering technique I
>>>     believe the drawing pipeline in "_backend_agg.cpp" would need to
>>>     change, which could be problematic. A less wide-impacting
>>>     alternative would be to draw all "patches" of a single
>>>     Collection in the same rasterization step (i.e. just change
>>>     _draw_path_collection_generic), though this does mean that, as
>>>     it stands, the result of plt.contourf would not be able to make
>>>     use of this new functionality - a MEP which changes the return
>>>     type of plt.contourf to a single Collection might be able to fix
>>>     that.
>>>
>>>     I've put together a simple example similar to this in C++ using
>>>     agg (no mpl changes yet), showing the differences in the code
>>>     needed between the old technique vs the "new" compound renderer
>>>     (attached).
>>>
>>>
>>>     Ok, so the question to those that have knowledge of the
>>>     _backend_agg.cpp code (Mike, Eric, JJ + others?):
>>>
>>>       * Have you already looked at doing this and determined that
>>>         this is a non-starter?
>>>       * Do you support adding the ability for the agg backend to
>>>         draw compound artists (i.e. Collections) in this way rather
>>>         than treating them as individual primitives in the canvas?
>>>       * Since many of the other backends can't do flash rendering,
>>>         would we even want to make this change?
>>>           o SVG in Firefox 10.0.2 has the same problem, it is
>>>             discussed slightly more in
>>>             http://www.svgopen.org/2002/papers/sorotokin__svg_secrets/
>>>           o Acroread has the same problem with PDFs, only to a much
>>>             lesser extent than in the PNG attached
>>>
>>>
>>>     Thoughts?
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>     ------------------------------------------------------------------------------
>>>     Symantec Endpoint Protection 12 positioned as A LEADER in The Forrester
>>>     Wave(TM): Endpoint Security, Q1 2013 and "remains a good choice" in the
>>>     endpoint security space. For insight on selecting the right partner to
>>>     tackle endpoint security challenges, access the full report.
>>>     http://p.sf.net/sfu/symantec-dev2dev
>>>
>>>
>>>     _______________________________________________
>>>     Matplotlib-devel mailing list
>>>     Mat...@li...  <mailto:Mat...@li...>
>>>     https://lists.sourceforge.net/lists/listinfo/matplotlib-devel
>>
>>
>>
>>     ------------------------------------------------------------------------------
>>     Symantec Endpoint Protection 12 positioned as A LEADER in The Forrester
>>     Wave(TM): Endpoint Security, Q1 2013 and "remains a good choice" in the
>>     endpoint security space. For insight on selecting the right partner to
>>     tackle endpoint security challenges, access the full report.
>>     http://p.sf.net/sfu/symantec-dev2dev
>>
>>
>>     _______________________________________________
>>     Matplotlib-devel mailing list
>>     Mat...@li...  <mailto:Mat...@li...>
>>     https://lists.sourceforge.net/lists/listinfo/matplotlib-devel
>
>
>     ------------------------------------------------------------------------------
>     Symantec Endpoint Protection 12 positioned as A LEADER in The
>     Forrester
>     Wave(TM): Endpoint Security, Q1 2013 and "remains a good choice"
>     in the
>     endpoint security space. For insight on selecting the right partner to
>     tackle endpoint security challenges, access the full report.
>     http://p.sf.net/sfu/symantec-dev2dev
>     _______________________________________________
>     Matplotlib-devel mailing list
>     Mat...@li...
>     <mailto:Mat...@li...>
>     https://lists.sourceforge.net/lists/listinfo/matplotlib-devel
>
>

Indeed, you analyzed/explained the overall situation very well in your blog post: http://mdboom.github.com/blog/2012/08/06/matplotlib-client-side/


OpenGL can make things very fast as long as there is not too many transfer between CPU/GPU memory. Once the data is within the GPU, most transformations (offset/translate/scale/rotate/interpolation/etc) can be made entirely on the GPU side. In the template backend however, the "draw_path" function (for example) receives a path to be rendered and I would need to ensure it is build only once and only applying transforms for subsequent calls.

At least it is my understanding of the matplotlib machinery but I may be wrong.


Nicolas






On Mar 7, 2013, at 15:36 , Michael Droettboom wrote:

> I'm not aware of any discussion about participating in GSoC this year, 
> though I am open to the idea.  I was involved as a mentor a few years 
> ago, but I wasn't terribly involved in the administrative side, so I 
> don't know what's involved.  I think then we did it under the umbrella 
> of the PSF.
> 
> I'd be interested in exploring a GL backend further.  What, from 10,000 
> m, is the main impedence mismatch between the current matplotlib backend 
> design and OpenGL rendering?
> 
> Mike
> 
> On 03/07/2013 03:39 AM, Nicolas Rougier wrote:
>> 
>> Hi all,
>> 
>> Are there any ongoing project for GSOC 2013 ? I would like to propose something around a GL backend but I'm not still sure OpenGL "philosophy" is compatible with current matplotlib design and any project would require co-mentoring with a matplotlib devel guru. There is a lot of experienced people around (Luke Campagnola, Cyrille Rossant, Almar Klein to name a few) who can help on the GL part and we're also currently trying to design together a common low-level API that may definitely help for the GL backend. What do you think ? Do we need first to make sure a GL backend make any sense at all before going further ?
>> 
>> 
>> Here is a small set of GL experiments I did recently that make me thinks it should be possible to come up with something nice and fast: https://github.com/rougier/gl-agg
>> 
>> 
>> Some movies for those who don't want to test:
>> 
>> http://www.youtube.com/watch?v=T010zMtorAk
>> http://www.youtube.com/watch?v=iFwEzV9Pw-4
>> 
>> 
>> 
>> 
>> Nicolas
>> ------------------------------------------------------------------------------
>> Symantec Endpoint Protection 12 positioned as A LEADER in The Forrester
>> Wave(TM): Endpoint Security, Q1 2013 and "remains a good choice" in the
>> endpoint security space. For insight on selecting the right partner to
>> tackle endpoint security challenges, access the full report.
>> http://p.sf.net/sfu/symantec-dev2dev
>> _______________________________________________
>> Matplotlib-devel mailing list
>> Mat...@li...
>> https://lists.sourceforge.net/lists/listinfo/matplotlib-devel
> 
> 
> ------------------------------------------------------------------------------
> Symantec Endpoint Protection 12 positioned as A LEADER in The Forrester  
> Wave(TM): Endpoint Security, Q1 2013 and "remains a good choice" in the  
> endpoint security space. For insight on selecting the right partner to 
> tackle endpoint security challenges, access the full report. 
> http://p.sf.net/sfu/symantec-dev2dev
> _______________________________________________
> Matplotlib-devel mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-devel

On 03/07/2013 04:41 AM, Phil Elson wrote:
> > Would this greatly slow down the rendering?
>
> That is a million dollar question. Without trying it to see, we could 
> ask the agg mailing list to see if they have any insight.
> Personally I'd be surprised if anything we did at the Agg level was a 
> bottleneck given how much python code comes before it - though I admit 
> that doesn't mean anything in practice...

At first glance, I would be concerned about how this scales with the 
number of paths (well, the number of vertices, really).  With regular 
path rendering in Agg, the time grows exponentially with the number of 
vertices in the path, which is why we've put so much effort into path 
simplification etc.  If we use this to render a contour with many 
levels, does it start to break down at some point?

>
>
> > I wonder, though, and the SVG article you link to hints at this, if 
> we wouldn't be better off just upsampling our Agg rendering instead.
> > ...
> > I put together a very quick hack on my agg-upsampling branch, which 
> hardcodes the upsampling to 2x in each direction.
>
> I'm not a big fan of supersampling 
> (http://en.wikipedia.org/wiki/Supersample_anti-aliasing) - that is 
> essentially implementing a global anti-aliasing scheme which one 
> wouldn't be able to control on an artist by artist basis (such as 
> fonts) with the overhead cost of increasing the memory highwater by a 
> factor of 5 (as you say for an image of size (n, m) at a minimum the 
> drawing buffer needs to be (2n x 2m) + then downsampled to (n x m)).

The last buffer can in many cases be done in the GPU, so there isn't 
necessarily an additional main memory buffer...  But that's a minor point.

> We might even want to upscale to a factor of 3 or 4 to really get rid 
> of most of the white in this example.

Maybe it would be useful to quantify the amount of error between the two 
approaches.  At a certain point, it gets below the level of perception 
and we can probably say it's "good enough".

While I understand the performance implications, I like the fact that it 
is bounded.

>
> > From the linked SVG article:
> > Perfect antialiasing can be done by rendering complete artwork at a 
> much higher resolution and subsequent downsampling.
>
> I also dispute this claim. Whilst good anti-aliasing can be achieved 
> using supersampling, I believe /no matter the size of your bigger draw 
> buffer /you will always get a component of background color (white in 
> our case) which goes into the averaging operation and therefore will 
> impact the final pixel color. It is then a compromise between 
> performance vs "good enough" rendering.

I agree.  It's perhaps worth determining how much worse it is and how 
good is "good enough".

>
>
> Having said all of the above, supersampling would be a nice feature to 
> add to mpl's Agg backends (and maybe the Cairo backend too) - though I 
> do not believe it is a full solution to this problem.

I think we agree there is a tradeoff here.  On the one hand, there is a 
mathematically perfect solution that requires some rather pervasive 
refactoring of the code and permanent care in how plots are constructed 
to ensure it's running optimally, and an exponentially growing 
performance curve as the complexity of the plot increases.  On the 
other, is a suboptimal end result that requires minimal changes, and a 
well-defined and upper bound performance penalty.

I think to really determine where on the tradeoff we want to be, some 
more experiments comparing the quality of the end result of the two 
approaches would be helpful.

Mike

>
> Cheers,
>
>
>
>
>
>
>
>
>
>
>
>
> On 7 March 2013 09:15, Phil Elson <pel...@gm... 
> <mailto:pel...@gm...>> wrote:
>
>     > I'm trying to compile your examples, but it seems perhaps you
>     forget to include a file
>
>     Sorry, you can find pixel_formats.h in the examples directory. I
>     have a mirror of agg here:
>     https://github.com/pelson/antigrain/tree/master/agg-2.4/examples
>
>     My compile steps are:
>
>     export AGG_DIR=<path to antigrain checkout>
>
>     g++ -c -O3 -I${AGG_DIR}/include -I${AGG_DIR}/examples
>     -L${AGG_DIR}/src basic_path.cpp -o basic_path.o
>     g++ -O3 -I${AGG_DIR}/include -L${AGG_DIR}/src basic_path.o -o
>     basic_path -lagg
>
>
>     > I had played with the "compound renderer" example in Agg some
>     years ago, but couldn't really understand how it worked
>
>     Agreed. It took the best part of a day to figure out how to do it,
>     given no prior knowledge of Agg. I intend to submit a couple of
>     simpler (GUI-less) examples to be included with the agg repo - and
>     perhaps even write some documentation for this stuff!
>
>
>     > Does it work with alpha < 1?
>
>     Yes. I've attached the two images (converted from ppm to png using
>     gimp + default settings) so that you can see the results. Each
>     shape has an alpha value of 200.
>
>     Inline images 1Inline images 2
>
>     HTH
>
>
>
>
>
>     On 6 March 2013 20:27, Michael Droettboom <md...@st...
>     <mailto:md...@st...>> wrote:
>
>         I'm trying to compile your examples, but it seems perhaps you
>         forget to include a file -- pixel_formats.hpp?  It's not in
>         the agg24 source tree.
>
>         Mike
>
>
>         On 03/06/2013 12:06 PM, Phil Elson wrote:
>>         Smart rendering of adjacent, anti-aliased patches is a
>>         question which has come up a couple of times in various
>>         guises in the past.
>>         It is my understanding that the lack of this functionality
>>         led us to disable anti-aliasing for contouring and is the
>>         reason the following image has a white stripe around the
>>         circle where there should be just a nice blend of the two colors:
>>
>>
>>         import matplotlib.pyplot as plt
>>         import numpy as np
>>         import matplotlib.patches as mpatches
>>         import matplotlib.path as mpath
>>         import matplotlib.collections as mcol
>>
>>
>>         # create two paths. One a circle, the other
>>         # a square with the same circle cut out.
>>         x = np.linspace(0, np.pi * 2, 1000)
>>
>>         circle_coords = np.array(zip(*[np.sin(x) * 0.8, np.cos(x) *
>>         0.8]))
>>         pth_circle = mpath.Path(circle_coords)
>>
>>         sqr_codes = np.repeat(mpath.Path.MOVETO, len(circle_coords) + 5)
>>         sqr_codes[1:5] = mpath.Path.LINETO
>>         sqr_codes[6:] = mpath.Path.LINETO
>>         sqr_coords = np.concatenate([[[-1, -1], [-1, 1], [1, 1], [1,
>>         -1], [-1, -1]],
>>         circle_coords[::-1]], axis=0)
>>         sqr_path = mpath.Path(sqr_coords, sqr_codes)
>>
>>
>>         ax = plt.axes()
>>         patches = [mpatches.PathPatch(pth_circle),
>>         mpatches.PathPatch(sqr_path)]
>>         col = mcol.PatchCollection(patches,
>>         antialiaseds=True,
>>         edgecolors='none',
>>         facecolors=[(0, 0.0, 0.0, 0.9), (0.1, 0.1, 0.02, 0.9)])
>>         ax.add_collection(col)
>>         ax.set_xlim([-1, 1])
>>         ax.set_ylim([-1, 1])
>>         plt.show()
>>
>>
>>
>>         I know of lots of the workarounds for this (turn off AA, turn
>>         on lines, extend the path slightly, set a dark background
>>         color) all of which have down-sides, so I'm keen to find a
>>         final solution to the problem.
>>
>>         When the two patches marry up perfectly with full
>>         anti-aliasing, the antigrain (AGG) community call this
>>         "flash" or compound rendering, and this capability was added
>>         to Agg 2.4 (which we already ship with mpl).
>>
>>         In order to make full use of the compound rendering technique
>>         I believe the drawing pipeline in "_backend_agg.cpp" would
>>         need to change, which could be problematic. A less
>>         wide-impacting alternative would be to draw all "patches" of
>>         a single Collection in the same rasterization step (i.e. just
>>         change _draw_path_collection_generic), though this does mean
>>         that, as it stands, the result of plt.contourf would not be
>>         able to make use of this new functionality - a MEP which
>>         changes the return type of plt.contourf to a single
>>         Collection might be able to fix that.
>>
>>         I've put together a simple example similar to this in C++
>>         using agg (no mpl changes yet), showing the differences in
>>         the code needed between the old technique vs the "new"
>>         compound renderer (attached).
>>
>>
>>         Ok, so the question to those that have knowledge of the
>>         _backend_agg.cpp code (Mike, Eric, JJ + others?):
>>
>>           * Have you already looked at doing this and determined that
>>             this is a non-starter?
>>           * Do you support adding the ability for the agg backend to
>>             draw compound artists (i.e. Collections) in this way
>>             rather than treating them as individual primitives in the
>>             canvas?
>>           * Since many of the other backends can't do flash
>>             rendering, would we even want to make this change?
>>               o SVG in Firefox 10.0.2 has the same problem, it is
>>                 discussed slightly more in
>>                 http://www.svgopen.org/2002/papers/sorotokin__svg_secrets/
>>               o Acroread has the same problem with PDFs, only to a
>>                 much lesser extent than in the PNG attached
>>
>>
>>         Thoughts?
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>         ------------------------------------------------------------------------------
>>         Symantec Endpoint Protection 12 positioned as A LEADER in The Forrester
>>         Wave(TM): Endpoint Security, Q1 2013 and "remains a good choice" in the
>>         endpoint security space. For insight on selecting the right partner to
>>         tackle endpoint security challenges, access the full report.
>>         http://p.sf.net/sfu/symantec-dev2dev
>>
>>
>>         _______________________________________________
>>         Matplotlib-devel mailing list
>>         Mat...@li...  <mailto:Mat...@li...>
>>         https://lists.sourceforge.net/lists/listinfo/matplotlib-devel
>
>
>         ------------------------------------------------------------------------------
>         Symantec Endpoint Protection 12 positioned as A LEADER in The
>         Forrester
>         Wave(TM): Endpoint Security, Q1 2013 and "remains a good
>         choice" in the
>         endpoint security space. For insight on selecting the right
>         partner to
>         tackle endpoint security challenges, access the full report.
>         http://p.sf.net/sfu/symantec-dev2dev
>         _______________________________________________
>         Matplotlib-devel mailing list
>         Mat...@li...
>         <mailto:Mat...@li...>
>         https://lists.sourceforge.net/lists/listinfo/matplotlib-devel
>
>
>