matplotlib-devel Mailing List for matplotlib (Page 3)

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

Amit,
On 6 March 2013 20:20, Amit Aronovitch <aro...@gm...> wrote:
> So, "working"/"not working" test (possibly including some time
> measurements) I can do on a fairly short notice.
> Producing some more examples that fail with the current code might require
> several hours of work, so would probably get delayed for a few weeks.
>
The quick working/not working tests without time measurements would be
ample, thanks. I will be writing a set of small tests within the mpl
testing framework for specific cases that currently fail, but it will be
useful to check on some of the big datasets that you use.
Ian

On 6 March 2013 19:10, Chris Barker - NOAA Federal <chr...@no...>wrote:
> """
> Qhull does not support triangulation of non-convex surfaces, mesh
> generation of non-convex objects, ... or constrained Delaunay
> triangulations
> """
> these are key to my needs, but are they for MPL?
>
No, all we need is a drop-in replacement for the existing code, i.e. 2D
unconstrained Delaunay. Further functionality is outside our remit of
"plotting library".
I've also got a colleague with some nice code based on Donald Knuth's
> Axioms and Hulls monograph. If someone it interested in adding some
> tests and python wrappers, I'll bet he'd be glad to release it with a
> suitable license. (C++).
We've talked about this before and previously I have been interested. But
now I think it is unnecessary to maintain our own code when we have a
drop-in replacement that has worked so well for scipy.
Ian

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

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...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-devel

Thanks Ian.
These examples occured when I processed large propriatary datasets. So far,
scipy's triangulation worked whenever matplotlib failed.
When we have a new implementation, it should be quite simple to check if it
works where it had previously failed.
Certainly easier than slicing the data to small chunks and trying to
distill a failing example of reasonable size as I did in this case.
So, "working"/"not working" test (possibly including some time
measurements) I can do on a fairly short notice.
Producing some more examples that fail with the current code might require
several hours of work, so would probably get delayed for a few weeks.
 Amit
On Wed, Mar 6, 2013 at 10:53 AM, Ian Thomas <ian...@gm...> wrote:
> Hi Amit,
>
> I am with you 100% of the way. We should use an existing open source
> Delaunay triangulator, and my preference is for QHull as well.
>
> "Improved Delaunay triangulator" is on my matplotlib todo list, albeit it
> quite a long way from the top. I don't tend to use the existing code as I
> usually specify my own triangulations, so I have never seen anything quite
> as embarrassing as issue #1809. Perhaps I need to bump it up my priority
> list.
>
> If I come up with a possible solution as a PR, would you be prepared to
> help test it? You seem to have quite a few examples that don't work under
> the existing code and would be very useful for demonstrating if the
> improved code is indeed an improvement.
>
> Ian
>
>
> On 5 March 2013 23:08, Amit Aronovitch <aro...@gm...> wrote:
>
>> Dear MPL-devs,
>>
>> Currently, matplotlib does Delaunay triangulation using a special
>> purpose module written in C++ (if I'm not mistaken, it was originally
>> forked off from some SciKit and wrapped into a Python module).
>> Some people (here and on github issues) had suggested it might need some
>> rewrites/modification.
>> In particular I was wondering if we should continue maintaining it here
>> or maybe switch to using some external library.
>>
>> Since triangulation is not a plotting-specific problem, and some free
>> libraries are available for solving it, we might actually benefit in
>> terms of efficiency and robustness.
>>
>> Specifically, I had suggested QHull, which is used by scipy (note that
>> now there is also a stand-alone python interface:
>> https://pypi.python.org/pypi/pyhull - I did not check that out yet).
>> @dmcdougall had suggested Jonathan Shewchuk's triangle library (we
>> should check the license though - I think it is "for non-commercial
>> use", unlike mpl). There are also other alternatives.
>>
>> On the other hand, there's the issue of minimizing external
>> dependencies. I think @ianthomas23 had once mentioned that he is happy
>> with having Delaunay code reside in mpl (and, of course, "maintainable"
>> is whatever is most convenient for the maintainers).
>>
>> I apologize for suggesting more tasks without contributing time to work
>> on them. Just thought that since I finally sat down to report issue
>> #1809 (which seems to be a particularly slippery bug in the code
>> mentioned above), it might be a good time to discuss this topic again.
>>
>> thanks,
>>
>> Amit Aronovitch
>>
>

On 2013年03月06日 7:06 AM, 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?
Phil,
Would this greatly slow down the rendering?
Does it work with alpha < 1?
I'm initially not enthusiastic about having contourf return a single 
Collection, but maybe in practice it would not make much difference. 
The drawback, apart from code brakeage, is that it would remove the 
ability to pick out a level for additional customization.
Could this be handled at a subsequent level, by having the renderer able 
to treat an arbitrary collection of artists as a group?
It seems that contourf is where this "flash" capability would be most 
important; if it can't be made to work there, I think it might not be 
worth the trouble to add.
Eric

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?
 - 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/
 - Acroread has the same problem with PDFs, only to a much lesser
 extent than in the PNG attached
Thoughts?

Hi Amit,
I am with you 100% of the way. We should use an existing open source
Delaunay triangulator, and my preference is for QHull as well.
"Improved Delaunay triangulator" is on my matplotlib todo list, albeit it
quite a long way from the top. I don't tend to use the existing code as I
usually specify my own triangulations, so I have never seen anything quite
as embarrassing as issue #1809. Perhaps I need to bump it up my priority
list.
If I come up with a possible solution as a PR, would you be prepared to
help test it? You seem to have quite a few examples that don't work under
the existing code and would be very useful for demonstrating if the
improved code is indeed an improvement.
Ian
On 5 March 2013 23:08, Amit Aronovitch <aro...@gm...> wrote:
> Dear MPL-devs,
>
> Currently, matplotlib does Delaunay triangulation using a special
> purpose module written in C++ (if I'm not mistaken, it was originally
> forked off from some SciKit and wrapped into a Python module).
> Some people (here and on github issues) had suggested it might need some
> rewrites/modification.
> In particular I was wondering if we should continue maintaining it here
> or maybe switch to using some external library.
>
> Since triangulation is not a plotting-specific problem, and some free
> libraries are available for solving it, we might actually benefit in
> terms of efficiency and robustness.
>
> Specifically, I had suggested QHull, which is used by scipy (note that
> now there is also a stand-alone python interface:
> https://pypi.python.org/pypi/pyhull - I did not check that out yet).
> @dmcdougall had suggested Jonathan Shewchuk's triangle library (we
> should check the license though - I think it is "for non-commercial
> use", unlike mpl). There are also other alternatives.
>
> On the other hand, there's the issue of minimizing external
> dependencies. I think @ianthomas23 had once mentioned that he is happy
> with having Delaunay code reside in mpl (and, of course, "maintainable"
> is whatever is most convenient for the maintainers).
>
> I apologize for suggesting more tasks without contributing time to work
> on them. Just thought that since I finally sat down to report issue
> #1809 (which seems to be a particularly slippery bug in the code
> mentioned above), it might be a good time to discuss this topic again.
>
> thanks,
>
> Amit Aronovitch
>

Dear MPL-devs,
Currently, matplotlib does Delaunay triangulation using a special 
purpose module written in C++ (if I'm not mistaken, it was originally 
forked off from some SciKit and wrapped into a Python module).
Some people (here and on github issues) had suggested it might need some 
rewrites/modification.
In particular I was wondering if we should continue maintaining it here 
or maybe switch to using some external library.
Since triangulation is not a plotting-specific problem, and some free 
libraries are available for solving it, we might actually benefit in 
terms of efficiency and robustness.
Specifically, I had suggested QHull, which is used by scipy (note that 
now there is also a stand-alone python interface: 
https://pypi.python.org/pypi/pyhull - I did not check that out yet).
@dmcdougall had suggested Jonathan Shewchuk's triangle library (we 
should check the license though - I think it is "for non-commercial 
use", unlike mpl). There are also other alternatives.
On the other hand, there's the issue of minimizing external 
dependencies. I think @ianthomas23 had once mentioned that he is happy 
with having Delaunay code reside in mpl (and, of course, "maintainable" 
is whatever is most convenient for the maintainers).
I apologize for suggesting more tasks without contributing time to work 
on them. Just thought that since I finally sat down to report issue 
#1809 (which seems to be a particularly slippery bug in the code 
mentioned above), it might be a good time to discuss this topic again.
thanks,
 Amit Aronovitch

On Mon, Mar 4, 2013 at 6:44 AM, David Verelst <dav...@gm...> wrote:
>
> sorry...forgot to tag the subject as [matplotlib-devel]
The server does it for you.
>
> On 4 March 2013 13:35, David Verelst <dav...@gm...> wrote:
>>
>> Hi,
>>
>> I am running Arch Linux, Matplotlib 1.2, Python 2.7, and today I realized
>> that generating *.eps figures when matplotlib is using the latex output
>> rc('text', usetex=True) results in a corrupted eps figure in combination
>> with Ghostscript 9.07. The png variant of the same figure works fine, eps
>> works fine if usetex=False. Everything works fine when I downgrade back to
>> Ghostscript 9.06.
>>
>> Is this related to this?
>> https://github.com/matplotlib/matplotlib/issues/1693
>> https://github.com/matplotlib/matplotlib/pull/1694
>> If it is, I guess the problem is already solved. Haven't tested that yet
>> (need to build matplitlib from git first...)
>>
>> I have no idea if this is Arch Linux packaging, Ghostscript or Matplotlib
>> issue...hence this email.
>>
>> Regards,
>> David
>>
>>
>> An example, from: http://matplotlib.org/users/usetex.html
>>
>> #!/usr/bin/env python
>> """
>> You can use TeX to render all of your matplotlib text if the rc
>> parameter text.usetex is set. This works currently on the agg and ps
>> backends, and requires that you have tex and the other dependencies
>> described at http://matplotlib.sf.net/matplotlib.texmanager.html
>> properly installed on your system. The first time you run a script
>> you will see a lot of output from tex and associated tools. The next
>> time, the run may be silent, as a lot of the information is cached in
>> ~/.tex.cache
>>
>> """
>> from matplotlib import rc
>> from numpy import arange, cos, pi
>> from matplotlib.pyplot import figure, axes, plot, xlabel, ylabel, title, \
>> grid, savefig, show
>>
>>
>> rc('text', usetex=True)
>> rc('font', family='serif')
>> figure(1, figsize=(6,4))
>> ax = axes([0.1, 0.1, 0.8, 0.7])
>> t = arange(0.0, 1.0+0.01, 0.01)
>> s = cos(2*2*pi*t)+2
>> plot(t, s)
>>
>> xlabel(r'\textbf{time (s)}')
>> ylabel(r'\textit{voltage (mV)}',fontsize=16)
>> title(r"\TeX\ is Number
>> $\displaystyle\sum_{n=1}^\infty\frac{-e^{i\pi}}{2^n}$!",
>> fontsize=16, color='r')
>> grid(True)
>> savefig('tex_demo.eps')
>> savefig('tex_demo.png')
>>
>> show()
>>
>>
>> When converting the eps figure with imagemagick (just to check the file),
>> the following error is given:
>> $ convert Desktop/tex_demo.eps ddd.eps
>> Error: /dictstackunderflow in --end--
>> Operand stack:
>>
>> Execution stack:
>> %interp_exit .runexec2 --nostringval-- --nostringval--
>> --nostringval-- 2 %stopped_push --nostringval-- --nostringval--
>> --nostringval-- false 1 %stopped_push 1900 1 3 %oparray_pop
>> 1899 1 3 %oparray_pop --nostringval-- 1883 1 3 %oparray_pop
>> 1771 1 3 %oparray_pop --nostringval-- %errorexec_pop .runexec2
>> --nostringval-- --nostringval-- --nostringval-- 2 %stopped_push
>> --nostringval--
>> Dictionary stack:
>> --dict:1169/1684(ro)(G)-- --dict:0/20(G)-- --dict:82/200(L)--
>> Current allocation mode is local
>> Last OS error: No such file or directory
>> Current file position is 102614
>> GPL Ghostscript 9.07: Unrecoverable error, exit code 1
>> Error: /dictstackunderflow in --end--
>> Operand stack:
>>
>> Execution stack:
>> %interp_exit .runexec2 --nostringval-- --nostringval--
>> --nostringval-- 2 %stopped_push --nostringval-- --nostringval--
>> --nostringval-- false 1 %stopped_push 1900 1 3 %oparray_pop
>> 1899 1 3 %oparray_pop --nostringval-- 1883 1 3 %oparray_pop
>> 1771 1 3 %oparray_pop --nostringval-- %errorexec_pop .runexec2
>> --nostringval-- --nostringval-- --nostringval-- 2 %stopped_push
>> --nostringval--
>> Dictionary stack:
>> --dict:1169/1684(ro)(G)-- --dict:0/20(G)-- --dict:82/200(L)--
>> Current allocation mode is local
>> Last OS error: No such file or directory
>> Current file position is 102614
>> GPL Ghostscript 9.07: Unrecoverable error, exit code 1
>> convert: Postscript delegate failed `Desktop/tex_demo.eps': No such file
>> or directory @ error/ps.c/ReadPSImage/836.
>> convert: no images defined `ddd.eps' @
>> error/convert.c/ConvertImageCommand/3068.
>>
>
>
> ------------------------------------------------------------------------------
> Everyone hates slow websites. So do we.
> Make your web apps faster with AppDynamics
> Download AppDynamics Lite for free today:
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>
-- 
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

sorry...forgot to tag the subject as [matplotlib-devel]
On 4 March 2013 13:35, David Verelst <dav...@gm...> wrote:
> Hi,
>
> I am running Arch Linux, Matplotlib 1.2, Python 2.7, and today I realized
> that generating *.eps figures when matplotlib is using the latex output
> rc('text', usetex=True) results in a corrupted eps figure in combination
> with Ghostscript 9.07. The png variant of the same figure works fine, eps
> works fine if usetex=False. Everything works fine when I downgrade back to
> Ghostscript 9.06.
>
> Is this related to this?
> https://github.com/matplotlib/matplotlib/issues/1693
> https://github.com/matplotlib/matplotlib/pull/1694
> If it is, I guess the problem is already solved. Haven't tested that yet
> (need to build matplitlib from git first...)
>
> I have no idea if this is Arch Linux packaging, Ghostscript or Matplotlib
> issue...hence this email.
>
> Regards,
> David
>
>
> An example, from: http://matplotlib.org/users/usetex.html
>
> #!/usr/bin/env python"""You can use TeX to render all of your matplotlib text if the rcparameter text.usetex is set. This works currently on the agg and psbackends, and requires that you have tex and the other dependenciesdescribed at http://matplotlib.sf.net/matplotlib.texmanager.htmlproperly installed on your system. The first time you run a scriptyou will see a lot of output from tex and associated tools. The nexttime, the run may be silent, as a lot of the information is cached in~/.tex.cache
> """from matplotlib import rcfrom numpy import arange, cos, pifrom matplotlib.pyplot import figure, axes, plot, xlabel, ylabel, title, \
> grid, savefig, show
>
> rc('text', usetex=True)rc('font', family='serif')figure(1, figsize=(6,4))ax = axes([0.1, 0.1, 0.8, 0.7])t = arange(0.0, 1.0+0.01, 0.01)s = cos(2*2*pi*t)+2plot(t, s)
> xlabel(r'\textbf{time (s)}')ylabel(r'\textit{voltage (mV)}',fontsize=16)title(r"\TeX\ is Number $\displaystyle\sum_{n=1}^\infty\frac{-e^{i\pi}}{2^n}$!",
> fontsize=16, color='r')grid(True)savefig('tex_demo.eps')savefig('tex_demo.png')
> show()
>
>
> When converting the eps figure with imagemagick (just to check the file),
> the following error is given:
> $ convert Desktop/tex_demo.eps ddd.eps
> Error: /dictstackunderflow in --end--
> Operand stack:
>
> Execution stack:
> %interp_exit .runexec2 --nostringval-- --nostringval--
> --nostringval-- 2 %stopped_push --nostringval-- --nostringval--
> --nostringval-- false 1 %stopped_push 1900 1 3 %oparray_pop
> 1899 1 3 %oparray_pop --nostringval-- 1883 1 3
> %oparray_pop 1771 1 3 %oparray_pop --nostringval--
> %errorexec_pop .runexec2 --nostringval-- --nostringval--
> --nostringval-- 2 %stopped_push --nostringval--
> Dictionary stack:
> --dict:1169/1684(ro)(G)-- --dict:0/20(G)-- --dict:82/200(L)--
> Current allocation mode is local
> Last OS error: No such file or directory
> Current file position is 102614
> GPL Ghostscript 9.07: Unrecoverable error, exit code 1
> Error: /dictstackunderflow in --end--
> Operand stack:
>
> Execution stack:
> %interp_exit .runexec2 --nostringval-- --nostringval--
> --nostringval-- 2 %stopped_push --nostringval-- --nostringval--
> --nostringval-- false 1 %stopped_push 1900 1 3 %oparray_pop
> 1899 1 3 %oparray_pop --nostringval-- 1883 1 3
> %oparray_pop 1771 1 3 %oparray_pop --nostringval--
> %errorexec_pop .runexec2 --nostringval-- --nostringval--
> --nostringval-- 2 %stopped_push --nostringval--
> Dictionary stack:
> --dict:1169/1684(ro)(G)-- --dict:0/20(G)-- --dict:82/200(L)--
> Current allocation mode is local
> Last OS error: No such file or directory
> Current file position is 102614
> GPL Ghostscript 9.07: Unrecoverable error, exit code 1
> convert: Postscript delegate failed `Desktop/tex_demo.eps': No such file
> or directory @ error/ps.c/ReadPSImage/836.
> convert: no images defined `ddd.eps' @
> error/convert.c/ConvertImageCommand/3068.
>
>

On 2 March 2013 23:19, Thomas Kluyver <th...@kl...> wrote:
> Not directly - it runs on a completely automated build server. I've just
> pushed a commit to the packaging rules which will try this the next time it
> does the build. But it's not exactly instant feedback for debugging ;-).
> Hopefully once we see the ImportError, it will be clear what we need to
> change.
>
The build environment was missing pyparsing. That doesn't affect compiling,
but matplotlib can't be imported without it. I've added pyparsing to the
build dependencies, so hopefully the next build will complete.
Thanks,
Thomas

Hi,
I am running Arch Linux, Matplotlib 1.2, Python 2.7, and today I realized
that generating *.eps figures when matplotlib is using the latex
output rc('text',
usetex=True) results in a corrupted eps figure in combination with
Ghostscript 9.07. The png variant of the same figure works fine, eps works
fine if usetex=False. Everything works fine when I downgrade back to
Ghostscript 9.06.
Is this related to this?
https://github.com/matplotlib/matplotlib/issues/1693
https://github.com/matplotlib/matplotlib/pull/1694
If it is, I guess the problem is already solved. Haven't tested that yet
(need to build matplitlib from git first...)
I have no idea if this is Arch Linux packaging, Ghostscript or Matplotlib
issue...hence this email.
Regards,
David
An example, from: http://matplotlib.org/users/usetex.html
#!/usr/bin/env python"""You can use TeX to render all of your
matplotlib text if the rcparameter text.usetex is set. This works
currently on the agg and psbackends, and requires that you have tex
and the other dependenciesdescribed at
http://matplotlib.sf.net/matplotlib.texmanager.htmlproperly installed
on your system. The first time you run a scriptyou will see a lot of
output from tex and associated tools. The nexttime, the run may be
silent, as a lot of the information is cached in~/.tex.cache
"""from matplotlib import rcfrom numpy import arange, cos, pifrom
matplotlib.pyplot import figure, axes, plot, xlabel, ylabel, title, \
 grid, savefig, show
rc('text', usetex=True)rc('font', family='serif')figure(1,
figsize=(6,4))ax = axes([0.1, 0.1, 0.8, 0.7])t = arange(0.0, 1.0+0.01,
0.01)s = cos(2*2*pi*t)+2plot(t, s)
xlabel(r'\textbf{time (s)}')ylabel(r'\textit{voltage
(mV)}',fontsize=16)title(r"\TeX\ is Number
$\displaystyle\sum_{n=1}^\infty\frac{-e^{i\pi}}{2^n}$!",
 fontsize=16,
color='r')grid(True)savefig('tex_demo.eps')savefig('tex_demo.png')
show()
When converting the eps figure with imagemagick (just to check the file),
the following error is given:
$ convert Desktop/tex_demo.eps ddd.eps
Error: /dictstackunderflow in --end--
Operand stack:
Execution stack:
 %interp_exit .runexec2 --nostringval-- --nostringval--
--nostringval-- 2 %stopped_push --nostringval-- --nostringval--
--nostringval-- false 1 %stopped_push 1900 1 3 %oparray_pop
1899 1 3 %oparray_pop --nostringval-- 1883 1 3
%oparray_pop 1771 1 3 %oparray_pop --nostringval--
%errorexec_pop .runexec2 --nostringval-- --nostringval--
--nostringval-- 2 %stopped_push --nostringval--
Dictionary stack:
 --dict:1169/1684(ro)(G)-- --dict:0/20(G)-- --dict:82/200(L)--
Current allocation mode is local
Last OS error: No such file or directory
Current file position is 102614
GPL Ghostscript 9.07: Unrecoverable error, exit code 1
Error: /dictstackunderflow in --end--
Operand stack:
Execution stack:
 %interp_exit .runexec2 --nostringval-- --nostringval--
--nostringval-- 2 %stopped_push --nostringval-- --nostringval--
--nostringval-- false 1 %stopped_push 1900 1 3 %oparray_pop
1899 1 3 %oparray_pop --nostringval-- 1883 1 3
%oparray_pop 1771 1 3 %oparray_pop --nostringval--
%errorexec_pop .runexec2 --nostringval-- --nostringval--
--nostringval-- 2 %stopped_push --nostringval--
Dictionary stack:
 --dict:1169/1684(ro)(G)-- --dict:0/20(G)-- --dict:82/200(L)--
Current allocation mode is local
Last OS error: No such file or directory
Current file position is 102614
GPL Ghostscript 9.07: Unrecoverable error, exit code 1
convert: Postscript delegate failed `Desktop/tex_demo.eps': No such file or
directory @ error/ps.c/ReadPSImage/836.
convert: no images defined `ddd.eps' @
error/convert.c/ConvertImageCommand/3068.

Nothing springs to mind. Perhaps the install is failing due to
https://github.com/matplotlib/matplotlib/pull/1454?
Sounds like you don't have access to the machine to check that
$> python -c "import matplotlib"
Works?
On 2 March 2013 17:25, Thomas Kluyver <th...@kl...> wrote:
> The Launchpad daily builds have recently started failing on the docs
> build. I get the error message "Error: matplotlib must be installed before
> building the documentation", which means that there's an ImportError raised
> when it tries to import matplotlib. Unfortunately, it hides any other
> information about the ImportError, and I can't currently replicate it on my
> own computer.
>
> It builds the Python library, then attempts to build the docs with the
> command:
>
> cd doc ; MATPLOTLIBDATA=../lib/matplotlib/mpl-data/ \
> PYTHONPATH=../build/lib.linux-i686-2.7 ./make.py --small all
>
> Does anyone know what might have changed a few days ago to cause this? It
> built successfully on the 26th of February, there was a different problem
> on the 27th, and when I resolved that, this failure started on the 28th.
> I've had a look at the commit log, but nothing jumps out.
>
> Thanks,
> Thomas
>
>
> ------------------------------------------------------------------------------
> 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_feb
> _______________________________________________
> Matplotlib-devel mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-devel
>
>

The Launchpad daily builds have recently started failing on the docs build.
I get the error message "Error: matplotlib must be installed before
building the documentation", which means that there's an ImportError raised
when it tries to import matplotlib. Unfortunately, it hides any other
information about the ImportError, and I can't currently replicate it on my
own computer.
It builds the Python library, then attempts to build the docs with the
command:
cd doc ; MATPLOTLIBDATA=../lib/matplotlib/mpl-data/ \
 PYTHONPATH=../build/lib.linux-i686-2.7 ./make.py --small all
Does anyone know what might have changed a few days ago to cause this? It
built successfully on the 26th of February, there was a different problem
on the 27th, and when I resolved that, this failure started on the 28th.
I've had a look at the commit log, but nothing jumps out.
Thanks,
Thomas

On Thu, Feb 28, 2013 at 11:24 AM, Nelle Varoquaux
<nel...@gm...> wrote:
> Hello,
>
> Since I've updated my master branch, I have a new error when installing
> matplotlib. It seems it doesn't find a header file from numpy:
>
> I've reinstalled numpy (development version), and I tried to reinstall
> everything (on python 2.6) but I still get the error. Am I the only one
> having installation problems ?
>
> Here is the (partial) traceback:
>
> building 'matplotlib._png' extension
> gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O2 -Wall -fPIC
> -DPY_ARRAY_UNIQUE_SYMBOL=MPL_ARRAY_API -DPYCXX_ISO_CPP_LIB=1
> -I/usr/local/include -I/usr/include -I. -I/usr/include/libpng12
> -I/usr/lib/pymodules/python2.6/numpy/core/include -I/usr/include/python2.6
> -c src/_png.cpp -o build/temp.linux-x86_64-2.6/src/_png.o
> In file included from src/_png.cpp:31:
> src/file_compat.h:4:32: error: numpy/npy_3kcompat.h: No such file or
> directory
> In file included from src/_png.cpp:31:
> src/file_compat.h: In function ‘int npy_PyFile_CloseFile(PyObject*)’:
> src/file_compat.h:125: warning: deprecated conversion from string constant
> to ‘char*’
> src/_png.cpp: In member function ‘Py::Object _png_module::write_png(const
> Py::Tuple&)’:
> src/_png.cpp:137: error: ‘npy_PyFile_OpenFile’ was not declared in this
> scope
> src/_png.cpp:147: error: ‘npy_PyFile_Dup’ was not declared in this scope
> src/_png.cpp:243: error: ‘npy_PyFile_DupClose’ was not declared in this
> scope
> src/_png.cpp:264: error: ‘npy_PyFile_DupClose’ was not declared in this
> scope
> src/_png.cpp: In member function ‘PyObject* _png_module::_read_png(const
> Py::Object&, bool, int)’:
> src/_png.cpp:321: error: ‘npy_PyFile_OpenFile’ was not declared in this
> scope
> src/_png.cpp:329: error: ‘npy_PyFile_Dup’ was not declared in this scope
> src/_png.cpp:577: error: ‘npy_PyFile_DupClose’ was not declared in this
> scope
> error: command 'gcc' failed with exit status 1
>
> Thanks,
> N
>
> ------------------------------------------------------------------------------
> 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_feb
> _______________________________________________
> Matplotlib-devel mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-devel
>
That's weird. Did you try installing a stable numpy version instead?
I tried compiling mpl against numpy 1.6.2 and everything worked out
fine.
-- 
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

I often find that a `git clean -fxd` is enough, rather than blitzing my 
whole virtualenv.
On 03/01/2013 10:16 AM, Nelle Varoquaux wrote:
>
>
> That's weird. Did you try installing a stable numpy version instead?
> I tried compiling mpl against numpy 1.6.2 and everything worked out
> fine.
>
>
> I'm reinstalled everything, and it works fine. I still have problems 
> when I switch from an old branch (before the merge of the packaging 
> changes) and master, but I've resigned myself to destroy my virtualenv 
> each time.
>
> Thanks for the inputs !
> N
>
>
> --
> 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
>
>
>
>
> ------------------------------------------------------------------------------
> 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_feb
>
>
> _______________________________________________
> Matplotlib-devel mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-devel

>
>
> That's weird. Did you try installing a stable numpy version instead?
> I tried compiling mpl against numpy 1.6.2 and everything worked out
> fine.
>
I'm reinstalled everything, and it works fine. I still have problems when I
switch from an old branch (before the merge of the packaging changes) and
master, but I've resigned myself to destroy my virtualenv each time.
Thanks for the inputs !
N
>
> --
> 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
>

It thinks the Numpy header files are here:
/usr/lib/pymodules/python2.6/numpy/core/include
are they there and from the right version of Numpy?
When you run python (the same copy you're building with), and import 
numpy and print "numpy.__version__" does that give the expected 
version. What does "numpy.__file__" and "numpy.get_version()" give?
Mike
On 02/28/2013 11:24 AM, Nelle Varoquaux wrote:
> Hello,
>
> Since I've updated my master branch, I have a new error when 
> installing matplotlib. It seems it doesn't find a header file from numpy:
>
> I've reinstalled numpy (development version), and I tried to reinstall 
> everything (on python 2.6) but I still get the error. Am I the only 
> one having installation problems ?
>
> Here is the (partial) traceback:
>
> building 'matplotlib._png' extension
> gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O2 -Wall -fPIC 
> -DPY_ARRAY_UNIQUE_SYMBOL=MPL_ARRAY_API -DPYCXX_ISO_CPP_LIB=1 
> -I/usr/local/include -I/usr/include -I. -I/usr/include/libpng12 
> -I/usr/lib/pymodules/python2.6/numpy/core/include 
> -I/usr/include/python2.6 -c src/_png.cpp -o 
> build/temp.linux-x86_64-2.6/src/_png.o
> In file included from src/_png.cpp:31:
> src/file_compat.h:4:32: error: numpy/npy_3kcompat.h: No such file or 
> directory
> In file included from src/_png.cpp:31:
> src/file_compat.h: In function 'int npy_PyFile_CloseFile(PyObject*)':
> src/file_compat.h:125: warning: deprecated conversion from string 
> constant to 'char*'
> src/_png.cpp: In member function 'Py::Object 
> _png_module::write_png(const Py::Tuple&)':
> src/_png.cpp:137: error: 'npy_PyFile_OpenFile' was not declared in 
> this scope
> src/_png.cpp:147: error: 'npy_PyFile_Dup' was not declared in this scope
> src/_png.cpp:243: error: 'npy_PyFile_DupClose' was not declared in 
> this scope
> src/_png.cpp:264: error: 'npy_PyFile_DupClose' was not declared in 
> this scope
> src/_png.cpp: In member function 'PyObject* 
> _png_module::_read_png(const Py::Object&, bool, int)':
> src/_png.cpp:321: error: 'npy_PyFile_OpenFile' was not declared in 
> this scope
> src/_png.cpp:329: error: 'npy_PyFile_Dup' was not declared in this scope
> src/_png.cpp:577: error: 'npy_PyFile_DupClose' was not declared in 
> this scope
> error: command 'gcc' failed with exit status 1
>
> Thanks,
> N
>
>
> ------------------------------------------------------------------------------
> 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_feb
>
>
> _______________________________________________
> Matplotlib-devel mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-devel

Hello,
Since I've updated my master branch, I have a new error when installing
matplotlib. It seems it doesn't find a header file from numpy:
I've reinstalled numpy (development version), and I tried to reinstall
everything (on python 2.6) but I still get the error. Am I the only one
having installation problems ?
Here is the (partial) traceback:
building 'matplotlib._png' extension
gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O2 -Wall -fPIC
-DPY_ARRAY_UNIQUE_SYMBOL=MPL_ARRAY_API -DPYCXX_ISO_CPP_LIB=1
-I/usr/local/include -I/usr/include -I. -I/usr/include/libpng12
-I/usr/lib/pymodules/python2.6/numpy/core/include -I/usr/include/python2.6
-c src/_png.cpp -o build/temp.linux-x86_64-2.6/src/_png.o
In file included from src/_png.cpp:31:
src/file_compat.h:4:32: error: numpy/npy_3kcompat.h: No such file or
directory
In file included from src/_png.cpp:31:
src/file_compat.h: In function ‘int npy_PyFile_CloseFile(PyObject*)’:
src/file_compat.h:125: warning: deprecated conversion from string constant
to ‘char*’
src/_png.cpp: In member function ‘Py::Object _png_module::write_png(const
Py::Tuple&)’:
src/_png.cpp:137: error: ‘npy_PyFile_OpenFile’ was not declared in this
scope
src/_png.cpp:147: error: ‘npy_PyFile_Dup’ was not declared in this scope
src/_png.cpp:243: error: ‘npy_PyFile_DupClose’ was not declared in this
scope
src/_png.cpp:264: error: ‘npy_PyFile_DupClose’ was not declared in this
scope
src/_png.cpp: In member function ‘PyObject* _png_module::_read_png(const
Py::Object&, bool, int)’:
src/_png.cpp:321: error: ‘npy_PyFile_OpenFile’ was not declared in this
scope
src/_png.cpp:329: error: ‘npy_PyFile_Dup’ was not declared in this scope
src/_png.cpp:577: error: ‘npy_PyFile_DupClose’ was not declared in this
scope
error: command 'gcc' failed with exit status 1
Thanks,
N