matplotlib-users Mailing List for matplotlib (Page 5)

Can you provide us with more information? You can create one plot, save it,
and then create the second, or is there something more specific you are
looking for?
On Mon, Aug 12, 2013 at 11:58 PM, vwf <vw...@vu...> wrote:
> Hello,
>
> I need to create two plots (png files) in one go, two unrelated views of
> the same dataset. There is good documentation about subplots but I
> cannot locate documentation about two plots. Can someone tell me how it
> is done?
>
>
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-- 
Joseph Hardin, MSEE
Colorado State University
Radar and Communications Laboratory
One must do not violence to nature, nor model it in conformity to any
blindly formed chimaera.
-Janos Boylai

Hello,
I need to create two plots (png files) in one go, two unrelated views of
the same dataset. There is good documentation about subplots but I
cannot locate documentation about two plots. Can someone tell me how it
is done?

On Mon, Aug 12, 2013 at 7:01 AM, Michael Droettboom <md...@st...> wrote:
> I propose to fix this by turning on interactive only when
> running at an interactive console.
I embed MPL more than other uses, and this sounds like a fine solution to me/
Thanks,
-Chris
-- 
Christopher Barker, Ph.D.
Oceanographer
Emergency Response Division
NOAA/NOS/OR&R (206) 526-6959 voice
7600 Sand Point Way NE (206) 526-6329 fax
Seattle, WA 98115 (206) 526-6317 main reception
Chr...@no...

I'm considering changing the behavior of the rcParam |interactive| 
(which also can be set through |matplotlib.interactive()| and 
|pyplot.ion()| and |pyplot.ioff()|). Currently, when setting 
|interactive| to |True|, running any sort of matplotlib plot as a script 
will fail to display a window. This can be very surprising if a user 
turns on |interactive| because they prefer its behavior in IPython, but 
are then surprised that none of their scripts continue to work. I 
propose to fix this by turning on |interactive| only when running at an 
interactive console.
See the pull request http://github.com/matplotlib/matplotlib/pull/2286 
for an implementation.
I'm trying to rule out any negative impact of this change, and I would 
appreciate any feedback if this change will have a negative impact on 
your application.
Mike

oh, I got it...needed to include backend package in the cf_freeze setup
script.
thanks,
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Hhhhmmm...what exactly do you have in mind? Sure, many of the steps to plot
something are the same; to be sure, the data changes and possibly the type
of plot...
what kind of application are you writing?
how much freedom do you really want to give to the user?
is the plot specific enough? given the context of the application?
is the user expected to know python and matplotlib?
have you tried eval?
have you thought about simply providing a python console along with your
application?
...just throwing questions at you to make you think more about this.
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So, I have a working program that starts like this:
import os
import sys
import numpy as np
from pylab import *
from matplotlib.widgets import Slider
from matplotlib.patches import FancyBboxPatch, Rectangle
but when I cx_freeze it, it complains about some missing backend_ stuff, so,
I do:
import os
import sys
import numpy as np
*import matplotlib
matplotlib.use("TkAgg")
*from pylab import *
from matplotlib.widgets import Slider
from matplotlib.patches import FancyBboxPatch, Rectangle
but the problem continues...
...anybody know how to solve this problem?
I would appreciate very much.
Oh, here is the setup file generated with cx_freeze
from cx_Freeze import setup, Executable
# Dependencies are automatically detected, but it might need
# fine tuning.
buildOptions = dict(packages = [], excludes = [])
import sys
base = 'Win32GUI' if sys.platform=='win32' else None
executables = [
 Executable('c.py', base=base)
]
setup(name='Cross-slot-flux',
 version = '1.0',
 description = 'Visual',
 options = dict(build_exe = buildOptions),
 executables = executables)
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On Fri, Aug 9, 2013 at 11:15 AM, Martin Mokrejs <mmo...@fo...
> wrote:
> Hi Ben,
> thank your for your comments. OK, here are revised patches. I see a hot
> spot
> in artist.py where the getattr() calls are too expensive. Actually, those
> under
> the callable() path.
>
>
Ah, yes... one of the biggest warts in our codebase. Does get_aliases()
really get called that often? If so, I doubt changes with regards to
startwith() is the best use of our time. I would imagine that a refactor
that caches possible aliases. Hell, I would just as soon like to see the
entire aliases framework ripped out and redone more correctly in the first
place.
As for the other startswith() changes, there are some subtle differences in
the change that has to be considered. First, is there a guarantee that the
string being indexed is not empty? startswith() would handle that
correctly, while indexing would throw an exception (and setting up code to
try...catch those exceptions would reduce readability and probably reduce
performance back to where we started).
I think that the *biggest* improvement we are going to get is from your
patch to figure.py because it touches on some very deep code that is
executed very frequently, and we were doing in probably the most
inefficient manner.
Again, I really stress the importance of setting up a github account and
submitting a PR. Once you do that, you are all set to go for contributing
to many other great projects (numpy, scipy, ipython, etc.).
Cheers!
Ben Root

Hi Ben,
 thank your for your comments. OK, here are revised patches. I see a hot spot
in artist.py where the getattr() calls are too expensive. Actually, those under
the callable() path.
 793 class ArtistInspector:
 817 def get_aliases(self):
 818 """
 819 Get a dict mapping *fullname* -> *alias* for each *alias* in
 820 the :class:`~matplotlib.artist.ArtistInspector`.
 821 
 822 Eg., for lines::
 823 
 824 {'markerfacecolor': 'mfc',
 825 'linewidth' : 'lw',
 826 }
 827 
 828 """
 829 names = [name for name in dir(self.o) if
 830 (name[:4] in ['set_', 'get_'])
 831 and callable(getattr(self.o, name))]
 832 aliases = {}
 833 for name in names:
 834 func = getattr(self.o, name)
 835 if not self.is_alias(func):
 836 continue
 837 docstring = func.__doc__
 838 fullname = docstring[10:]
 839 aliases.setdefault(fullname[4:], {})[name[4:]] = None
 840 return aliases
Another hot spot is setp() artist.py, actually its get_aliases() on line 817,
which again leads to getattr() and callable(). The problem is there are millions of
their calls.
So, once again, my all my patches attached (figure.py.patch artist.py.patch have changed).
Martin
Benjamin Root wrote:
> 
> On Fri, Aug 9, 2013 at 9:04 AM, Martin Mokrejs <mmo...@fo... <mailto:mmo...@fo...>> wrote:
> 
> Hi Phil,
> 
> Phil Elson wrote:
> > Hi Martin,
> >
> > Thanks for this - we are really interested in speeding up the scatter and barchart plotting with large data sets. In fact, we've done some work (https://github.com/matplotlib/matplotlib/pull/2156) recently to make the situation better.
> >
> > I'd really like to review these changes (against matplotlib master), and the best possible solution to doing this is if you were to submit a pull request. If the changes you have made are logically seperable, then I'd encourage you to make a few PRs, but otherwise, a single PR with all of these changes would be great.
> 
> I went through the changes there and they just cope with other pieces of matplotlib.
> My changes are general python improvements moving away from str.startswith()
> and using generators instead of for loops. Just apply the patches yourself and see.
> ;)
> 
> >
> > Would you mind turning these patches into PR(s)? (https://github.com/matplotlib/matplotlib/compare/)
> 
> Um, I don't know what to do on that page, sorry. I don't see how to upload my patch file or patched file
> to be compared with master. :(
> 
> >
> > Thanks!
> 
> I am sorry but I just don't have time to fiddle with github. It is just awkward. I even failed to download
> diffs of the changes from https://github.com/matplotlib/matplotlib/pull/2156/commits.
> 
> I rather continue studying runsnake output. ;-)
> 
> Martin
> 
> 
> A snippet from one of you patches:
> 
> dsu = []
> 
> - for a in self.patches:
> - dsu.append( (a.get_zorder(), a, a.draw, [renderer]))
> + [ dsu.append( (x.get_zorder(), x, x.draw, [renderer])) for x in self.patches ]
> 
> Yes, we certainly should use list-comprehensions here, but you are using it incorrectly. It should be:
> 
> dsu = [(x.get_zorder(), x, x.draw, [renderer])) for x in self.patches ]
> 
> And then, further down, do the following:
> 
> dsu.extend((x.get_zorder(), x, x.draw, [renderer])) for x in self.lines)
> 
> Note the generator form of the comprehension as opposed to the list comprehension form. List comprehensions should *always* be assigned to something. List comprehensions should only be for replacing the for-append idiom in python.
> 
> Thank you though for pointing out parts of the code that can benefit from revisions. I certainly hope you can get this put together as a pull request on github so we can work to make this patch better!
> 
> Ben Root
> 
> P.S. - I <3 runsnakerun!

On Fri, Aug 9, 2013 at 9:04 AM, Martin Mokrejs
<mmo...@fo...>wrote:
> Hi Phil,
>
> Phil Elson wrote:
> > Hi Martin,
> >
> > Thanks for this - we are really interested in speeding up the scatter
> and barchart plotting with large data sets. In fact, we've done some work (
> https://github.com/matplotlib/matplotlib/pull/2156) recently to make the
> situation better.
> >
> > I'd really like to review these changes (against matplotlib master), and
> the best possible solution to doing this is if you were to submit a pull
> request. If the changes you have made are logically seperable, then I'd
> encourage you to make a few PRs, but otherwise, a single PR with all of
> these changes would be great.
>
> I went through the changes there and they just cope with other pieces of
> matplotlib.
> My changes are general python improvements moving away from
> str.startswith()
> and using generators instead of for loops. Just apply the patches yourself
> and see.
> ;)
>
> >
> > Would you mind turning these patches into PR(s)? (
> https://github.com/matplotlib/matplotlib/compare/)
>
> Um, I don't know what to do on that page, sorry. I don't see how to upload
> my patch file or patched file
> to be compared with master. :(
>
> >
> > Thanks!
>
> I am sorry but I just don't have time to fiddle with github. It is just
> awkward. I even failed to download
> diffs of the changes from
> https://github.com/matplotlib/matplotlib/pull/2156/commits.
>
> I rather continue studying runsnake output. ;-)
>
> Martin
>
>
A snippet from one of you patches:
 dsu = []
- for a in self.patches:
- dsu.append( (a.get_zorder(), a, a.draw, [renderer]))
+ [ dsu.append( (x.get_zorder(), x, x.draw, [renderer])) for x in
self.patches ]
Yes, we certainly should use list-comprehensions here, but you are using it
incorrectly. It should be:
dsu = [(x.get_zorder(), x, x.draw, [renderer])) for x in self.patches ]
And then, further down, do the following:
dsu.extend((x.get_zorder(), x, x.draw, [renderer])) for x in self.lines)
Note the generator form of the comprehension as opposed to the list
comprehension form. List comprehensions should *always* be assigned to
something. List comprehensions should only be for replacing the for-append
idiom in python.
Thank you though for pointing out parts of the code that can benefit from
revisions. I certainly hope you can get this put together as a pull request
on github so we can work to make this patch better!
Ben Root
P.S. - I <3 runsnakerun!

On Fri, Aug 9, 2013 at 6:57 AM, SquirrelSeq
<ral...@un...>wrote:
> Hello everybody,
>
> I created 3D-Plots with 3D bars in matplotlib. The bars are colored
> according to a colormap.
>
> Unfortunately, only vertical faces have the desired bright colors, whereas
> the top sides of the bars are shaded darker to make it look more 3D.
>
> This makes the colors a lot more difficult to see, depending on the
> perspective.
>
> What can I do in order to switch of shading or to add an ambivalent light
> source?
>
> Best regards
> SquirrelSeq
>
> <http://matplotlib.1069221.n5.nabble.com/file/n41766/overlap_heatmap.png>
>
>
The one way to do it is to patch the source code in the following way.
in mpl_toolkits/mplot3d/axes3d.py, at around line 2355, replace the line:
sfacecolors = self._shade_colors(facecolors, normals)
with
sfacecolors = facecolors
Could you file a github issue requesting a keyword argument to turn this
on/off?
Cheers!
Ben Root

Hi Phil,
Phil Elson wrote:
> Hi Martin,
> 
> Thanks for this - we are really interested in speeding up the scatter and barchart plotting with large data sets. In fact, we've done some work (https://github.com/matplotlib/matplotlib/pull/2156) recently to make the situation better.
> 
> I'd really like to review these changes (against matplotlib master), and the best possible solution to doing this is if you were to submit a pull request. If the changes you have made are logically seperable, then I'd encourage you to make a few PRs, but otherwise, a single PR with all of these changes would be great.
I went through the changes there and they just cope with other pieces of matplotlib.
My changes are general python improvements moving away from str.startswith()
and using generators instead of for loops. Just apply the patches yourself and see.
;)
> 
> Would you mind turning these patches into PR(s)? (https://github.com/matplotlib/matplotlib/compare/)
Um, I don't know what to do on that page, sorry. I don't see how to upload my patch file or patched file
to be compared with master. :(
> 
> Thanks!
I am sorry but I just don't have time to fiddle with github. It is just awkward. I even failed to download
diffs of the changes from https://github.com/matplotlib/matplotlib/pull/2156/commits.
I rather continue studying runsnake output. ;-)
Martin
> 
> Phil
> 
> 
> On 9 August 2013 12:53, Martin Mokrejs <mmo...@fo... <mailto:mmo...@fo...>> wrote:
> 
> Hi,
> I am drawing some barcharts and scatter plot and the speed for rendering is awful once you have
> 100 000 of dots. I ran python profiler which lead me to .startswith() calls and some for loops
> which append do a list repeatedly. This parts could be still sped up I think but a first attempt
> is here:
> 
> 
> UNPATCHED 1.2.1
> 
> real 23m17.764s
> user 13m25.880s
> sys 3m37.180s
> 
> 
> PATCHED:
> 
> real 6m59.831s
> user 5m18.000s
> sys 1m40.360s
> 
> 
> 
> The patches are simple and because I see elsewhere in the code list expansions I do not see any
> problems with backwards compatibility (new new python language features are required).
> 
> Hope this helps,
> Martin
> 
> ------------------------------------------------------------------------------
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> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
> 
> 

Hi Martin,
Thanks for this - we are really interested in speeding up the scatter and
barchart plotting with large data sets. In fact, we've done some work (
https://github.com/matplotlib/matplotlib/pull/2156) recently to make the
situation better.
I'd really like to review these changes (against matplotlib master), and
the best possible solution to doing this is if you were to submit a pull
request. If the changes you have made are logically seperable, then I'd
encourage you to make a few PRs, but otherwise, a single PR with all of
these changes would be great.
Would you mind turning these patches into PR(s)? (
https://github.com/matplotlib/matplotlib/compare/)
Thanks!
Phil
On 9 August 2013 12:53, Martin Mokrejs <mmo...@fo...> wrote:
> Hi,
> I am drawing some barcharts and scatter plot and the speed for rendering
> is awful once you have
> 100 000 of dots. I ran python profiler which lead me to .startswith()
> calls and some for loops
> which append do a list repeatedly. This parts could be still sped up I
> think but a first attempt
> is here:
>
>
> UNPATCHED 1.2.1
>
> real 23m17.764s
> user 13m25.880s
> sys 3m37.180s
>
>
> PATCHED:
>
> real 6m59.831s
> user 5m18.000s
> sys 1m40.360s
>
>
>
> The patches are simple and because I see elsewhere in the code list
> expansions I do not see any
> problems with backwards compatibility (new new python language features
> are required).
>
> Hope this helps,
> Martin
>
>
> ------------------------------------------------------------------------------
> Get 100% visibility into Java/.NET code with AppDynamics Lite!
> It's a free troubleshooting tool designed for production.
> Get down to code-level detail for bottlenecks, with <2% overhead.
> Download for free and get started troubleshooting in minutes.
> http://pubads.g.doubleclick.net/gampad/clk?id=48897031&iu=/4140/ostg.clktrk
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>
>

Hi,
 I am drawing some barcharts and scatter plot and the speed for rendering is awful once you have
100 000 of dots. I ran python profiler which lead me to .startswith() calls and some for loops
which append do a list repeatedly. This parts could be still sped up I think but a first attempt
is here:
UNPATCHED 1.2.1
real 23m17.764s
user 13m25.880s
sys 3m37.180s
PATCHED:
real 6m59.831s
user 5m18.000s
sys 1m40.360s
The patches are simple and because I see elsewhere in the code list expansions I do not see any
problems with backwards compatibility (new new python language features are required).
Hope this helps,
Martin

[re-sending with also the 3rd patch file, sorry]
Hi,
 I am drawing some barcharts and scatter plot and the speed for rendering is awful once you have
100 000 of dots. I ran python profiler which lead me to .startswith() calls and some for loops
which append do a list repeatedly. This parts could be still sped up I think but a first attempt
is here:
UNPATCHED 1.2.1
real 23m17.764s
user 13m25.880s
sys 3m37.180s
PATCHED:
real 6m59.831s
user 5m18.000s
sys 1m40.360s
The patches are simple and because I see elsewhere in the code list expansions I do not see any
problems with backwards compatibility (new new python language features are required).
Hope this helps,
Martin

Hello everybody,
I created 3D-Plots with 3D bars in matplotlib. The bars are colored
according to a colormap.
Unfortunately, only vertical faces have the desired bright colors, whereas
the top sides of the bars are shaded darker to make it look more 3D.
This makes the colors a lot more difficult to see, depending on the
perspective.
What can I do in order to switch of shading or to add an ambivalent light
source?
Best regards
SquirrelSeq
<http://matplotlib.1069221.n5.nabble.com/file/n41766/overlap_heatmap.png> 
--
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Hi all,
I am incredibly thrilled, on behalf of the amazing IPython Dev Team,
to announce the official release of IPython 1.0 today, an effort
nearly 12 years in the making. The previous version (0.13) was
released on June 30, 2012, and in this development cycle we had:
~12 months of work.
~700 pull requests merged.
~600 issues closed (non-pull requests).
contributions from ~150 authors.
~4000 commits.
# A little context
What does "1.0" mean for IPython? Obviously IPython has been a staple
of the scientific Python community for years, and we've made every
effort to make it a robust and production ready tool for a long time,
so what exactly do we mean by tagging this particular release as 1.0?
Basically, we feel that the core design of IPython, and the scope of
the project, is where we want it to be.
What we have today is what we consider a reasonably complete, design-
and scope-wise, IPython 1.0: an architecture for interactive
computing, that can drive kernels in a number of ways using a
well-defined protocol, and rich and powerful clients that let users
control those kernels effectively. Our different clients serve
different needs, with the old workhorse of the terminal still being
very useful, but much of our current development energy going into the
Notebook, obviously. The Notebook enables interactive exploration to
become Literate Computing, bridging the gaps from individual work to
collaboration and publication, all with an open file format that is a
direct record of the underlying communication protocol.
There are obviously plenty of open issues (many of them very
important) that need fixing, and large and ambitious new lines of
development for the years to come. But the work of the last four
years, since the summer of 2009 when Brian Granger was able to devote
a summer (thanks to funding from the NiPy project - nipy.org) to
refactoring the old IPython core code, finally opened up or
infrastructure for real innovation. By disentangling what was a useful
but impenetrable codebase, it became possible for us to start building
a flexible, modern system for interactive computing that abstracted
the old REPL model into a generic protocol that kernels could use to
talk to clients. This led at first to the creation of the Qt console,
and then to the Notebook and out-of-process terminal client. It also
allowed us to (finally!) unify our parallel computing machinery with
the rest of the interactive system, which Min Ragan-Kelley pulled off
in a development tour de force that involved rewriting in a few weeks
a huge and complex Twisted-based system.
We are very happy with how the Notebook work has turned out, and it
seems the entire community agrees with us, as the uptake has been
phenomenal. Back from the very first "IPython 0.0.1" that I started
in 2001:
https://gist.github.com/fperez/1579699
there were already hints of tools like Mathematica: it was my everyday
workhorse as a theoretical physicist and I found its Notebook
environment invaluable. But as a grad student trying out "just an
afternoon hack" (IPython was my very first Python program as I was
learning the language), I didn't have the resources, skills or vision
to attempt building an entire notebook system, and to be honest the
tools of the day would have made that enterprise a miserable one. But
those ideas were always driving our efforts, and as IPython started
becoming a project with a team, we made multiple attempts to get a
good Notebook built around IPython. Those interested can read an old
blog post of mine with the history
(http://blog.fperez.org/2012/01/ipython-notebook-historical.html).
The short story is that in 2011, on our sixth attempt, Brian was again
able to devote a focused summer into using our client-server
architecture and, with the stack of the modern web (Javascript, CSS,
websockets, Tornado, ...), finally build a robust system for Literate
Computing across programming languages.
Today, thanks to the generous support and vision of Josh Greenberg at
the Alfred P. Sloan Foundation, we are working very hard on building
the notebook infrastructure, and this release contains major advances
on that front. We have high hopes for what we'll do next; as a
glimpse of the future that this enables, now there is a native Julia
kernel that speaks to our clients, notebook included:
https://github.com/JuliaLang/IJulia.jl.
# Team
I can't stress enough how impressed I am with the work people are
doing in IPython, and what a privilege it is to work with colleagues
like these. Brian Granger and Min Ragan-Kelley joined IPython around
2005, initially working on the parallel machinery, but since ~ 2009
they have become the heart of the project. Today Min is our top
committer and knows our codebase better than anyone else, and I can't
imagine better partners for an effort like this.
And from regulars in our core team like Thomas Kluyver, Matthias
Bussonnier, Brad Froehle and Paul Ivanov to newcomers like Jonathan
Frederic and Zach Sailer, in addition to the many more whose names are
in our logs, we have a crazy amount of energy being poured into
IPython. I hope we'll continue to harness it productively!
The full list of contributors to this release can be seen here:
http://ipython.org/ipython-doc/rel-1.0.0/whatsnew/github-stats-1.0.html
# Release highlights
* nbconvert: this is the major piece of new functionality in this
cycle, and was an explicit part of our roadmap
(https://github.com/ipython/ipython/wiki/Roadmap:-IPython). nbconvert
is now an IPython subcommand to convert notebooks into other formats
such as HTML or LaTeX, but more importantly, it's a very flexible
system that lets you write custom templates to generate new output
with arbitrary control over the formatting and transformations that
are applied to the input.
We want to stress that despite the fact that a huge amount of work
went into nbconvert, this should be considered a *tech preview*
release. We've come to realize how complex this problem is, and while
we'll make every effort to keep the high-level command-line syntax and
APIs as stable as possible, it is quite likely that the internals will
continue to evolve, possibly in backwards-incompatible ways. So if
you start building services and libraries that make heavy use of the
nbconvert internals, please be prepared for some turmoil in the months
to come, and ping us on the dev list with questions or concerns.
* Notebook improvements: there has been a ton of polish work in the
notebook at many levels, though the file format remains unchanged from
0.13, so you shouldn't have any problems sharing notebooks with
colleagues still using 0.13.
 - Autosave: probably the most oft-requested feature, the notebook
server now autosaves your files! You can still hit Ctrl-S to force a
manual save (which also creates a special 'checkpoint' you can come
back to).
 - The notebook supports raw_input(), and thus also %debug. This was
probably the main deficiency of the notebook as a client compared to
the terminal/qtconsole, and it has been finally fixed.
 - Add %%html, %%svg, %%javascript, and %%latex cell magics for
writing raw output in notebook cells.
 - Fix an issue parsing LaTeX in markdown cells, which required users
to type \\,円 instead of \\.
 -Images support width and height metadata, and thereby 2x scaling
(retina support).
 - %%file has been renamed %%writefile (%%file) is deprecated.
* The input transofrmation code has been updated and rationalized.
This is a somewhat specialized part of IPython, but of importance to
projects that build upon it for custom environments, like Sympy and
Sage.
Our full release notes are here:
http://ipython.org/ipython-doc/rel-1.0.0/whatsnew/version1.0.html
and the gory details are here:
http://ipython.org/ipython-doc/rel-1.0.0/whatsnew/github-stats-1.0.html
# Installation
Installation links and instructions are at: http://ipython.org/install.html
And IPython is also on PyPI: http://pypi.python.org/pypi/ipython
# Requirements
IPython 1.0 requires Python ≥ 2.6.5 or ≥ 3.2.1. It does not support
Python 3.0, 3.1, or 2.5.
# Acknowledgments
Last but not least, we'd like to acknowledge the generous support of
those who make it possible for us to spend our time working on
IPython. In particular, the Alfred P. Sloan Foundation today lets us
have a solid team working full-time on the project, and without the
support of Enthought Inc at multiple points in our history, we
wouldn't be where we are today.
The full list of our support is here:
http://ipython.org/index.html#support
Thanks to everyone! Please enjoy IPython 1.0, and report all bugs as usual!
Fernando, on behalf of the IPython Dev Team.
-- 
Fernando Perez (@fperez_org; http://fperez.org)
fperez.net-at-gmail: mailing lists only (I ignore this when swamped!)
fernando.perez-at-berkeley: contact me here for any direct mail

On 2013年08月08日 12:33 AM, bruno.pace wrote:
> Hey all!
>
> I have been using interactive mode inside classes for a long time, but now
> I'm just trying to use it in a Shell (idle or even in terminal) and it's not
> working.
>
> I try even simple stuff like:
>
> import matplotlib.pyplot as plt
>
> plt.ion()
> plt.plot([1.6,2.7])
> plt.draw()
>
>
> I have found in a topic that using plt.pause(0.001) would work, and indeed
> it shows the graph. But after
> a few seconds the window gets unresponsive. Note that when I turn off the
> interactive mode everything works normally as well...
>
> In my application, I am plotting a graph (with networkX) and I have to
> refresh it once in a while. When I use the interactive mode inside of a
> class, everything works fine. But now I'm using a module with no class
> inside (or even in the shell) and it refuses to plot!
>
> Any ideas?
The problem here is one of expectations. See 
http://matplotlib.org/users/shell.html
Eric
>
> Thanks a lot!
>
> Bruno
>
>
>
> --
> View this message in context: http://matplotlib.1069221.n5.nabble.com/ion-is-not-working-tp41755.html
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>
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Hey all!
I have been using interactive mode inside classes for a long time, but now
I'm just trying to use it in a Shell (idle or even in terminal) and it's not
working.
I try even simple stuff like:
import matplotlib.pyplot as plt
plt.ion()
plt.plot([1.6,2.7])
plt.draw()
I have found in a topic that using plt.pause(0.001) would work, and indeed
it shows the graph. But after
 a few seconds the window gets unresponsive. Note that when I turn off the
interactive mode everything works normally as well... 
In my application, I am plotting a graph (with networkX) and I have to
refresh it once in a while. When I use the interactive mode inside of a
class, everything works fine. But now I'm using a module with no class
inside (or even in the shell) and it refuses to plot!
Any ideas?
Thanks a lot!
Bruno
--
View this message in context: http://matplotlib.1069221.n5.nabble.com/ion-is-not-working-tp41755.html
Sent from the matplotlib - users mailing list archive at Nabble.com.

I think by download page, Keith meant project page.
The direct link seems to be http://sourceforge.net/p/matplotlib/mailman/ . Is
that permanent enough? Perhaps SF's structure has changed since the "lists" link
on the MPL website (http://sourceforge.net/mail/?group_id=80706) was last updated.
Martin
On 13-08-07 11:37 AM, Michael Droettboom wrote:
> Hmm... It takes me to the matplotlib project page on sourceforge, which I
> think is as close to a direct permalink as we can get. Not sure why it takes
> you somewhere else. Did you get redirected?
>
> Mike
>
> On 08/07/2013 11:47 AM, kei...@bt... wrote:
>> The link "join the matplotlib mailing lists" actually goes to the
>> sourceforge download page.
>>
>> Keith
>>
>>

I think the function should be np.all(dash_list <= 0.0) instead of np.any?
This works 100% fine on my machine so I think it just hangs if all values
are less than or equal to zero. That hangs on my machine as you stated.
>From digging into that I also realized I could have put (None, None) to get
a solid line in my dash list which is what I desired but I think (1,0) is
more intuitive and doesn't cause any errors. Since dash lists have to be
even number of values, could not just put (1) to get a solid line.
I guess do whichever you think is better.
Cheers,
Jeff
On Tue, Aug 6, 2013 at 4:14 AM, Michael Droettboom <md...@st...> wrote:
> The problem is that a 0-length dash or space is undefined. In Agg, it
> causes an infinite loop (presumably because the line cursor never moves).
> Saving it to a PDF file and opening it in Acrobat Reader reveals a blank
> page (presumably because it's doing something smarter, but also basically
> throwing up its hands). In SVG, you get a solid line, which may or may not
> be the right behavior.
>
> Given that a value of 0 doesn't make much sense anyway, I thought it best
> to just disallow it. Jeffrey: Do you have a good need for this?
>
> Here's the original PR:
>
> https://github.com/matplotlib/matplotlib/pull/1999
>
> Mike
>
>
> On 08/05/2013 01:36 PM, Benjamin Root wrote:
>
> @mdboom, from git blame, this looks to be specifically introduced by you
> via 7e7b5320<https://github.com/matplotlib/matplotlib/commit/7e7b532057c08541489203697987a924e56a7aeb>on May 15th, and you even added some tests for handling path clipping.
> Perhaps the choice of "<=" should have been just "<"?
>
>
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>
>
>
>
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>

On 08/07/2013 01:24 PM, Matthew Brett wrote:
> Hi,
>
> On Wed, Aug 7, 2013 at 4:50 AM, Michael Droettboom <md...@st...> wrote:
>> It should look in /usr/include and /usr/local/include by default. Is it
>> in either place?
> There are no freetype* files in either place, no. How would they get
> there (other than an explicit install)?
>
I think the usual advice here is to install the freetype development 
packages with MacPorts or homebrew -- but this is probably where I 
should step back at let one of the Mac OS-X folks speak up.
Mike

Hmm... It takes me to the matplotlib project page on sourceforge, which 
I think is as close to a direct permalink as we can get. Not sure why 
it takes you somewhere else. Did you get redirected?
Mike
On 08/07/2013 11:47 AM, kei...@bt... wrote:
> The link "join the matplotlib mailing lists" actually goes to the sourceforge download page.
>
> Keith
>
>
> ------------------------------------------------------------------------------
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You can set the rcParam "verbose.level" to "debug-annoying". Then, when 
it runs through all of your fonts, it should be clear which one caused 
the problem.
Note that I'm in the process of rewriting large parts of the font 
infrastructure as part of MEP14, so these sorts of things should 
hopefully be less common in the future.
Mike
On 08/07/2013 11:56 AM, vwf wrote:
> Hello,
>
> Matplotlib does not like one (or more) of my fonts. Since I own a
> considerable set it is very hard to find out which one violates the
> requirements. Is it possible to let matplotlib which font is the
> problem?
>
> Thanks
>
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Hello,
I found an issue where the figure editor (the checkbox icon in the toolbar) 
incorrectly captures the color properties from the existing curves in the plot:
https://github.com/matplotlib/matplotlib/issues/2274
So I put together a pull request that fixes it:
https://github.com/matplotlib/matplotlib/pull/2275
Could we get someone (or someones?) to give it a whirl, make sure everything 
works as expected and that nothing obvious has broken?
Thanks,
Martin