matplotlib-users Mailing List for matplotlib (Page 80)

I expected that floor(x) would return an integer especially since the docs
state:

Return the floor of the input, element-wise.


The floor of the scalar `x` is the largest integer `i`, such that

`i <= x`. It is often denoted as :math:`\lfloor x \rfloor`.


Any reason why it returns a float? Bug/feature?


Thanks, Mark

As for the rest of the two suggestions: [
https://github.com/pmarshwx/SHARPpy/tree/gui
https://github.com/PyAOS/aoslib]
there doesn't seem be routines included for CAPE and CIN calculations.

I am working on idealized orographic precipitation simulations in WRF that
will hopefully be presented in the Mountain Meteorology conference in
August. Perhaps I am extra cautious, but I need a good control especially
on CAPE calculation (independent of data-points, being able to specify
where to lift the parcel and specify mixed layer depth etc.).

This could be a good subject to discuss on the coming SciPy conference. The
abstract deadline is tomorrow. I might join if I can get some funding to
attend the conference. Is there any symposium planned for atmospheric
science people? Thanks again.


On Sat, Mar 29, 2014 at 6:32 PM, Gökhan Sever <gok...@gm...> wrote:

> Hello,
>
> Lately, I am working on plotting sounding profiles on a SkewT/LogP
> diagram. The SkewT package which is located at
> https://github.com/tchubb/SkewT has a nice feature to lift a parcel on
> dry/moist adiabats. This is very useful to demonstrate the regions of CIN
> and CAPE overlaid with the full sounding.
>
> However, the package misses these diagnostic calculations. This is the
> only step holding me back to use Python only (migrating from NCL) for my
> plotting tasks.  I am aware that these calculations are usually performed
> in fortran. Are there any routines wrapped in Python to calculate CAPE and
> CIN parameters?  Any suggestions or comments would be really appreciated.
>
> --
> Gökhan
>



-- 
Gökhan

Thanks Daniel. Your CAPE calculation approach looks robust. I will give it
a try with my sounding data. In the mean time, do you plan on putting a
function for CIN calculation?


On Sun, Mar 30, 2014 at 10:37 AM, Daniel Rothenberg <dar...@mi...>wrote:

> Hello Gokhan,
>
> I've written my own SkewT/LogP code with some lifted parcel calculations -
> it's included in a GitHub repo I use to maintain a collection of utilities
> I've written for analyzing output from a cloud resolving model I use. You
> can find my code for this purpose at
> https://github.com/darothen/crm-tools/blob/master/vis/soundings.py, along
> with other useful scripts.
>
> - Daniel Rothenberg
>
>
> On Sat, Mar 29, 2014 at 6:32 PM, Gökhan Sever <gok...@gm...>wrote:
>
>> Hello,
>>
>> Lately, I am working on plotting sounding profiles on a SkewT/LogP
>> diagram. The SkewT package which is located at
>> https://github.com/tchubb/SkewT has a nice feature to lift a parcel on
>> dry/moist adiabats. This is very useful to demonstrate the regions of CIN
>> and CAPE overlaid with the full sounding.
>>
>> However, the package misses these diagnostic calculations. This is the
>> only step holding me back to use Python only (migrating from NCL) for my
>> plotting tasks.  I am aware that these calculations are usually performed
>> in fortran. Are there any routines wrapped in Python to calculate CAPE and
>> CIN parameters?  Any suggestions or comments would be really appreciated.
>>
>> --
>> Gökhan
>>
>> _______________________________________________
>> Pyaos mailing list
>> Py...@li...
>> http://lists.johnny-lin.com/listinfo.cgi/pyaos-johnny-lin.com
>>
>>
>


-- 
Gökhan

Thanks Alex. This seems to be the easiest way to approach the problem.
However, for the sake of reproducibility, I am looking for a way to
interface to one of the common fortran routines for the task. Your
suggested approach will require some sort of interpolation at the very
least to make the estimation number of data point insensitive. Putting this
in my TODO list.


On Sat, Mar 29, 2014 at 7:56 PM, Alex Goodman <ale...@co...>wrote:

> You can easily visualize the CAPE and CIN with matplotlib using
> fill_between() on the environmental and parcel temperature curves. As for
> actually calculating it though, I don't know of a way to do it directly
> from matplotlib. There are probably several other python packages out there
> that can, but I am not familiar with them. In any case, why not just write
> your own function for calculating the CAPE and CIN? It is a bit surprising
> that this functionality isn't be included in the SkewT package, but since
> you can use it to get the parcel temperature curve, you should be able to
> calculate the CAPE and CIN rather easily by simply discretizing their
> respective formulas. Here's a rough example:
>
> import numpy as np
> cape = 9.8 * np.sum(dz * (Tp - T) / T)
>
> Where Tp and T are the parcel and environmental temperature arrays
> respectively, and dz are the height differences between layers. You would
> of course need to perform the sum from the LFC to EL for CAPE, so the
> arrays would have to to be subsetted. With numpy the easiest way to do this
> is with fancy indexing, eg:
>
> levs = (z >= LFC) & (z <= EL)
> Tp = Tp[levs]
> T = T[levs]
> where z is your array of heights (or pressure levels).
>
> Does this help?
>
> Alex
>
>
> On Sat, Mar 29, 2014 at 4:32 PM, Gökhan Sever <gok...@gm...>wrote:
>
>> Hello,
>>
>> Lately, I am working on plotting sounding profiles on a SkewT/LogP
>> diagram. The SkewT package which is located at
>> https://github.com/tchubb/SkewT has a nice feature to lift a parcel on
>> dry/moist adiabats. This is very useful to demonstrate the regions of CIN
>> and CAPE overlaid with the full sounding.
>>
>> However, the package misses these diagnostic calculations. This is the
>> only step holding me back to use Python only (migrating from NCL) for my
>> plotting tasks.  I am aware that these calculations are usually performed
>> in fortran. Are there any routines wrapped in Python to calculate CAPE and
>> CIN parameters?  Any suggestions or comments would be really appreciated.
>>
>> --
>> Gökhan
>>
>>
>> ------------------------------------------------------------------------------
>>
>> _______________________________________________
>> Matplotlib-users mailing list
>> Mat...@li...
>> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>>
>>
>
>
> --
> Alex Goodman
> Graduate Research Assistant
> Department of Atmospheric Science
> Colorado State University
>



-- 
Gökhan

Hi James,

I have managed to run CLIMT's thermodyn.py . Most of the functions I tested
from within the Driver.f90 works fine except the CAPE and CIN routines. I
sent an e-mail to the author regarding this but nothing back from him so
far. Would you give a test if I send you a simple sounding data? My system
is Window 7 (x64) and using Python(XY). f2py uses the gfortran provided in
MinGW32 folder.

Could you provide an example (with some test data) for Kerry Emanuel's
code? That code has definitely more functions than I need but it might be a
valuable source.

As for the NCL, it is easy to interface to a WRF output, it also includes
 a SkewT/LogP [https://www.ncl.ucar.edu/Applications/skewt.shtml], but the
CAPE estimation in this script is very sensitive to the number of
data-points, which I have bitten a couple of times. Dennis Shea has
provided some CAPE calculation routines coded in fortran. [Check under
http://www.cgd.ucar.edu/~shea/ for the files starting with cape*]. Yet I
have no luck wrapping them via f2py.


On Sat, Mar 29, 2014 at 7:11 PM, James Boyle <jsb...@gm...> wrote:

> I have used the CAPE and CIN from ClMT - a bit of overkill but many useful
> functions:
> http://people.su.se/~rcaba/climt/
>
> I have also wrapped using f2py the the Fortran CAPE and CIN of Kerry
> Emanuel ( a prestigious source) in his convect code:
> http://eaps4.mit.edu/faculty/Emanuel/products
>
> If you prowl about in the NCL source distribution, you will find the
> fortran that the NCL skew - T uses.
> If you ask, Dennis Shea of NCAR might break the code out for you. It is
> trivial to wrap using f2py ( f77).
>
>
> On Mar 29, 2014, at 3:32 PM, Gökhan Sever <gok...@gm...> wrote:
>
> Hello,
>
> Lately, I am working on plotting sounding profiles on a SkewT/LogP
> diagram. The SkewT package which is located at
> https://github.com/tchubb/SkewT has a nice feature to lift a parcel on
> dry/moist adiabats. This is very useful to demonstrate the regions of CIN
> and CAPE overlaid with the full sounding.
>
> However, the package misses these diagnostic calculations. This is the
> only step holding me back to use Python only (migrating from NCL) for my
> plotting tasks.  I am aware that these calculations are usually performed
> in fortran. Are there any routines wrapped in Python to calculate CAPE and
> CIN parameters?  Any suggestions or comments would be really appreciated.
>
> --
> Gökhan
>
> ------------------------------------------------------------------------------
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>
>
>


-- 
Gökhan

On Sun, Mar 30, 2014 at 4:36 PM, Thomas Chubb <tho...@mo...>wrote:

> Gokhan [and others],
>
> Thanks for showing an interest in SkewT. This has been a side project for
> me for a little while now, and only publicly available on PyPI in the last
> 12 months or so. I haven't been maintaining the github repository, so
> please get the latest version from here:
>
> https://pypi.python.org/pypi/SkewT
>
> I'll take the github repository down in the very near future unless I hear
> howls of protest.
>
> Regarding CAPE and CIN, these have been on my to-do list for a while. I
> agree that it would be a nice feature to include on the SkewT plots, but I
> don't really know the best way to proceed, but it would be nice to get some
> thoughts from the community.
>
>
Thomas (and others),

As the author of the original matplotlib code that you ran with (and
extended greatly!) I wanted to note two things:

1) Fixes to matplotlib to add skew transforms and make them work better for
plots is in master and should go out in 1.4. A basic version
of the SkewT plot is in the tree under examples/api/skewt.py

2) I've been (slowly) working on fleshing out that script with more
features (and more class-based) on a branch here:
https://github.com/metpy/MetPy/blob/skewt/metpy/plots/skewt.py

Ryan

-- 
Ryan May
Graduate Research Assistant
School of Meteorology
University of Oklahoma

plt.clf()

solved all my problems. I simply did not notice this simple solution. 
Whenever I saw "memory leak" there was given the advice to upgrade older 
versions.

Thanks a lot,
Foehn

Am 2014-03-30 13:55, schrieb Remo Goetschi:
> Hi Föhn,
>
> By default, the pyplot interface (recall that it is a Matlab-like state
> machine) does not release a figure's memory. You need to do this by hand
> by calling the clf() method after every plot you make:
> ...
> plt.close()
> plt.clf()
>
> Stackoverflow contains a few threads about the subject.
>
> Best,
> Remo
>
>

Hi Föhn,

By default, the pyplot interface (recall that it is a Matlab-like state 
machine) does not release a figure's memory. You need to do this by hand 
by calling the clf() method after every plot you make:
...
plt.close()
plt.clf()

Stackoverflow contains a few threads about the subject.

Best,
Remo



On 30.03.2014 13:33, Foehn wrote:
> I am using the matplotlib, basemap and numpy versions delivered with
> Ubuntu 12.04. I know the versions are outdated and should be replaced by
> something new. But I do not want to install "alien" versions, because
> sometimes you can run into problems with dependencies and so on.
>
> My problem now is a more elaborated basemap application that dies of
> memory hunger in course of the time. I have  a workaround that splits
> the program into chunks small enough not to die. For several reasons his
> is not ideal.
>
> I condensed the memory leak problem for my machine down to a rudimentary
> program. My question to the community with up to date
> (LINUX-)maplotlibs: Does it still cause a memory problem or not. If
> "yes" I will stick to my old matplotlib version, if "no" I consider a
> change.
>
> The Program can be found here:
>
> https://www.wuala.com/Foehn/Sonstiges/testmplmemleak/?key=nEhr83BTW4tx
>
>
> Thanks for the help,
> Föhn
>
>
>
> ------------------------------------------------------------------------------
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>

I am using the matplotlib, basemap and numpy versions delivered with 
Ubuntu 12.04. I know the versions are outdated and should be replaced by 
something new. But I do not want to install "alien" versions, because 
sometimes you can run into problems with dependencies and so on.

My problem now is a more elaborated basemap application that dies of 
memory hunger in course of the time. I have  a workaround that splits 
the program into chunks small enough not to die. For several reasons his 
is not ideal.

I condensed the memory leak problem for my machine down to a rudimentary 
program. My question to the community with up to date 
(LINUX-)maplotlibs: Does it still cause a memory problem or not. If 
"yes" I will stick to my old matplotlib version, if "no" I consider a 
change.

The Program can be found here:

https://www.wuala.com/Foehn/Sonstiges/testmplmemleak/?key=nEhr83BTW4tx


Thanks for the help,
Föhn

You can easily visualize the CAPE and CIN with matplotlib using
fill_between() on the environmental and parcel temperature curves. As for
actually calculating it though, I don't know of a way to do it directly
from matplotlib. There are probably several other python packages out there
that can, but I am not familiar with them. In any case, why not just write
your own function for calculating the CAPE and CIN? It is a bit surprising
that this functionality isn't be included in the SkewT package, but since
you can use it to get the parcel temperature curve, you should be able to
calculate the CAPE and CIN rather easily by simply discretizing their
respective formulas. Here's a rough example:

import numpy as np
cape = 9.8 * np.sum(dz * (Tp - T) / T)

Where Tp and T are the parcel and environmental temperature arrays
respectively, and dz are the height differences between layers. You would
of course need to perform the sum from the LFC to EL for CAPE, so the
arrays would have to to be subsetted. With numpy the easiest way to do this
is with fancy indexing, eg:

levs = (z >= LFC) & (z <= EL)
Tp = Tp[levs]
T = T[levs]
where z is your array of heights (or pressure levels).

Does this help?

Alex


On Sat, Mar 29, 2014 at 4:32 PM, Gökhan Sever <gok...@gm...> wrote:

> Hello,
>
> Lately, I am working on plotting sounding profiles on a SkewT/LogP
> diagram. The SkewT package which is located at
> https://github.com/tchubb/SkewT has a nice feature to lift a parcel on
> dry/moist adiabats. This is very useful to demonstrate the regions of CIN
> and CAPE overlaid with the full sounding.
>
> However, the package misses these diagnostic calculations. This is the
> only step holding me back to use Python only (migrating from NCL) for my
> plotting tasks.  I am aware that these calculations are usually performed
> in fortran. Are there any routines wrapped in Python to calculate CAPE and
> CIN parameters?  Any suggestions or comments would be really appreciated.
>
> --
> Gökhan
>
>
> ------------------------------------------------------------------------------
>
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>
>


-- 
Alex Goodman
Graduate Research Assistant
Department of Atmospheric Science
Colorado State University

On 28.03.2014 19:13, Pierre Haessig wrote:
> Hi,
>
> I just ran across this new Qt "add-on" for data visualization :
> blog.qt.digia.com/blog/2014/03/26/qt-data-visualization-1-0-released/
>
> It's a bit off-topic because I think there is not (yet?) a Python
> binding,

 From the PyQt mailing list:

"""
PyQtDataVisualization v1.0 has been released. These are Python bindings
for Digia's Qt Data Visualization library and PyQt5, see...

http://qt.digia.com/Product/Qt-Enterprise-Features/Advanced-Data-Visualization/

PyQtDataVisualization is bundled with PyQt. It is not available under 
an open source license.

Phil
"""

Anyways, being not open source products, the interest is limited.

For Qt the natural choice would be Qwt but then the problem would be to 
get a wrapper able to work with PySide, PyQt4 and PyQt5.

Because of those (an other) issues I am currently using matplotlib in 
my PyQt applications.

Armando

Hello,

Lately, I am working on plotting sounding profiles on a SkewT/LogP diagram.
The SkewT package which is located at https://github.com/tchubb/SkewT has a
nice feature to lift a parcel on dry/moist adiabats. This is very useful to
demonstrate the regions of CIN and CAPE overlaid with the full sounding.

However, the package misses these diagnostic calculations. This is the only
step holding me back to use Python only (migrating from NCL) for my
plotting tasks.  I am aware that these calculations are usually performed
in fortran. Are there any routines wrapped in Python to calculate CAPE and
CIN parameters?  Any suggestions or comments would be really appreciated.

-- 
Gökhan

http://2sn.org/python3/color.py

On 30 March 2014 07:27, Emilia Petrisor <emi...@gm...> wrote:
> Hi all,
>
> While working  on this IPython Notebook
> http://nbviewer.ipython.org/github/empet/Math/blob/master/DomainColoring.ipynb
> I wanted to compare the visual images of the same complex-valued function
> generated by the classical domain coloring method, using HSV, respectively
> HSL color model.
>
> Unfortunately there is no matplotlib.colors.hsl_to_rgb(array)  function,
> only colorsys.hsl_to_rgb(h,s,l). The latter acts on each pixel and is time
> consuming.
>
> My question is, there is a special reason for which hsl_to_rgb is not
> implemented in matplotlib.colors for arrays?
>  I also looked in skimage.color
> http://scikit-image.org/docs/dev/api/skimage.color.html and couldn't find
> such a function.
>
> Is there a package containing such a conversion?
> Thank you!
>
> Em
>
>
>
>
>
> ------------------------------------------------------------------------------
>
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>

Hi all,

While working  on this IPython Notebook
 http://nbviewer.ipython.org/github/empet/Math/blob/master/DomainColoring.ipynb<about:invalid#zClosurez>
I wanted to compare the visual images of the same complex-valued function
generated by the classical domain coloring method, using HSV, respectively
HSL color model.

Unfortunately there is no matplotlib.colors.hsl_to_rgb(array)  function,
only colorsys.hsl_to_rgb(h,s,l). The latter acts on each pixel and is time
consuming.

My question is, there is a special reason for which hsl_to_rgb is not
implemented in matplotlib.colors for arrays?
 I also looked in skimage.color
http://scikit-image.org/docs/dev/api/skimage.color.html and couldn't find
such a function.

Is there a package containing such a conversion?
Thank you!

Em

sometimes, we need to plot array value on a grid, for example
a=np.array([[1,3,5], [2,4,6]])
is shown as
+------+------+------+
|   1   |   3   |   5   |
 +------+------+------+
|   2   |   4   |   6   |
 +------+------+------+

Is there any ready-to-use method?

thanks

Hi Ian

Thanks for your reply and help. I see your point. I guess it is only the
BoundaryNorm where it would make sense to have contourf use the boundary
levels from the norm. In my real problem described by the above example I
have long forgotten the levs variable when I arrive at the contourf point.
I will therefore instead just use levels=norm.boundaries.

Best regards,
Jesper


2014-03-28 15:17 GMT+01:00 Ian Thomas <ian...@gm...>:

> On 28 March 2014 12:56, Jesper Larsen <jes...@gm...> wrote:
>
>> I believe the normalization behaviour is wrong for contourf at least when
>> using a BoundaryNorm. In the script below I am using the same norm to plot
>> the same data using contourf and pcolormesh. The color should change around
>> an x value of 0.15 but it is shifted somewhat for contourf. I do realize
>> that the pcolormesh is in principle shifted a little - but with a grid
>> spacing of 0.001 that should not matter. Please see the example script
>> below.
>>
>> Best regards,
>> Jesper
>>
>> """
>> Test inconsistent normalization behaviour for matplotlib
>> """
>> import numpy as np
>> import matplotlib.pyplot as plt
>> from matplotlib.colors import from_levels_and_colors
>>
>> # Make custom colormap and norm
>> levs = [0.0, 0.1, 0.2]
>> cols = [[0.00392156862745098, 0.23137254901960785, 0.07450980392156863],
>> [0.00392156862745098, 0.49019607843137253, 0.15294117647058825]]
>> extend = 'neither'
>> cmap, norm = from_levels_and_colors(levs, cols, extend)
>>
>> # Setup testdata
>> a = np.arange(0.05, 0.15, 0.001, dtype=np.float_)
>> a, b = np.meshgrid(a, a)0
>> plt.contourf(a, b, a, norm=norm, cmap=cmap, antialiased=False)
>> plt.savefig('contourf.png')
>> plt.clf()
>> plt.pcolormesh(a, b, a, norm=norm, cmap=cmap, antialiased=False)
>> plt.savefig('pcolormesh.png')
>>
>
> Jesper,
>
> Regardless of whether you specify a colormap and norm, if you want
> contourf to calculate contours at particular levels
> then you need to specify those levels.  If you don't then contourf will
> choose the levels for you, and in your case these are chosen to be
> [0.045  0.06   0.075  0.09   0.105  0.12   0.135  0.15 ]
> which is why you see the color transition at x=0.105.
>
> To fix this, change your contourf line from
> plt.contourf(a, b, a, norm=norm, cmap=cmap, antialiased=False)
> to
> plt.contourf(a, b, a, norm=norm, cmap=cmap, antialiased=False, levels=levs)
> and you will get exactly what you want.
>
> Ian
>

By the way.. don't know if it's important but I'm running that under OSX
Mavericks 10.9.2


On Fri, Mar 28, 2014 at 8:40 PM, Jorge Ferrando <jor...@gm...> wrote:

> Hi Sterling.
>
> Sorry, the line is this:
>
> asfdll = loadLibrary('libAPI.dylib')
>
>
> On Fri, Mar 28, 2014 at 8:38 PM, Sterling Smith <sm...@fu...>wrote:
>
>> You forgot to add the line that causes the problems.
>>
>> You might want to give a minimum self contained working example.
>>
>> -Sterling
>>
>> On Mar 28, 2014, at 12:20PM, Jorge Ferrando wrote:
>>
>> > Hello
>> >
>> > I'm workign on a project where we are using ctypes and I wanted to plot
>> some data with matplotlib.
>> >
>> > Everything is running fine, but as soon as I add this line:
>> >
>> > The project crashes with this error:
>> >
>> > File "........./myfile.py", line 117, in loadLibrary
>> >     return cdll.LoadLibrary(library)
>> >   File
>> "/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/ctypes/__init__.py",
>> line 443, in LoadLibrary
>> >     return self._dlltype(name)
>> >   File
>> "/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/ctypes/__init__.py",
>> line 365, in __init__
>> >     self._handle = _dlopen(self._name, mode)
>> > OSError: dlopen(libAPI.dylib, 6): initializer function 0x7fff9568c26e
>> not in mapped image for /Users/jorfermo/Desarrollo//libAPI.dylib
>> >
>> > It seems to be that there's a conflict between ctypes library and
>> matplotlib but i failed to find a workaround to this error.
>> >
>> > Any Idea how to solve it?
>> >
>> > Thank you
>> >
>> > Jorge
>> >
>> ------------------------------------------------------------------------------
>> > _______________________________________________
>> > Matplotlib-users mailing list
>> > Mat...@li...
>> > https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>>
>>
>

Hi Sterling.

Sorry, the line is this:

asfdll = loadLibrary('libAPI.dylib')


On Fri, Mar 28, 2014 at 8:38 PM, Sterling Smith <sm...@fu...>wrote:

> You forgot to add the line that causes the problems.
>
> You might want to give a minimum self contained working example.
>
> -Sterling
>
> On Mar 28, 2014, at 12:20PM, Jorge Ferrando wrote:
>
> > Hello
> >
> > I'm workign on a project where we are using ctypes and I wanted to plot
> some data with matplotlib.
> >
> > Everything is running fine, but as soon as I add this line:
> >
> > The project crashes with this error:
> >
> > File "........./myfile.py", line 117, in loadLibrary
> >     return cdll.LoadLibrary(library)
> >   File
> "/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/ctypes/__init__.py",
> line 443, in LoadLibrary
> >     return self._dlltype(name)
> >   File
> "/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/ctypes/__init__.py",
> line 365, in __init__
> >     self._handle = _dlopen(self._name, mode)
> > OSError: dlopen(libAPI.dylib, 6): initializer function 0x7fff9568c26e
> not in mapped image for /Users/jorfermo/Desarrollo//libAPI.dylib
> >
> > It seems to be that there's a conflict between ctypes library and
> matplotlib but i failed to find a workaround to this error.
> >
> > Any Idea how to solve it?
> >
> > Thank you
> >
> > Jorge
> >
> ------------------------------------------------------------------------------
> > _______________________________________________
> > Matplotlib-users mailing list
> > Mat...@li...
> > https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>
>

You forgot to add the line that causes the problems.

You might want to give a minimum self contained working example.

-Sterling

On Mar 28, 2014, at 12:20PM, Jorge Ferrando wrote:

> Hello
> 
> I'm workign on a project where we are using ctypes and I wanted to plot some data with matplotlib.
> 
> Everything is running fine, but as soon as I add this line:
> 
> The project crashes with this error:
> 
> File "........./myfile.py", line 117, in loadLibrary
>     return cdll.LoadLibrary(library)
>   File "/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/ctypes/__init__.py", line 443, in LoadLibrary
>     return self._dlltype(name)
>   File "/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/ctypes/__init__.py", line 365, in __init__
>     self._handle = _dlopen(self._name, mode)
> OSError: dlopen(libAPI.dylib, 6): initializer function 0x7fff9568c26e not in mapped image for /Users/jorfermo/Desarrollo//libAPI.dylib
> 
> It seems to be that there's a conflict between ctypes library and matplotlib but i failed to find a workaround to this error.
> 
> Any Idea how to solve it?
> 
> Thank you
> 
> Jorge
> ------------------------------------------------------------------------------
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users

Hello

I'm workign on a project where we are using ctypes and I wanted to plot
some data with matplotlib.

Everything is running fine, but as soon as I add this line:

The project crashes with this error:

File "........./myfile.py", line 117, in loadLibrary
    return cdll.LoadLibrary(library)
  File
"/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/ctypes/__init__.py",
line 443, in LoadLibrary
    return self._dlltype(name)
  File
"/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/ctypes/__init__.py",
line 365, in __init__
    self._handle = _dlopen(self._name, mode)
OSError: dlopen(libAPI.dylib, 6): initializer function 0x7fff9568c26e not
in mapped image for /Users/jorfermo/Desarrollo//libAPI.dylib

It seems to be that there's a conflict between ctypes library and
matplotlib but i failed to find a workaround to this error.

Any Idea how to solve it?

Thank you

Jorge

Hi,

I just ran across this new Qt "add-on" for data visualization :
blog.qt.digia.com/blog/2014/03/26/qt-data-visualization-1-0-released/

It's a bit off-topic because I think there is not (yet?) a Python
binding, but there is a demo video which is worth taking a look at.
The video doesn't mention the API so the question is open : how easy is
it to build these visualization GUIs, compared to Mayavi for instance ?
(one minor difference at least... not open source)

In the same category, but focusing on 2D plots, I see that Digia has
also produced a "Qt Charts" add-on. This one has a Python binding
http://www.riverbankcomputing.co.uk/software/pyqtchart/intro (commercial
license only)

-- 
Pierre

On 28 March 2014 12:56, Jesper Larsen <jes...@gm...> wrote:

> I believe the normalization behaviour is wrong for contourf at least when
> using a BoundaryNorm. In the script below I am using the same norm to plot
> the same data using contourf and pcolormesh. The color should change around
> an x value of 0.15 but it is shifted somewhat for contourf. I do realize
> that the pcolormesh is in principle shifted a little - but with a grid
> spacing of 0.001 that should not matter. Please see the example script
> below.
>
> Best regards,
> Jesper
>
> """
> Test inconsistent normalization behaviour for matplotlib
> """
> import numpy as np
> import matplotlib.pyplot as plt
> from matplotlib.colors import from_levels_and_colors
>
> # Make custom colormap and norm
> levs = [0.0, 0.1, 0.2]
> cols = [[0.00392156862745098, 0.23137254901960785, 0.07450980392156863],
> [0.00392156862745098, 0.49019607843137253, 0.15294117647058825]]
> extend = 'neither'
> cmap, norm = from_levels_and_colors(levs, cols, extend)
>
> # Setup testdata
> a = np.arange(0.05, 0.15, 0.001, dtype=np.float_)
> a, b = np.meshgrid(a, a)0
> plt.contourf(a, b, a, norm=norm, cmap=cmap, antialiased=False)
> plt.savefig('contourf.png')
> plt.clf()
> plt.pcolormesh(a, b, a, norm=norm, cmap=cmap, antialiased=False)
> plt.savefig('pcolormesh.png')
>

Jesper,

Regardless of whether you specify a colormap and norm, if you want contourf
to calculate contours at particular levels
then you need to specify those levels.  If you don't then contourf will
choose the levels for you, and in your case these are chosen to be
[0.045  0.06   0.075  0.09   0.105  0.12   0.135  0.15 ]
which is why you see the color transition at x=0.105.

To fix this, change your contourf line from
plt.contourf(a, b, a, norm=norm, cmap=cmap, antialiased=False)
to
plt.contourf(a, b, a, norm=norm, cmap=cmap, antialiased=False, levels=levs)
and you will get exactly what you want.

Ian

Hi matplotlib users,

I believe the normalization behaviour is wrong for contourf at least when
using a BoundaryNorm. In the script below I am using the same norm to plot
the same data using contourf and pcolormesh. The color should change around
an x value of 0.15 but it is shifted somewhat for contourf. I do realize
that the pcolormesh is in principle shifted a little - but with a grid
spacing of 0.001 that should not matter. Please see the example script
below.

Best regards,
Jesper

"""
Test inconsistent normalization behaviour for matplotlib
"""
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import from_levels_and_colors

# Make custom colormap and norm
levs = [0.0, 0.1, 0.2]
cols = [[0.00392156862745098, 0.23137254901960785, 0.07450980392156863],
[0.00392156862745098, 0.49019607843137253, 0.15294117647058825]]
extend = 'neither'
cmap, norm = from_levels_and_colors(levs, cols, extend)

# Setup testdata
a = np.arange(0.05, 0.15, 0.001, dtype=np.float_)
a, b = np.meshgrid(a, a)
plt.contourf(a, b, a, norm=norm, cmap=cmap, antialiased=False)
plt.savefig('contourf.png')
plt.clf()
plt.pcolormesh(a, b, a, norm=norm, cmap=cmap, antialiased=False)
plt.savefig('pcolormesh.png')

Dear colleagues, 

Took a while to fix it here. 

 I've chosen the qt package downloading and installing it manually 
together with the sip package as well as the pyqy4 package and qt-devel 
package. 

The pyqy4 configure process showed a dependency from the module "qmake" 
which I fixed with the install command:  python configure.py -q 
/usr/lib64/qt4/bin/qmake -g 
Further I've updated the matplotlibrc file pointing the entry to:  backend 
: QT4Agg. 

Numpy 1.8.1 and Matplotlib 1.3.1 is executing now the 3dScatter diagram, 
all working nicely, latest versions of Matplotlib and Numpy are running on 
the RedHat 6.4. 

Thanks Ben for your guidance and support. Problem solved. 

Regards, 
Claude 

Claude Falbriard 
Certified IT Specialist L2 - Middleware
AMS Hortolândia / SP - Brazil
phone:    +55 13 9 9760 0453
cell:         +55 13 9 8117 3316
e-mail:    cl...@br...

----- Forwarded by Claude Falbriard/Brazil/IBM on 27/03/2014 21:20 -----

From:   Benjamin Root <ben...@ou...>
To:     falbriard <cl...@br...>, 
Cc:     Matplotlib Users <mat...@li...>
Date:   27/03/2014 17:16
Subject:        Re: [Matplotlib-users] RedHat and Release Upgrade to Numpy 
1.8.1 and Matplotlib 1.3.1 / Install from Source
Sent by:        ben...@gm...



Claude,

Just noticed your matplotlibrc file has "agg" listed for the backend. That 
usually happens when the build process for matplotlib does not find any 
development files for a particular backend to be available. See this page: 
http://matplotlib.org/faq/installing_faq.html#install-from-git

Essentially, just having the "devel" packages for one or more of the 
various toolkits is sufficient. Once you have that installed, clean the 
build and rebuild.

Cheers!
Ben Root



On Thu, Mar 27, 2014 at 4:08 PM, <cl...@br...> wrote:
Dear Ben, 

I've also repeated the install using pip unistall and install of 
matplotlib, both completed successfully but the issue remains, no 
graphical display at the RedHat Linux, as well as very fast and silent 
exit from the program code. 


The source used for my test is the 3D Scatter Sample form the Gallery: 
  
from mpl_toolkits.mplot3d import Axes3D 
import numpy as np 
import matplotlib.pyplot as plt 

fig = plt.figure() 
ax = fig.gca(projection='3d') 

x = np.linspace(0, 1, 100) 
y = np.sin(x * 2 * np.pi) / 2 + 0.5 
ax.plot(x, y, zs=0, zdir='z', label='zs=0, zdir=z') 

colors = ('r', 'g', 'b', 'k') 
for c in colors: 
    x = np.random.sample(20) 
    y = np.random.sample(20) 
    ax.scatter(x, y, 0, zdir='y', c=c) 

ax.legend() 
ax.set_xlim3d(0, 1) 
ax.set_ylim3d(0, 1) 
ax.set_zlim3d(0, 1) 

plt.show() 


The maplotlibrc file: 

### MATPLOTLIBRC FORMAT 

# This is a sample matplotlib configuration file - you can find a copy 
# of it on your system in 
# site-packages/matplotlib/mpl-data/matplotlibrc.  If you edit it 
# there, please note that it will be overwritten in your next install. 
# If you want to keep a permanent local copy that will not be 
# overwritten, place it in HOME/.matplotlib/matplotlibrc (unix/linux 
# like systems) and C:\Documents and Settings\yourname\.matplotlib 
# (win32 systems). 
# 
# This file is best viewed in a editor which supports python mode 
# syntax highlighting. Blank lines, or lines starting with a comment 
# symbol, are ignored, as are trailing comments.  Other lines must 
# have the format 
#    key : val # optional comment 
# 
# Colors: for the color values below, you can either use - a 
# matplotlib color string, such as r, k, or b - an rgb tuple, such as 
# (1.0, 0.5, 0.0) - a hex string, such as ff00ff or #ff00ff - a scalar 
# grayscale intensity such as 0.75 - a legal html color name, eg red, 
# blue, darkslategray 

#### CONFIGURATION BEGINS HERE 

# the default backend; one of GTK GTKAgg GTKCairo GTK3Agg GTK3Cairo 
# CocoaAgg MacOSX Qt4Agg TkAgg WX WXAgg Agg Cairo GDK PS PDF SVG 
# Template 
# You can also deploy your own backend outside of matplotlib by 
# referring to the module name (which must be in the PYTHONPATH) as 
# 'module://my_backend' 
backend      : agg 

# If you are using the Qt4Agg backend, you can choose here 
# to use the PyQt4 bindings or the newer PySide bindings to 
# the underlying Qt4 toolkit. 
#backend.qt4 : PyQt4        # PyQt4 | PySide 

# Note that this can be overridden by the environment variable 
# QT_API used by Enthought Tool Suite (ETS); valid values are 
# "pyqt" and "pyside".  The "pyqt" setting has the side effect of 
# forcing the use of Version 2 API for QString and QVariant. 

# The port to use for the web server in the WebAgg backend. 
# webagg.port : 8888 

# If webagg.port is unavailable, a number of other random ports will 
# be tried until one that is available is found. 
# webagg.port_retries : 50 

# When True, open the webbrowser to the plot that is shown 
# webagg.open_in_browser : True 

# if you are running pyplot inside a GUI and your backend choice 
# conflicts, we will automatically try to find a compatible one for 
# you if backend_fallback is True 
#backend_fallback: True 

#interactive  : False 
#toolbar      : toolbar2   # None | toolbar2  ("classic" is deprecated) 
#timezone     : UTC        # a pytz timezone string, eg US/Central or 
Europe/Paris 

# Where your matplotlib data lives if you installed to a non-default 
# location.  This is where the matplotlib fonts, bitmaps, etc reside 
#datapath : /home/jdhunter/mpldata 


### LINES 
# See http://matplotlib.org/api/artist_api.html#module-matplotlib.lines 
for more 
# information on line properties. 
#lines.linewidth   : 1.0     # line width in points 
#lines.linestyle   : -       # solid line 
#lines.color       : blue    # has no affect on plot(); see 
axes.color_cycle 
#lines.marker      : None    # the default marker 
#lines.markeredgewidth  : 0.5     # the line width around the marker 
symbol 
#lines.markersize  : 6            # markersize, in points 
#lines.dash_joinstyle : miter        # miter|round|bevel 
#lines.dash_capstyle : butt          # butt|round|projecting 
#lines.solid_joinstyle : miter       # miter|round|bevel 
#lines.solid_capstyle : projecting   # butt|round|projecting 
#lines.antialiased : True         # render lines in antialised (no 
jaggies) 

### PATCHES 
# Patches are graphical objects that fill 2D space, like polygons or 
# circles.  See 
# http://matplotlib.org/api/artist_api.html#module-matplotlib.patches 
# information on patch properties 
#patch.linewidth        : 1.0     # edge width in points 
#patch.facecolor        : blue 
#patch.edgecolor        : black 
#patch.antialiased      : True    # render patches in antialised (no 
jaggies) 

### FONT 
# 
# font properties used by text.Text.  See 
# http://matplotlib.org/api/font_manager_api.html for more 
# information on font properties.  The 6 font properties used for font 
# matching are given below with their default values. 
# 
# The font.family property has five values: 'serif' (e.g., Times), 
# 'sans-serif' (e.g., Helvetica), 'cursive' (e.g., Zapf-Chancery), 
# 'fantasy' (e.g., Western), and 'monospace' (e.g., Courier).  Each of 
# these font families has a default list of font names in decreasing 
# order of priority associated with them.  When text.usetex is False, 
# font.family may also be one or more concrete font names. 
# 
# The font.style property has three values: normal (or roman), italic 
# or oblique.  The oblique style will be used for italic, if it is not 
# present. 
# 
# The font.variant property has two values: normal or small-caps.  For 
# TrueType fonts, which are scalable fonts, small-caps is equivalent 
# to using a font size of 'smaller', or about 83% of the current font 
# size. 
# 
# The font.weight property has effectively 13 values: normal, bold, 
# bolder, lighter, 100, 200, 300, ..., 900.  Normal is the same as 
# 400, and bold is 700.  bolder and lighter are relative values with 
# respect to the current weight. 
# 
# The font.stretch property has 11 values: ultra-condensed, 
# extra-condensed, condensed, semi-condensed, normal, semi-expanded, 
# expanded, extra-expanded, ultra-expanded, wider, and narrower.  This 
# property is not currently implemented. 
# 
# The font.size property is the default font size for text, given in pts. 
# 12pt is the standard value. 
# 
#font.family         : sans-serif 
#font.style          : normal 
#font.variant        : normal 
#font.weight         : medium 
#font.stretch        : normal 
# note that font.size controls default text sizes.  To configure 
# special text sizes tick labels, axes, labels, title, etc, see the rc 
# settings for axes and ticks. Special text sizes can be defined 
# relative to font.size, using the following values: xx-small, x-small, 
# small, medium, large, x-large, xx-large, larger, or smaller 
#font.size           : 12.0 
#font.serif          : Bitstream Vera Serif, New Century Schoolbook, 
Century Schoolbook L, Utopia, ITC Bookman, Bookman, Nimbus Roman No9 L, 
Times New Roman, Times, Palatino, Charter, serif 
#font.sans-serif     : Bitstream Vera Sans, Lucida Grande, Verdana, 
Geneva, Lucid, Arial, Helvetica, Avant Garde, sans-serif 
#font.cursive        : Apple Chancery, Textile, Zapf Chancery, Sand, 
cursive 
#font.fantasy        : Comic Sans MS, Chicago, Charcoal, Impact, Western, 
fantasy 
#font.monospace      : Bitstream Vera Sans Mono, Andale Mono, Nimbus Mono 
L, Courier New, Courier, Fixed, Terminal, monospace 

### TEXT 
# text properties used by text.Text.  See 
# http://matplotlib.org/api/artist_api.html#module-matplotlib.text for 
more 
# information on text properties 

#text.color          : black 

### LaTeX customizations. See 
http://www.scipy.org/Wiki/Cookbook/Matplotlib/UsingTex 
#text.usetex         : False  # use latex for all text handling. The 
following fonts 
                              # are supported through the usual rc 
parameter settings: 
                              # new century schoolbook, bookman, times, 
palatino, 
                              # zapf chancery, charter, serif, sans-serif, 
helvetica, 
                              # avant garde, courier, monospace, computer 
modern roman, 
                              # computer modern sans serif, computer 
modern typewriter 
                              # If another font is desired which can 
loaded using the 
                              # LaTeX \usepackage command, please inquire 
at the 
                              # matplotlib mailing list 
#text.latex.unicode : False # use "ucs" and "inputenc" LaTeX packages for 
handling 
                            # unicode strings. 
#text.latex.preamble :  # IMPROPER USE OF THIS FEATURE WILL LEAD TO LATEX 
FAILURES 
                            # AND IS THEREFORE UNSUPPORTED. PLEASE DO NOT 
ASK FOR HELP 
                            # IF THIS FEATURE DOES NOT DO WHAT YOU EXPECT 
IT TO. 
                            # preamble is a comma separated list of LaTeX 
statements 
                            # that are included in the LaTeX document 
preamble. 
                            # An example: 
                            # text.latex.preamble : 
\usepackage{bm},\usepackage{euler} 
                            # The following packages are always loaded 
with usetex, so 
                            # beware of package collisions: color, 
geometry, graphicx, 
                            # type1cm, textcomp. Adobe Postscript (PSSNFS) 
font packages 
                            # may also be loaded, depending on your font 
settings 

#text.dvipnghack : None      # some versions of dvipng don't handle alpha 
                             # channel properly.  Use True to correct 
                             # and flush ~/.matplotlib/tex.cache 
                             # before testing and False to force 
                             # correction off.  None will try and 
                             # guess based on your dvipng version 

#text.hinting : 'auto' # May be one of the following: 
                       #   'none': Perform no hinting 
                       #   'auto': Use freetype's autohinter 
                       #   'native': Use the hinting information in the 
                       #             font file, if available, and if your 
                       #             freetype library supports it 
                       #   'either': Use the native hinting information, 
                       #             or the autohinter if none is 
available. 
                       # For backward compatibility, this value may also 
be 
                       # True === 'auto' or False === 'none'. 
#text.hinting_factor : 8 # Specifies the amount of softness for hinting in 
the 
                         # horizontal direction.  A value of 1 will hint 
to full 
                         # pixels.  A value of 2 will hint to half pixels 
etc. 

#text.antialiased : True # If True (default), the text will be 
antialiased. 
                         # This only affects the Agg backend. 

# The following settings allow you to select the fonts in math mode. 
# They map from a TeX font name to a fontconfig font pattern. 
# These settings are only used if mathtext.fontset is 'custom'. 
# Note that this "custom" mode is unsupported and may go away in the 
# future. 
#mathtext.cal : cursive 
#mathtext.rm  : serif 
#mathtext.tt  : monospace 
#mathtext.it  : serif:italic 
#mathtext.bf  : serif:bold 
#mathtext.sf  : sans 
#mathtext.fontset : cm # Should be 'cm' (Computer Modern), 'stix', 
                       # 'stixsans' or 'custom' 
#mathtext.fallback_to_cm : True  # When True, use symbols from the 
Computer Modern 
                                 # fonts when a symbol can not be found in 
one of 
                                 # the custom math fonts. 

#mathtext.default : it # The default font to use for math. 
                       # Can be any of the LaTeX font names, including 
                       # the special name "regular" for the same font 
                       # used in regular text. 

### AXES 
# default face and edge color, default tick sizes, 
# default fontsizes for ticklabels, and so on.  See 
# http://matplotlib.org/api/axes_api.html#module-matplotlib.axes 
#axes.hold           : True    # whether to clear the axes by default on 
#axes.facecolor      : white   # axes background color 
#axes.edgecolor      : black   # axes edge color 
#axes.linewidth      : 1.0     # edge linewidth 
#axes.grid           : False   # display grid or not 
#axes.titlesize      : large   # fontsize of the axes title 
#axes.labelsize      : medium  # fontsize of the x any y labels 
#axes.labelweight    : normal  # weight of the x and y labels 
#axes.labelcolor     : black 
#axes.axisbelow      : False   # whether axis gridlines and ticks are 
below 
                              # the axes elements (lines, text, etc) 
#axes.formatter.limits : -7, 7 # use scientific notation if log10 
                               # of the axis range is smaller than the 
                               # first or larger than the second 
#axes.formatter.use_locale : False # When True, format tick labels 
                                   # according to the user's locale. 
                                   # For example, use ',' as a decimal 
                                   # separator in the fr_FR locale. 
#axes.formatter.use_mathtext : False # When True, use mathtext for 
scientific 
                                     # notation. 
#axes.unicode_minus  : True    # use unicode for the minus symbol 
                               # rather than hyphen.  See 
                               # 
http://en.wikipedia.org/wiki/Plus_and_minus_signs#Character_codes 
#axes.color_cycle    : b, g, r, c, m, y, k  # color cycle for plot lines 
                                            # as list of string 
colorspecs: 
                                            # single letter, long name, or 

                                            # web-style hex 
#axes.xmargin        : 0  # x margin.  See `axes.Axes.margins` 
#axes.ymargin        : 0  # y margin See `axes.Axes.margins` 

#polaraxes.grid      : True    # display grid on polar axes 
#axes3d.grid         : True    # display grid on 3d axes 

### TICKS 
# see http://matplotlib.org/api/axis_api.html#matplotlib.axis.Tick 
#xtick.major.size     : 4      # major tick size in points 
#xtick.minor.size     : 2      # minor tick size in points 
#xtick.major.width    : 0.5    # major tick width in points 
#xtick.minor.width    : 0.5    # minor tick width in points 
#xtick.major.pad      : 4      # distance to major tick label in points 
#xtick.minor.pad      : 4      # distance to the minor tick label in 
points 
#xtick.color          : k      # color of the tick labels 
#xtick.labelsize      : medium # fontsize of the tick labels 
#xtick.direction      : in     # direction: in, out, or inout 

#ytick.major.size     : 4      # major tick size in points 
#ytick.minor.size     : 2      # minor tick size in points 
#ytick.major.width    : 0.5    # major tick width in points 
#ytick.minor.width    : 0.5    # minor tick width in points 
#ytick.major.pad      : 4      # distance to major tick label in points 
#ytick.minor.pad      : 4      # distance to the minor tick label in 
points 
#ytick.color          : k      # color of the tick labels 
#ytick.labelsize      : medium # fontsize of the tick labels 
#ytick.direction      : in     # direction: in, out, or inout 


### GRIDS 
#grid.color       :   black   # grid color 
#grid.linestyle   :   :       # dotted 
#grid.linewidth   :   0.5     # in points 
#grid.alpha       :   1.0     # transparency, between 0.0 and 1.0 

### Legend 
#legend.fancybox      : False  # if True, use a rounded box for the 
                               # legend, else a rectangle 
#legend.isaxes        : True 
#legend.numpoints     : 2      # the number of points in the legend line 
#legend.fontsize      : large 
#legend.borderpad     : 0.5    # border whitespace in fontsize units 
#legend.markerscale   : 1.0    # the relative size of legend markers vs. 
original 
# the following dimensions are in axes coords 
#legend.labelspacing  : 0.5    # the vertical space between the legend 
entries in fraction of fontsize 
#legend.handlelength  : 2.     # the length of the legend lines in 
fraction of fontsize 
#legend.handleheight  : 0.7     # the height of the legend handle in 
fraction of fontsize 
#legend.handletextpad : 0.8    # the space between the legend line and 
legend text in fraction of fontsize 
#legend.borderaxespad : 0.5   # the border between the axes and legend 
edge in fraction of fontsize 
#legend.columnspacing : 2.    # the border between the axes and legend 
edge in fraction of fontsize 
#legend.shadow        : False 
#legend.frameon       : True   # whether or not to draw a frame around 
legend 
#legend.scatterpoints : 3 # number of scatter points 

### FIGURE 
# See http://matplotlib.org/api/figure_api.html#matplotlib.figure.Figure 
#figure.figsize   : 8, 6    # figure size in inches 
#figure.dpi       : 80      # figure dots per inch 
#figure.facecolor : 0.75    # figure facecolor; 0.75 is scalar gray 
#figure.edgecolor : white   # figure edgecolor 
#figure.autolayout : False  # When True, automatically adjust subplot 
                            # parameters to make the plot fit the figure 
#figure.max_open_warning : 20  # The maximum number of figures to open 
through 
                               # the pyplot interface before emitting a 
warning. 
                               # If less than one this feature is 
disabled. 

# The figure subplot parameters.  All dimensions are a fraction of the 
# figure width or height 
#figure.subplot.left    : 0.125  # the left side of the subplots of the 
figure 
#figure.subplot.right   : 0.9    # the right side of the subplots of the 
figure 
#figure.subplot.bottom  : 0.1    # the bottom of the subplots of the 
figure 
#figure.subplot.top     : 0.9    # the top of the subplots of the figure 
#figure.subplot.wspace  : 0.2    # the amount of width reserved for blank 
space between subplots 
#figure.subplot.hspace  : 0.2    # the amount of height reserved for white 
space between subplots 

### IMAGES 
#image.aspect : equal             # equal | auto | a number 
#image.interpolation  : bilinear  # see help(imshow) for options 
#image.cmap   : jet               # gray | jet etc... 
#image.lut    : 256               # the size of the colormap lookup table 
#image.origin : upper             # lower | upper 
#image.resample  : False 

### CONTOUR PLOTS 
#contour.negative_linestyle :  dashed # dashed | solid 

### Agg rendering 
### Warning: experimental, 2008/10/10 
#agg.path.chunksize : 0           # 0 to disable; values in the range 
                                  # 10000 to 100000 can improve speed 
slightly 
                                  # and prevent an Agg rendering failure 
                                  # when plotting very large data sets, 
                                  # especially if they are very gappy. 
                                  # It may cause minor artifacts, though. 
                                  # A value of 20000 is probably a good 
                                  # starting point. 
### SAVING FIGURES 
#path.simplify : True   # When True, simplify paths by removing 
"invisible" 
                        # points to reduce file size and increase 
rendering 
                        # speed 
#path.simplify_threshold : 0.1  # The threshold of similarity below which 
                                # vertices will be removed in the 
simplification 
                                # process 
#path.snap : True # When True, rectilinear axis-aligned paths will be 
snapped to 
                  # the nearest pixel when certain criteria are met.  When 
False, 
                  # paths will never be snapped. 
#path.sketch : None # May be none, or a 3-tuple of the form (scale, 
length, 
                    # randomness). 
                    # *scale* is the amplitude of the wiggle 
                    # perpendicular to the line (in pixels).  *length* 
                    # is the length of the wiggle along the line (in 
                    # pixels).  *randomness* is the factor by which 
                    # the length is randomly scaled. 

# the default savefig params can be different from the display params 
# e.g., you may want a higher resolution, or to make the figure 
# background white 
#savefig.dpi         : 100      # figure dots per inch 
#savefig.facecolor   : white    # figure facecolor when saving 
#savefig.edgecolor   : white    # figure edgecolor when saving 
#savefig.format      : png      # png, ps, pdf, svg 
#savefig.bbox        : standard # 'tight' or 'standard'. 
#savefig.pad_inches  : 0.1      # Padding to be used when bbox is set to 
'tight' 
#savefig.jpeg_quality: 95       # when a jpeg is saved, the default 
quality parameter. 
#savefig.directory   : ~        # default directory in savefig dialog box, 

                                # leave empty to always use current 
working directory 

# tk backend params 
#tk.window_focus   : False    # Maintain shell focus for TkAgg 

# ps backend params 
#ps.papersize      : letter   # auto, letter, legal, ledger, A0-A10, 
B0-B10 
#ps.useafm         : False    # use of afm fonts, results in small files 
#ps.usedistiller   : False    # can be: None, ghostscript or xpdf 
                                          # Experimental: may produce 
smaller files. 
                                          # xpdf intended for production 
of publication quality files, 
                                          # but requires ghostscript, xpdf 
and ps2eps 
#ps.distiller.res  : 6000      # dpi 
#ps.fonttype       : 3         # Output Type 3 (Type3) or Type 42 
(TrueType) 

# pdf backend params 
#pdf.compression   : 6 # integer from 0 to 9 
                       # 0 disables compression (good for debugging) 
#pdf.fonttype       : 3         # Output Type 3 (Type3) or Type 42 
(TrueType) 

# svg backend params 
#svg.image_inline : True       # write raster image data directly into the 
svg file 
#svg.image_noscale : False     # suppress scaling of raster data embedded 
in SVG 
#svg.fonttype : 'path'         # How to handle SVG fonts: 
#    'none': Assume fonts are installed on the machine where the SVG will 
be viewed. 
#    'path': Embed characters as paths -- supported by most SVG renderers 
#    'svgfont': Embed characters as SVG fonts -- supported only by Chrome, 

#               Opera and Safari 

# docstring params 
#docstring.hardcopy = False  # set this when you want to generate hardcopy 
docstring 

# Set the verbose flags.  This controls how much information 
# matplotlib gives you at runtime and where it goes.  The verbosity 
# levels are: silent, helpful, debug, debug-annoying.  Any level is 
# inclusive of all the levels below it.  If your setting is "debug", 
# you'll get all the debug and helpful messages.  When submitting 
# problems to the mailing-list, please set verbose to "helpful" or "debug" 

# and paste the output into your report. 
# 
# The "fileo" gives the destination for any calls to verbose.report. 
# These objects can a filename, or a filehandle like sys.stdout. 
# 
# You can override the rc default verbosity from the command line by 
# giving the flags --verbose-LEVEL where LEVEL is one of the legal 
# levels, eg --verbose-helpful. 
# 
# You can access the verbose instance in your code 
#   from matplotlib import verbose. 
#verbose.level  : silent      # one of silent, helpful, debug, 
debug-annoying 
#verbose.fileo  : sys.stdout  # a log filename, sys.stdout or sys.stderr 

# Event keys to interact with figures/plots via keyboard. 
# Customize these settings according to your needs. 
# Leave the field(s) empty if you don't need a key-map. (i.e., fullscreen 
: '') 

#keymap.fullscreen : f               # toggling 
#keymap.home : h, r, home            # home or reset mnemonic 
#keymap.back : left, c, backspace    # forward / backward keys to enable 
#keymap.forward : right, v           #   left handed quick navigation 
#keymap.pan : p                      # pan mnemonic 
#keymap.zoom : o                     # zoom mnemonic 
#keymap.save : s                     # saving current figure 
#keymap.quit : ctrl+w, cmd+w         # close the current figure 
#keymap.grid : g                     # switching on/off a grid in current 
axes 
#keymap.yscale : l                   # toggle scaling of y-axes 
('log'/'linear') 
#keymap.xscale : L, k                # toggle scaling of x-axes 
('log'/'linear') 
#keymap.all_axes : a                 # enable all axes 

# Control location of examples data files 
#examples.directory : ''   # directory to look in for custom installation 

###ANIMATION settings 
#animation.writer : ffmpeg         # MovieWriter 'backend' to use 
#animation.codec : mp4             # Codec to use for writing movie 
#animation.bitrate: -1             # Controls size/quality tradeoff for 
movie. 
                                   # -1 implies let utility auto-determine 

#animation.frame_format: 'png'     # Controls frame format used by temp 
files 
#animation.ffmpeg_path: 'ffmpeg'   # Path to ffmpeg binary. Without full 
path 
                                   # $PATH is searched 
#animation.ffmpeg_args: ''         # Additional arguments to pass to 
ffmpeg 
#animation.avconv_path: 'avconv'   # Path to avconv binary. Without full 
path 
                                   # $PATH is searched 
#animation.avconv_args: ''         # Additional arguments to pass to 
avconv 
#animation.mencoder_path: 'mencoder' 
                                   # Path to mencoder binary. Without full 
path 
                                   # $PATH is searched 
#animation.mencoder_args: ''       # Additional arguments to pass to 
mencoder 

Hope this helps to isolate the error. 

Regards, 
Claude 


Claude Falbriard 
Certified IT Specialist L2 - Middleware
AMS Hortolândia / SP - Brazil
phone:    +55 13 9 9760 0453
cell:         +55 13 9 8117 3316
e-mail:    cl...@br...




From:        Benjamin Root <ben...@ou...> 
To:        falbriard <cl...@br...>, Matplotlib Users <
mat...@li...>, 
Date:        27/03/2014 16:20 
Subject:        Re: [Matplotlib-users] RedHat and Release Upgrade to Numpy 
1.8.1 and Matplotlib 1.3.1 / Install from Source 
Sent by:        ben...@gm... 



Claude, it would be helpful to know exactly what code you executed. Some 
example code assumes interactive modes, while others simply save files 
without ever showing them to the screen.

Also, please include a copy of your matplotlibrc file.

Ben Root



On Thu, Mar 27, 2014 at 1:47 PM, <cl...@br...> wrote: 
Dear Ben, 

The execution of any of the Matplotlib sample code start quickly and and 
exits immediately with no error message displayed  at the screen. The 
process runs instantly, so there is no wait in the process. 
Looks more like a missing setup option, matplotlib does not find a valid 
graphical screen display environment. What do you think is causing this 
error in RedHat Linux? 

Regards, 
Claude 

Claude Falbriard 
Certified IT Specialist L2 - Middleware
AMS Hortolândia / SP - Brazil
phone:    +55 13 9 9760 0453
cell:         +55 13 9 8117 3316
e-mail:    cl...@br...



From:        Benjamin Root <ben...@ou...> 
To:        falbriard <cl...@br...>, 
Cc:        Matplotlib Users <mat...@li...> 
Date:        27/03/2014 14:32 
Subject:        Re: [Matplotlib-users] RedHat and Release Upgrade to Numpy 
1.8.1 and Matplotlib 1.3.1 / Install from Source 
Sent by:        ben...@gm... 




How long did you wait? Do allow approximately one minute for the first 
execution to allow for the font.cache to be built. It can appear that the 
process has "hung" because it is waiting for "fc-list" subprocess to 
complete. 

Cheers!
Ben Root 


------------------------------------------------------------------------------

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

Claude,

Just noticed your matplotlibrc file has "agg" listed for the backend. That
usually happens when the build process for matplotlib does not find any
development files for a particular backend to be available. See this page:
http://matplotlib.org/faq/installing_faq.html#install-from-git

Essentially, just having the "devel" packages for one or more of the
various toolkits is sufficient. Once you have that installed, clean the
build and rebuild.

Cheers!
Ben Root



On Thu, Mar 27, 2014 at 4:08 PM, <cl...@br...> wrote:

> Dear Ben,
>
> I've also repeated the install using pip unistall and install of
> matplotlib, both completed successfully but the issue remains, no graphical
> display at the RedHat Linux, as well as very fast and silent exit from the
> program code.
>
>
> *The source used for my test is the 3D Scatter Sample form the Gallery:*
>
> from mpl_toolkits.mplot3d import Axes3D
> import numpy as np
> import matplotlib.pyplot as plt
>
> fig = plt.figure()
> ax = fig.gca(projection='3d')
>
> x = np.linspace(0, 1, 100)
> y = np.sin(x * 2 * np.pi) / 2 + 0.5
> ax.plot(x, y, zs=0, zdir='z', label='zs=0, zdir=z')
>
> colors = ('r', 'g', 'b', 'k')
> for c in colors:
>     x = np.random.sample(20)
>     y = np.random.sample(20)
>     ax.scatter(x, y, 0, zdir='y', c=c)
>
> ax.legend()
> ax.set_xlim3d(0, 1)
> ax.set_ylim3d(0, 1)
> ax.set_zlim3d(0, 1)
>
> plt.show()
>
>
> *The maplotlibrc file: *
>
> ### MATPLOTLIBRC FORMAT
>
> # This is a sample matplotlib configuration file - you can find a copy
> # of it on your system in
> # site-packages/matplotlib/mpl-data/matplotlibrc.  If you edit it
> # there, please note that it will be overwritten in your next install.
> # If you want to keep a permanent local copy that will not be
> # overwritten, place it in HOME/.matplotlib/matplotlibrc (unix/linux
> # like systems) and C:\Documents and Settings\yourname\.matplotlib
> # (win32 systems).
> #
> # This file is best viewed in a editor which supports python mode
> # syntax highlighting. Blank lines, or lines starting with a comment
> # symbol, are ignored, as are trailing comments.  Other lines must
> # have the format
> #    key : val # optional comment
> #
> # Colors: for the color values below, you can either use - a
> # matplotlib color string, such as r, k, or b - an rgb tuple, such as
> # (1.0, 0.5, 0.0) - a hex string, such as ff00ff or #ff00ff - a scalar
> # grayscale intensity such as 0.75 - a legal html color name, eg red,
> # blue, darkslategray
>
> #### CONFIGURATION BEGINS HERE
>
> # the default backend; one of GTK GTKAgg GTKCairo GTK3Agg GTK3Cairo
> # CocoaAgg MacOSX Qt4Agg TkAgg WX WXAgg Agg Cairo GDK PS PDF SVG
> # Template
> # You can also deploy your own backend outside of matplotlib by
> # referring to the module name (which must be in the PYTHONPATH) as
> # 'module://my_backend'
> backend      : agg
>
> # If you are using the Qt4Agg backend, you can choose here
> # to use the PyQt4 bindings or the newer PySide bindings to
> # the underlying Qt4 toolkit.
> #backend.qt4 : PyQt4        # PyQt4 | PySide
>
> # Note that this can be overridden by the environment variable
> # QT_API used by Enthought Tool Suite (ETS); valid values are
> # "pyqt" and "pyside".  The "pyqt" setting has the side effect of
> # forcing the use of Version 2 API for QString and QVariant.
>
> # The port to use for the web server in the WebAgg backend.
> # webagg.port : 8888
>
> # If webagg.port is unavailable, a number of other random ports will
> # be tried until one that is available is found.
> # webagg.port_retries : 50
>
> # When True, open the webbrowser to the plot that is shown
> # webagg.open_in_browser : True
>
> # if you are running pyplot inside a GUI and your backend choice
> # conflicts, we will automatically try to find a compatible one for
> # you if backend_fallback is True
> #backend_fallback: True
>
> #interactive  : False
> #toolbar      : toolbar2   # None | toolbar2  ("classic" is deprecated)
> #timezone     : UTC        # a pytz timezone string, eg US/Central or
> Europe/Paris
>
> # Where your matplotlib data lives if you installed to a non-default
> # location.  This is where the matplotlib fonts, bitmaps, etc reside
> #datapath : /home/jdhunter/mpldata
>
>
> ### LINES
> # See http://matplotlib.org/api/artist_api.html#module-matplotlib.linesfor more
> # information on line properties.
> #lines.linewidth   : 1.0     # line width in points
> #lines.linestyle   : -       # solid line
> #lines.color       : blue    # has no affect on plot(); see
> axes.color_cycle
> #lines.marker      : None    # the default marker
> #lines.markeredgewidth  : 0.5     # the line width around the marker symbol
> #lines.markersize  : 6            # markersize, in points
> #lines.dash_joinstyle : miter        # miter|round|bevel
> #lines.dash_capstyle : butt          # butt|round|projecting
> #lines.solid_joinstyle : miter       # miter|round|bevel
> #lines.solid_capstyle : projecting   # butt|round|projecting
> #lines.antialiased : True         # render lines in antialised (no jaggies)
>
> ### PATCHES
> # Patches are graphical objects that fill 2D space, like polygons or
> # circles.  See
> # http://matplotlib.org/api/artist_api.html#module-matplotlib.patches
> # information on patch properties
> #patch.linewidth        : 1.0     # edge width in points
> #patch.facecolor        : blue
> #patch.edgecolor        : black
> #patch.antialiased      : True    # render patches in antialised (no
> jaggies)
>
> ### FONT
> #
> # font properties used by text.Text.  See
> # http://matplotlib.org/api/font_manager_api.html for more
> # information on font properties.  The 6 font properties used for font
> # matching are given below with their default values.
> #
> # The font.family property has five values: 'serif' (e.g., Times),
> # 'sans-serif' (e.g., Helvetica), 'cursive' (e.g., Zapf-Chancery),
> # 'fantasy' (e.g., Western), and 'monospace' (e.g., Courier).  Each of
> # these font families has a default list of font names in decreasing
> # order of priority associated with them.  When text.usetex is False,
> # font.family may also be one or more concrete font names.
> #
> # The font.style property has three values: normal (or roman), italic
> # or oblique.  The oblique style will be used for italic, if it is not
> # present.
> #
> # The font.variant property has two values: normal or small-caps.  For
> # TrueType fonts, which are scalable fonts, small-caps is equivalent
> # to using a font size of 'smaller', or about 83% of the current font
> # size.
> #
> # The font.weight property has effectively 13 values: normal, bold,
> # bolder, lighter, 100, 200, 300, ..., 900.  Normal is the same as
> # 400, and bold is 700.  bolder and lighter are relative values with
> # respect to the current weight.
> #
> # The font.stretch property has 11 values: ultra-condensed,
> # extra-condensed, condensed, semi-condensed, normal, semi-expanded,
> # expanded, extra-expanded, ultra-expanded, wider, and narrower.  This
> # property is not currently implemented.
> #
> # The font.size property is the default font size for text, given in pts.
> # 12pt is the standard value.
> #
> #font.family         : sans-serif
> #font.style          : normal
> #font.variant        : normal
> #font.weight         : medium
> #font.stretch        : normal
> # note that font.size controls default text sizes.  To configure
> # special text sizes tick labels, axes, labels, title, etc, see the rc
> # settings for axes and ticks. Special text sizes can be defined
> # relative to font.size, using the following values: xx-small, x-small,
> # small, medium, large, x-large, xx-large, larger, or smaller
> #font.size           : 12.0
> #font.serif          : Bitstream Vera Serif, New Century Schoolbook,
> Century Schoolbook L, Utopia, ITC Bookman, Bookman, Nimbus Roman No9 L,
> Times New Roman, Times, Palatino, Charter, serif
> #font.sans-serif     : Bitstream Vera Sans, Lucida Grande, Verdana,
> Geneva, Lucid, Arial, Helvetica, Avant Garde, sans-serif
> #font.cursive        : Apple Chancery, Textile, Zapf Chancery, Sand,
> cursive
> #font.fantasy        : Comic Sans MS, Chicago, Charcoal, Impact, Western,
> fantasy
> #font.monospace      : Bitstream Vera Sans Mono, Andale Mono, Nimbus Mono
> L, Courier New, Courier, Fixed, Terminal, monospace
>
> ### TEXT
> # text properties used by text.Text.  See
> # http://matplotlib.org/api/artist_api.html#module-matplotlib.text for
> more
> # information on text properties
>
> #text.color          : black
>
> ### LaTeX customizations. See
> http://www.scipy.org/Wiki/Cookbook/Matplotlib/UsingTex
> #text.usetex         : False  # use latex for all text handling. The
> following fonts
>                               # are supported through the usual rc
> parameter settings:
>                               # new century schoolbook, bookman, times,
> palatino,
>                               # zapf chancery, charter, serif, sans-serif,
> helvetica,
>                               # avant garde, courier, monospace, computer
> modern roman,
>                               # computer modern sans serif, computer
> modern typewriter
>                               # If another font is desired which can
> loaded using the
>                               # LaTeX \usepackage command, please inquire
> at the
>                               # matplotlib mailing list
> #text.latex.unicode : False # use "ucs" and "inputenc" LaTeX packages for
> handling
>                             # unicode strings.
> #text.latex.preamble :  # IMPROPER USE OF THIS FEATURE WILL LEAD TO LATEX
> FAILURES
>                             # AND IS THEREFORE UNSUPPORTED. PLEASE DO NOT
> ASK FOR HELP
>                             # IF THIS FEATURE DOES NOT DO WHAT YOU EXPECT
> IT TO.
>                             # preamble is a comma separated list of LaTeX
> statements
>                             # that are included in the LaTeX document
> preamble.
>                             # An example:
>                             # text.latex.preamble :
> \usepackage{bm},\usepackage{euler}
>                             # The following packages are always loaded
> with usetex, so
>                             # beware of package collisions: color,
> geometry, graphicx,
>                             # type1cm, textcomp. Adobe Postscript (PSSNFS)
> font packages
>                             # may also be loaded, depending on your font
> settings
>
> #text.dvipnghack : None      # some versions of dvipng don't handle alpha
>                              # channel properly.  Use True to correct
>                              # and flush ~/.matplotlib/tex.cache
>                              # before testing and False to force
>                              # correction off.  None will try and
>                              # guess based on your dvipng version
>
> #text.hinting : 'auto' # May be one of the following:
>                        #   'none': Perform no hinting
>                        #   'auto': Use freetype's autohinter
>                        #   'native': Use the hinting information in the
>                        #             font file, if available, and if your
>                        #             freetype library supports it
>                        #   'either': Use the native hinting information,
>                        #             or the autohinter if none is
> available.
>                        # For backward compatibility, this value may also be
>                        # True === 'auto' or False === 'none'.
> #text.hinting_factor : 8 # Specifies the amount of softness for hinting in
> the
>                          # horizontal direction.  A value of 1 will hint
> to full
>                          # pixels.  A value of 2 will hint to half pixels
> etc.
>
> #text.antialiased : True # If True (default), the text will be antialiased.
>                          # This only affects the Agg backend.
>
> # The following settings allow you to select the fonts in math mode.
> # They map from a TeX font name to a fontconfig font pattern.
> # These settings are only used if mathtext.fontset is 'custom'.
> # Note that this "custom" mode is unsupported and may go away in the
> # future.
> #mathtext.cal : cursive
> #mathtext.rm  : serif
> #mathtext.tt  : monospace
> #mathtext.it  : serif:italic
> #mathtext.bf  : serif:bold
> #mathtext.sf  : sans
> #mathtext.fontset : cm # Should be 'cm' (Computer Modern), 'stix',
>                        # 'stixsans' or 'custom'
> #mathtext.fallback_to_cm : True  # When True, use symbols from the
> Computer Modern
>                                  # fonts when a symbol can not be found in
> one of
>                                  # the custom math fonts.
>
> #mathtext.default : it # The default font to use for math.
>                        # Can be any of the LaTeX font names, including
>                        # the special name "regular" for the same font
>                        # used in regular text.
>
> ### AXES
> # default face and edge color, default tick sizes,
> # default fontsizes for ticklabels, and so on.  See
> # http://matplotlib.org/api/axes_api.html#module-matplotlib.axes
> #axes.hold           : True    # whether to clear the axes by default on
> #axes.facecolor      : white   # axes background color
> #axes.edgecolor      : black   # axes edge color
> #axes.linewidth      : 1.0     # edge linewidth
> #axes.grid           : False   # display grid or not
> #axes.titlesize      : large   # fontsize of the axes title
> #axes.labelsize      : medium  # fontsize of the x any y labels
> #axes.labelweight    : normal  # weight of the x and y labels
> #axes.labelcolor     : black
> #axes.axisbelow      : False   # whether axis gridlines and ticks are below
>                               # the axes elements (lines, text, etc)
> #axes.formatter.limits : -7, 7 # use scientific notation if log10
>                                # of the axis range is smaller than the
>                                # first or larger than the second
> #axes.formatter.use_locale : False # When True, format tick labels
>                                    # according to the user's locale.
>                                    # For example, use ',' as a decimal
>                                    # separator in the fr_FR locale.
> #axes.formatter.use_mathtext : False # When True, use mathtext for
> scientific
>                                      # notation.
> #axes.unicode_minus  : True    # use unicode for the minus symbol
>                                # rather than hyphen.  See
>                                #
> http://en.wikipedia.org/wiki/Plus_and_minus_signs#Character_codes
> #axes.color_cycle    : b, g, r, c, m, y, k  # color cycle for plot lines
>                                             # as list of string colorspecs:
>                                             # single letter, long name, or
>                                             # web-style hex
> #axes.xmargin        : 0  # x margin.  See `axes.Axes.margins`
> #axes.ymargin        : 0  # y margin See `axes.Axes.margins`
>
> #polaraxes.grid      : True    # display grid on polar axes
> #axes3d.grid         : True    # display grid on 3d axes
>
> ### TICKS
> # see http://matplotlib.org/api/axis_api.html#matplotlib.axis.Tick
> #xtick.major.size     : 4      # major tick size in points
> #xtick.minor.size     : 2      # minor tick size in points
> #xtick.major.width    : 0.5    # major tick width in points
> #xtick.minor.width    : 0.5    # minor tick width in points
> #xtick.major.pad      : 4      # distance to major tick label in points
> #xtick.minor.pad      : 4      # distance to the minor tick label in points
> #xtick.color          : k      # color of the tick labels
> #xtick.labelsize      : medium # fontsize of the tick labels
> #xtick.direction      : in     # direction: in, out, or inout
>
> #ytick.major.size     : 4      # major tick size in points
> #ytick.minor.size     : 2      # minor tick size in points
> #ytick.major.width    : 0.5    # major tick width in points
> #ytick.minor.width    : 0.5    # minor tick width in points
> #ytick.major.pad      : 4      # distance to major tick label in points
> #ytick.minor.pad      : 4      # distance to the minor tick label in points
> #ytick.color          : k      # color of the tick labels
> #ytick.labelsize      : medium # fontsize of the tick labels
> #ytick.direction      : in     # direction: in, out, or inout
>
>
> ### GRIDS
> #grid.color       :   black   # grid color
> #grid.linestyle   :   :       # dotted
> #grid.linewidth   :   0.5     # in points
> #grid.alpha       :   1.0     # transparency, between 0.0 and 1.0
>
> ### Legend
> #legend.fancybox      : False  # if True, use a rounded box for the
>                                # legend, else a rectangle
> #legend.isaxes        : True
> #legend.numpoints     : 2      # the number of points in the legend line
> #legend.fontsize      : large
> #legend.borderpad     : 0.5    # border whitespace in fontsize units
> #legend.markerscale   : 1.0    # the relative size of legend markers vs.
> original
> # the following dimensions are in axes coords
> #legend.labelspacing  : 0.5    # the vertical space between the legend
> entries in fraction of fontsize
> #legend.handlelength  : 2.     # the length of the legend lines in
> fraction of fontsize
> #legend.handleheight  : 0.7     # the height of the legend handle in
> fraction of fontsize
> #legend.handletextpad : 0.8    # the space between the legend line and
> legend text in fraction of fontsize
> #legend.borderaxespad : 0.5   # the border between the axes and legend
> edge in fraction of fontsize
> #legend.columnspacing : 2.    # the border between the axes and legend
> edge in fraction of fontsize
> #legend.shadow        : False
> #legend.frameon       : True   # whether or not to draw a frame around
> legend
> #legend.scatterpoints : 3 # number of scatter points
>
> ### FIGURE
> # See http://matplotlib.org/api/figure_api.html#matplotlib.figure.Figure
> #figure.figsize   : 8, 6    # figure size in inches
> #figure.dpi       : 80      # figure dots per inch
> #figure.facecolor : 0.75    # figure facecolor; 0.75 is scalar gray
> #figure.edgecolor : white   # figure edgecolor
> #figure.autolayout : False  # When True, automatically adjust subplot
>                             # parameters to make the plot fit the figure
> #figure.max_open_warning : 20  # The maximum number of figures to open
> through
>                                # the pyplot interface before emitting a
> warning.
>                                # If less than one this feature is disabled.
>
> # The figure subplot parameters.  All dimensions are a fraction of the
> # figure width or height
> #figure.subplot.left    : 0.125  # the left side of the subplots of the
> figure
> #figure.subplot.right   : 0.9    # the right side of the subplots of the
> figure
> #figure.subplot.bottom  : 0.1    # the bottom of the subplots of the figure
> #figure.subplot.top     : 0.9    # the top of the subplots of the figure
> #figure.subplot.wspace  : 0.2    # the amount of width reserved for blank
> space between subplots
> #figure.subplot.hspace  : 0.2    # the amount of height reserved for white
> space between subplots
>
> ### IMAGES
> #image.aspect : equal             # equal | auto | a number
> #image.interpolation  : bilinear  # see help(imshow) for options
> #image.cmap   : jet               # gray | jet etc...
> #image.lut    : 256               # the size of the colormap lookup table
> #image.origin : upper             # lower | upper
> #image.resample  : False
>
> ### CONTOUR PLOTS
> #contour.negative_linestyle :  dashed # dashed | solid
>
> ### Agg rendering
> ### Warning: experimental, 2008/10/10
> #agg.path.chunksize : 0           # 0 to disable; values in the range
>                                   # 10000 to 100000 can improve speed
> slightly
>                                   # and prevent an Agg rendering failure
>                                   # when plotting very large data sets,
>                                   # especially if they are very gappy.
>                                   # It may cause minor artifacts, though.
>                                   # A value of 20000 is probably a good
>                                   # starting point.
> ### SAVING FIGURES
> #path.simplify : True   # When True, simplify paths by removing "invisible"
>                         # points to reduce file size and increase rendering
>                         # speed
> #path.simplify_threshold : 0.1  # The threshold of similarity below which
>                                 # vertices will be removed in the
> simplification
>                                 # process
> #path.snap : True # When True, rectilinear axis-aligned paths will be
> snapped to
>                   # the nearest pixel when certain criteria are met.  When
> False,
>                   # paths will never be snapped.
> #path.sketch : None # May be none, or a 3-tuple of the form (scale, length,
>                     # randomness).
>                     # *scale* is the amplitude of the wiggle
>                     # perpendicular to the line (in pixels).  *length*
>                     # is the length of the wiggle along the line (in
>                     # pixels).  *randomness* is the factor by which
>                     # the length is randomly scaled.
>
> # the default savefig params can be different from the display params
> # e.g., you may want a higher resolution, or to make the figure
> # background white
> #savefig.dpi         : 100      # figure dots per inch
> #savefig.facecolor   : white    # figure facecolor when saving
> #savefig.edgecolor   : white    # figure edgecolor when saving
> #savefig.format      : png      # png, ps, pdf, svg
> #savefig.bbox        : standard # 'tight' or 'standard'.
> #savefig.pad_inches  : 0.1      # Padding to be used when bbox is set to
> 'tight'
> #savefig.jpeg_quality: 95       # when a jpeg is saved, the default
> quality parameter.
> #savefig.directory   : ~        # default directory in savefig dialog box,
>                                 # leave empty to always use current
> working directory
>
> # tk backend params
> #tk.window_focus   : False    # Maintain shell focus for TkAgg
>
> # ps backend params
> #ps.papersize      : letter   # auto, letter, legal, ledger, A0-A10, B0-B10
> #ps.useafm         : False    # use of afm fonts, results in small files
> #ps.usedistiller   : False    # can be: None, ghostscript or xpdf
>                                           # Experimental: may produce
> smaller files.
>                                           # xpdf intended for production
> of publication quality files,
>                                           # but requires ghostscript, xpdf
> and ps2eps
> #ps.distiller.res  : 6000      # dpi
> #ps.fonttype       : 3         # Output Type 3 (Type3) or Type 42
> (TrueType)
>
> # pdf backend params
> #pdf.compression   : 6 # integer from 0 to 9
>                        # 0 disables compression (good for debugging)
> #pdf.fonttype       : 3         # Output Type 3 (Type3) or Type 42
> (TrueType)
>
> # svg backend params
> #svg.image_inline : True       # write raster image data directly into the
> svg file
> #svg.image_noscale : False     # suppress scaling of raster data embedded
> in SVG
> #svg.fonttype : 'path'         # How to handle SVG fonts:
> #    'none': Assume fonts are installed on the machine where the SVG will
> be viewed.
> #    'path': Embed characters as paths -- supported by most SVG renderers
> #    'svgfont': Embed characters as SVG fonts -- supported only by Chrome,
> #               Opera and Safari
>
> # docstring params
> #docstring.hardcopy = False  # set this when you want to generate hardcopy
> docstring
>
> # Set the verbose flags.  This controls how much information
> # matplotlib gives you at runtime and where it goes.  The verbosity
> # levels are: silent, helpful, debug, debug-annoying.  Any level is
> # inclusive of all the levels below it.  If your setting is "debug",
> # you'll get all the debug and helpful messages.  When submitting
> # problems to the mailing-list, please set verbose to "helpful" or "debug"
> # and paste the output into your report.
> #
> # The "fileo" gives the destination for any calls to verbose.report.
> # These objects can a filename, or a filehandle like sys.stdout.
> #
> # You can override the rc default verbosity from the command line by
> # giving the flags --verbose-LEVEL where LEVEL is one of the legal
> # levels, eg --verbose-helpful.
> #
> # You can access the verbose instance in your code
> #   from matplotlib import verbose.
> #verbose.level  : silent      # one of silent, helpful, debug,
> debug-annoying
> #verbose.fileo  : sys.stdout  # a log filename, sys.stdout or sys.stderr
>
> # Event keys to interact with figures/plots via keyboard.
> # Customize these settings according to your needs.
> # Leave the field(s) empty if you don't need a key-map. (i.e., fullscreen
> : '')
>
> #keymap.fullscreen : f               # toggling
> #keymap.home : h, r, home            # home or reset mnemonic
> #keymap.back : left, c, backspace    # forward / backward keys to enable
> #keymap.forward : right, v           #   left handed quick navigation
> #keymap.pan : p                      # pan mnemonic
> #keymap.zoom : o                     # zoom mnemonic
> #keymap.save : s                     # saving current figure
> #keymap.quit : ctrl+w, cmd+w         # close the current figure
> #keymap.grid : g                     # switching on/off a grid in current
> axes
> #keymap.yscale : l                   # toggle scaling of y-axes
> ('log'/'linear')
> #keymap.xscale : L, k                # toggle scaling of x-axes
> ('log'/'linear')
> #keymap.all_axes : a                 # enable all axes
>
> # Control location of examples data files
> #examples.directory : ''   # directory to look in for custom installation
>
> ###ANIMATION settings
> #animation.writer : ffmpeg         # MovieWriter 'backend' to use
> #animation.codec : mp4             # Codec to use for writing movie
> #animation.bitrate: -1             # Controls size/quality tradeoff for
> movie.
>                                    # -1 implies let utility auto-determine
> #animation.frame_format: 'png'     # Controls frame format used by temp
> files
> #animation.ffmpeg_path: 'ffmpeg'   # Path to ffmpeg binary. Without full
> path
>                                    # $PATH is searched
> #animation.ffmpeg_args: ''         # Additional arguments to pass to ffmpeg
> #animation.avconv_path: 'avconv'   # Path to avconv binary. Without full
> path
>                                    # $PATH is searched
> #animation.avconv_args: ''         # Additional arguments to pass to avconv
> #animation.mencoder_path: 'mencoder'
>                                    # Path to mencoder binary. Without full
> path
>                                    # $PATH is searched
> #animation.mencoder_args: ''       # Additional arguments to pass to
> mencoder
>
> Hope this helps to isolate the error.
>
> Regards,
> Claude
>
>
>
>
>
>
>
>
> * Claude Falbriard Certified IT Specialist L2 - Middleware AMS Hortolândia
> / SP - Brazil phone:    +55 13 9 9760 0453 <%2B55%2013%209%209760%200453>
> cell:         +55 13 9 8117 3316 <%2B55%2013%209%208117%203316> e-mail:
>  cl...@br... <cl...@br...> *
>
>
>
> From:        Benjamin Root <ben...@ou...>
> To:        falbriard <cl...@br...>, Matplotlib Users <
> mat...@li...>,
> Date:        27/03/2014 16:20
> Subject:        Re: [Matplotlib-users] RedHat and Release Upgrade to
> Numpy 1.8.1 and Matplotlib 1.3.1 / Install from Source
> Sent by:        ben...@gm...
> ------------------------------
>
>
>
> Claude, it would be helpful to know exactly what code you executed. Some
> example code assumes interactive modes, while others simply save files
> without ever showing them to the screen.
>
> Also, please include a copy of your matplotlibrc file.
>
> Ben Root
>
>
>
> On Thu, Mar 27, 2014 at 1:47 PM, <*cl...@br...*<cl...@br...>>
> wrote:
> Dear Ben,
>
> The execution of any of the Matplotlib sample code start quickly and and
> exits immediately with no error message displayed  at the screen. The
> process runs instantly, so there is no wait in the process.
> Looks more like a missing setup option, matplotlib does not find a valid
> graphical screen display environment. What do you think is causing this
> error in RedHat Linux?
>
> Regards,
> Claude
>
>
>
>
> * Claude Falbriard Certified IT Specialist L2 - Middleware AMS Hortolândia
> / SP - Brazil phone:    **+55 13 9 9760 0453*<%2B55%2013%209%209760%200453>
> * cell:         **+55 13 9 8117 3316* <%2B55%2013%209%208117%203316>
> * e-mail:    **cl...@br...* <cl...@br...>
>
>
>
> From:        Benjamin Root <*ben...@ou...* <ben...@ou...>>
> To:        falbriard <*cl...@br...* <cl...@br...>>,
> Cc:        Matplotlib Users <*mat...@li...*<mat...@li...>
> >
> Date:        27/03/2014 14:32
> Subject:        Re: [Matplotlib-users] RedHat and Release Upgrade to
> Numpy 1.8.1 and Matplotlib 1.3.1 / Install from Source
> Sent by:        *ben...@gm...* <ben...@gm...>
>  ------------------------------
>
>
>
>
> How long did you wait? Do allow approximately one minute for the first
> execution to allow for the font.cache to be built. It can appear that the
> process has "hung" because it is waiting for "fc-list" subprocess to
> complete.
>
> Cheers!
> Ben Root
>
>
>
> ------------------------------------------------------------------------------
>
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>
>