matplotlib-users Mailing List for matplotlib (Page 3)

Use the `pad` property which sets how far the tick labels are from the axis.
 ax1.tick_params(axis='x',which='minor',bottom='off',top='off', pad=25)
iirc, the units on pad are font-points
Tom
On Fri, Aug 15, 2014 at 5:21 PM, Sterling Smith <sm...@fu...> wrote:
> How about prepending '\n' to your minor labels?
>
> On Aug 15, 2014, at 1:38PM, Ted To wrote:
>
>> Hi,
>>
>> I'm trying to set two lines of xtick_labels But I can't figure out how
>> to get them on separate lines. These are for errorbars where I have two
>> variables for each of four categories. Using the following code, I'm
>> able to create the attached figure. How does one move the "minor"
>> xtick_labels to a 2nd line? A minor problem that maybe someone knows
>> the answer to is, how do I increase the width of the x axis so that
>> there is some space before and after the first and last ticks?
>> (ax1.set_xlim(-.5,7.5) does not seem to work.)
>>
>> Many thanks,
>> Ted
>>
>> fig,ax1=plt.subplots()
>> ax1.errorbar(wageAllTypes.index,wageAllTypes['mean'],yerr=wageAllTypes['sd'],fmt='bo')
>> xticks=[0,1,2,3,4,5,6,7]
>> xticks_minor=[.5,2.5,4.5,6.5]
>> ax1.set_xticks(xticks)
>> ax1.set_xticks(xticks_minor, minor=True)
>> ax1.set_xticklabels(['All Jobs','Job 1','Job 2','Job 3'],minor=True)
>> ax1.set_xticklabels([r'$w$',r'$\xi$',r'$w$',r'$\xi$',r'$w$',r'$\xi$',r'$w$',r'$\xi$]')
>>
>> ax1.tick_params(axis='x',which='major')
>> ax1.tick_params(axis='x',which='minor',bottom='off',top='off')
>> ax1.set_ylabel(r'$\ln w$',ha='right',rotation='horizontal')
>>
>> ax2 = ax1.twinx()
>> ax2.errorbar(xiAllTypes.index,xiAllTypes['mean'],yerr=xiAllTypes['sd'],fmt='ro')
>> ax2.set_ylabel(r'$\xi$',ha='left',rotation='horizontal')
>> <figure_1.png>------------------------------------------------------------------------------
>> _______________________________________________
>> Matplotlib-users mailing list
>> Mat...@li...
>> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>
>
> ------------------------------------------------------------------------------
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
-- 
Thomas Caswell
tca...@gm...

How about prepending '\n' to your minor labels?
On Aug 15, 2014, at 1:38PM, Ted To wrote:
> Hi,
> 
> I'm trying to set two lines of xtick_labels But I can't figure out how
> to get them on separate lines. These are for errorbars where I have two
> variables for each of four categories. Using the following code, I'm
> able to create the attached figure. How does one move the "minor"
> xtick_labels to a 2nd line? A minor problem that maybe someone knows
> the answer to is, how do I increase the width of the x axis so that
> there is some space before and after the first and last ticks?
> (ax1.set_xlim(-.5,7.5) does not seem to work.)
> 
> Many thanks,
> Ted
> 
> fig,ax1=plt.subplots()
> ax1.errorbar(wageAllTypes.index,wageAllTypes['mean'],yerr=wageAllTypes['sd'],fmt='bo')
> xticks=[0,1,2,3,4,5,6,7]
> xticks_minor=[.5,2.5,4.5,6.5]
> ax1.set_xticks(xticks)
> ax1.set_xticks(xticks_minor, minor=True)
> ax1.set_xticklabels(['All Jobs','Job 1','Job 2','Job 3'],minor=True)
> ax1.set_xticklabels([r'$w$',r'$\xi$',r'$w$',r'$\xi$',r'$w$',r'$\xi$',r'$w$',r'$\xi$]')
> 
> ax1.tick_params(axis='x',which='major')
> ax1.tick_params(axis='x',which='minor',bottom='off',top='off')
> ax1.set_ylabel(r'$\ln w$',ha='right',rotation='horizontal')
> 
> ax2 = ax1.twinx()
> ax2.errorbar(xiAllTypes.index,xiAllTypes['mean'],yerr=xiAllTypes['sd'],fmt='ro')
> ax2.set_ylabel(r'$\xi$',ha='left',rotation='horizontal')
> <figure_1.png>------------------------------------------------------------------------------
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users

Hi,
I'm trying to set two lines of xtick_labels But I can't figure out how
to get them on separate lines. These are for errorbars where I have two
variables for each of four categories. Using the following code, I'm
able to create the attached figure. How does one move the "minor"
xtick_labels to a 2nd line? A minor problem that maybe someone knows
the answer to is, how do I increase the width of the x axis so that
there is some space before and after the first and last ticks?
(ax1.set_xlim(-.5,7.5) does not seem to work.)
Many thanks,
Ted
fig,ax1=plt.subplots()
ax1.errorbar(wageAllTypes.index,wageAllTypes['mean'],yerr=wageAllTypes['sd'],fmt='bo')
xticks=[0,1,2,3,4,5,6,7]
xticks_minor=[.5,2.5,4.5,6.5]
ax1.set_xticks(xticks)
ax1.set_xticks(xticks_minor, minor=True)
ax1.set_xticklabels(['All Jobs','Job 1','Job 2','Job 3'],minor=True)
ax1.set_xticklabels([r'$w$',r'$\xi$',r'$w$',r'$\xi$',r'$w$',r'$\xi$',r'$w$',r'$\xi$]')
ax1.tick_params(axis='x',which='major')
ax1.tick_params(axis='x',which='minor',bottom='off',top='off')
ax1.set_ylabel(r'$\ln w$',ha='right',rotation='horizontal')
ax2 = ax1.twinx()
ax2.errorbar(xiAllTypes.index,xiAllTypes['mean'],yerr=xiAllTypes['sd'],fmt='ro')
ax2.set_ylabel(r'$\xi$',ha='left',rotation='horizontal')

My problem is that I have a curvilinear grid with some velocity data and I'm
trying to so streamplot
of it. I read at documentation that streamplot do not plot uneven grids and
so I googled it and found this website: 
http://www.flannaghan.com/2013/02/23/interpolation
<http://www.flannaghan.com/2013/02/23/interpolation> that someone solved
the uneven problem with interpolation, but my problem is a quite harder. My
grid is not just uneven, it is curvilinear. This is really important to me
and I would be very thankful for any help.
The example code and the result:
import numpy as np
import matplotlib.pyplot as plt
xx = np.array([[-32.77352506, -32.50517324, -32.30341846, -32.12060867,
-31.99103968],
 [-32.88670112, -32.63078693, -32.42892793, -32.2527705 , -32.11911059],
 [-32.99884749, -32.75419286, -32.55377179, -32.38220417,-32.24664094],
 [-33.10888993, -32.87495033, -32.67707405, -32.50885765,-32.37311971],
 [-33.2179889 , -32.99317728, -32.79848554, -32.63304009,-32.49815164],
 [-33.32651265, -33.10917094, -32.91791567, -32.75496914,-32.62146004],
 [-33.43449219, -33.22321261, -33.03537769, -32.87477271,-32.74286757],
 [-33.54184645, -33.33551502, -33.15092328, -32.99252837,-32.86227065],
 [-33.64847256, -33.44622558, -33.26461466, -33.1082882 ,-32.97961644],
 [-33.7542787 , -33.55544297, -33.37651245, -33.22209182,-33.09488502],
 [-33.85919362, -33.66323316, -33.48667092, -33.33397251,-33.20807667],
 [-33.96316738, -33.76964127, -33.59513666, -33.44395994,-33.31920308],
 [-34.06616899, -33.87469962, -33.70194888, -33.55208114,-33.42828168],
 [-34.16818354, -33.97843289, -33.80714029, -33.6583608 , -33.5353318 ],
 [-34.26920936, -34.08086103, -33.91073804, -33.76282129,-33.64037232],
 [-34.36925566, -34.18200076, -34.01276449, -33.86548266,-33.74342012],
 [-34.46834041, -34.28186594, -34.11323791, -33.96636282,-33.84448915],
 [-34.56648851, -34.38046701, -34.21217281, -34.06547784,-33.94358994],
 [-34.66372971, -34.4778096 , -34.30958029, -34.16284263, -34.0407292 ],
 [-34.76009619, -34.57389229, -34.40546833, -34.2584719 ,-34.13590974],
 [-34.85561889, -34.66870332, -34.49984222, -34.35238178,-34.22913045],
 [-34.95032165, -34.76221629, -34.59270529, -34.44459209,-34.32038648],
 [-35.04421102, -34.85438466, -34.6840602 , -34.5351297 ,-34.40966975],
 [-35.13725786, -34.94513595, -34.77391107, -34.62403315,-34.49697031],
 [-35.22936336, -35.03436723, -34.86226647, -34.7113589 , -34.5822793 ],
 [-35.3202956 , -35.12194712, -34.9491432 , -34.79718943,-34.66559567],
 [-35.40957149, -35.20773648, -35.03457067, -34.88164276,-34.74693998],
 [-35.49624617, -35.29165455, -35.11859869, -34.96488179,-34.82637986],
 [-35.57858891, -35.3738341 , -35.20134195, -35.04711944,-34.90406862],
 [-35.65407127, -35.45484625, -35.28327484, -35.12861303,-34.98028311]])
yy = np.array([[-11.3529916 , -10.83017948, -10.36062676, 
-9.85499224,-9.36742115],
 [-11.24914312, -10.77486528, -10.30767657, -9.81790781,-9.33347811],
 [-11.16896123, -10.71827884, -10.25654788, -9.77873607,-9.29985941],
 [-11.09864806, -10.66157581, -10.20688907, -9.7389486 ,-9.26669158],
 [-11.03379175, -10.60554773, -10.15836065, -9.69919353, -9.2340536 ],
 [-10.97234269, -10.55056628, -10.11076275, -9.65973621,-9.20197376],
 [-10.91318741, -10.49672964, -10.06398502, -9.62068888,-9.17044015],
 [-10.85567682, -10.44399733, -10.01795592, -9.58209354,-9.13941538],
 [-10.79941292, -10.39227109, -9.97261586, -9.54395471,-9.10884902],
 [-10.74413933, -10.3414354 , -9.92790608, -9.50625469,-9.07868586],
 [-10.68968138, -10.29137538, -9.88376528, -9.46896173,-9.04887037],
 [-10.63591212, -10.24198327, -9.84012944, -9.43203502,-9.01934878],
 [-10.58273238, -10.19315956, -9.79693259, -9.39542787,-8.99006955],
 [-10.53005851, -10.14481189, -9.75410767, -9.35908973,-8.96098324],
 [-10.47781454, -10.09685326, -9.71158727, -9.32296761,-8.93204187],
 [-10.42592671, -10.04920008, -9.66930413, -9.28700687,-8.90319822],
 [-10.37431922, -10.00177041, -9.62719161, -9.25115173, -8.874405 ],
 [-10.32291042, -9.95448254, -9.5851842 , -9.21534553,-8.84561388],
 [-10.27160885, -9.90725399, -9.54321824, -9.17953081, -8.8167744 ],
 [-10.22030865, -9.86000117, -9.50123304, -9.1436493 ,-8.78783251],
 [-10.16888358, -9.81263985, -9.45917264, -9.10764179,-8.75872859],
 [-10.11717908, -9.7650871 , -9.41698825, -9.07144794,-8.72939466],
 [-10.06500151, -9.71726547, -9.37464171, -9.03500603,-8.69975035],
 [-10.01210356, -9.66911062, -9.33211024, -8.99825263,-8.66969694],
 [ -9.95816574, -9.62058406, -9.28939255, -8.96112225, -8.639109 ],
 [ -9.90277604, -9.57169217, -9.24651752, -8.92354679,-8.60782312],
 [ -9.84541584, -9.52250952, -9.2035585 , -8.88545441,-8.57562469],
 [ -9.78546516, -9.4731919 , -9.1606645 , -8.84676681,-8.54223813],
 [ -9.72217224, -9.42392909, -9.11814094, -8.80739381,-8.50733329],
 [ -9.65407127, -9.37474785, -9.07654968, -8.76722606,-8.47056622]])
u = 3*np.cos(xx)*(-3)*np.sin(yy)
v = 2*np.sin(xx)*3*np.cos(yy)
speed = np.sqrt((u**2)+(v**2))
plt.ion()
fig,(ax1,ax2) = plt.subplots(1,2,figsize=(14,8))
ax1.contourf(xx,yy,speed)
ax1.plot(xx,yy,'-k',alpha=0.3)
ax1.plot(xx.T,yy.T,'-k',alpha=0.3)
ax1.quiver(xx,yy,u,v)
ax2.contourf(xx,yy,speed)
ax2.plot(xx,yy,'-k',alpha=0.3)
ax2.plot(xx.T,yy.T,'-k',alpha=0.3)
ax2.streamplot(xx,yy,u,v)
<http://matplotlib.1069221.n5.nabble.com/file/n43795/error.png> 
--
View this message in context: http://matplotlib.1069221.n5.nabble.com/Streamplot-for-Uneven-Curvilinear-Grid-tp43795.html
Sent from the matplotlib - users mailing list archive at Nabble.com.

Hello,
I build RPMs to distribute matplotlib to our local RedHat 6 machines.
The procedure I use is simply
 python setup.py bdist_rpm
This works fine until I attempt to include gtk support. The problem
is that the bdist_rpm procedure creates a temporary shell script file
which unset's the $DISPLAY variable. This then causes setupext.py's
'import gtk' to fail due to not having $DISPLAY set and the build then
happens without gtk support. If I hard code it in setup.cfg backend
section to True, then the build fails completely (as it should).
Am I missing something obvious to work around this?
daryl

Thanks for your insights, Ian! 
A somewhat slower trifinder which requires less memory might be even faster in the end as creating the trifinder itself takes a lot of time (almost a minute in our case). 
Regards Hartmut
---------------
http://boost-spirit.com
http://stellar.cct.lsu.edu
> -----Original Message-----
> From: Ian Thomas [mailto:ian...@gm...]
> Sent: Tuesday, August 12, 2014 4:35 AM
> To: Hartmut Kaiser
> Cc: Andrew Dawson; Carola Kaiser; matplotlib-users
> Subject: Re: [Matplotlib-users] Crash when using
> matplotlib.tri.LinearTriInterpolator
> 
> Here are the results of my investigation. There is probably more
> information here than anyone else wants, but it is useful information for
> future improvements.
> 
> Most of the RAM is taken up by a trifinder object which is at the heart of
> a triinterpolator, and is used to find the triangles of a Triangulation in
> which (x,y) points lie. The code
> interp = tri.LinearTriInterpolator(triang, data)
> is equivalent to
> trifinder = tri.TrapezoidMapTriFinder(triang)
> interp = tri.LinearTriInterpolator(triang, data, trifinder=trifinder)
> 
> Using the latter with memory_profiler
> (https://pypi.python.org/pypi/memory_profiler) indicates that this is
> where most of the RAM is being used. Here are some figures for trifinder
> RAM usage as a function of ntri, the number of triangles in the
> triangulation:
> 
> ntri trifinder MB
> ---- ------------
> 1000 26
> 10000 33
> 100000 116
> 1000000 912
> 2140255 1936
> 
> The RAM usage is less than linear in ntri, but clearly too much for large
> triangulations unless you have a lot of RAM.
> 
> The trifinder precomputes a tree of nodes to make looking up triangles
> quick. Searching through 2 million triangles in an ad-hoc manner would be
> very slow; the trifinder is very fast in comparison. Here are some stats
> for the tree that trifinder uses (the columns are number of nodes in the
> tree, maximum node depth, and mean node depth):
> ntri nodes max depth mean depth
> ------- --------- --------- ----------
> 1000 179097 37 23.24
> 10000 3271933 53 30.74
> 100000 36971309 69 37.15
> 1000000 853117229 87 48.66
> The mean depth is the mean number of nodes that have to be traversed to
> find a triangle, and the max depth is the worst case. The search time is
> therefore O(log ntri).
> The triangle interpolator code is structured in such a way that it is easy
> to plug in a different trifinder if the default one isn't appropriate. At
> the moment there is only the one available however
> (TrapezoidMapTriFinder). For the problem at hand, a trifinder that is
> slower but consumes less RAM would be preferable. There are various
> possibilities, they just have to be implemented! I will take a look at it
> sometime, but it probably will not be soon.
> Ian Thomas

Here are the results of my investigation. There is probably more
information here than anyone else wants, but it is useful information for
future improvements.
Most of the RAM is taken up by a trifinder object which is at the heart of
a triinterpolator, and is used to find the triangles of a Triangulation in
which (x,y) points lie. The code
 interp = tri.LinearTriInterpolator(triang, data)
is equivalent to
 trifinder = tri.TrapezoidMapTriFinder(triang)
 interp = tri.LinearTriInterpolator(triang, data, trifinder=trifinder)
Using the latter with memory_profiler (
https://pypi.python.org/pypi/memory_profiler) indicates that this is where
most of the RAM is being used. Here are some figures for trifinder RAM
usage as a function of ntri, the number of triangles in the triangulation:
ntri trifinder MB
---- ------------
1000 26
10000 33
100000 116
1000000 912
2140255 1936
The RAM usage is less than linear in ntri, but clearly too much for large
triangulations unless you have a lot of RAM.
The trifinder precomputes a tree of nodes to make looking up triangles
quick. Searching through 2 million triangles in an ad-hoc manner would be
very slow; the trifinder is very fast in comparison. Here are some stats
for the tree that trifinder uses (the columns are number of nodes in the
tree, maximum node depth, and mean node depth):
 ntri nodes max depth mean depth
------- --------- --------- ----------
 1000 179097 37 23.24
 10000 3271933 53 30.74
 100000 36971309 69 37.15
1000000 853117229 87 48.66
The mean depth is the mean number of nodes that have to be traversed to
find a triangle, and the max depth is the worst case. The search time is
therefore O(log ntri).
The triangle interpolator code is structured in such a way that it is easy
to plug in a different trifinder if the default one isn't appropriate. At
the moment there is only the one available however
(TrapezoidMapTriFinder). For the problem at hand, a trifinder that is
slower but consumes less RAM would be preferable. There are various
possibilities, they just have to be implemented! I will take a look at it
sometime, but it probably will not be soon.
Ian Thomas

> > I ran the example on my machine (which is a 64-bit Linux box with 8 GB
> of
> > RAM; Python 2.7, matplotlib 1.3.1) and it runs fine. However, it does
> use
> > around 2 GB of memory, perhaps slightly more. I think the memory usage
> > might be a problem for you if you are using 32-bit Windows. I'm not
> > familiar with the details but I believe the memory available to a single
> > 32-bit process on Win32 may be only 2 GB. I'm also not familiar with the
> > data you provided, but is it possible to reduce to number of points in
> > order to test if memory limitations are the underlying problemhere?
> 
> Nod, your suspicion is correct. The python interpreter bails out once the
> memory footprint reaches 2GBytes. That leaves us with the question if this
> is a quality of implementation issue - using up 2GBytes of main memory for
> 1 million node elements seems to be a bit excessive...
> 
> Thanks everybody for verifying anyways!
Just to round that issue up - I tried running this using Python 2.7 (64Bit) and it does not crash anymore. The memory requirement grows up to almost 4GByte. 
I will verify whether I can get the results I hope for and will report back.
Thanks again!
Regards Hartmut
---------------
http://boost-spirit.com
http://stellar.cct.lsu.edu
> 
> Regards Hartmut
> ---------------
> http://boost-spirit.com
> http://stellar.cct.lsu.edu
> 
> >
> >
> >
> > On 11 August 2014 14:54, Hartmut Kaiser <har...@gm...>
> wrote:
> > Ian,
> >
> > > I'm running into a crash while trying to construct a
> > > tri.LinearTriInterpolator. Here is the short version of the code:
> > >
> > > import netCDF4
> > > import matplotlib.tri as tri
> > >
> > > var = netCDF4.Dataset('filename.cdf').variables
> > > x = var['x'][:]
> > > y = var['y'][:]
> > > data = var['zeta_max'][:]
> > > elems = var['element'][:, :]-1
> > >
> > > triang = tri.Triangulation(x, y, triangles=elems)
> > >
> > > # this crashes the python interpreter
> > > interp = tri.LinearTriInterpolator(triang, data)
> > >
> > > The data arrays (x, y, data, elems) are fairly large (>1 mio
> elements),
> > > all
> > > represented as numpy arrays (as returned by netCDF4). The 'data' array
> > is
> > > a
> > > masked array and contains masked values.
> > >
> > > If somebody cares, I'd be able to post a link to the netCDF data file
> > > causing this.
> > >
> > > All this happens when using matplotlib 1.3.1, Win32, Python 2.7.
> > >
> > > Any help would be highly appreciated!
> > > Regards Hartmut
> > >
> > > Hartmut,
> > > That is an excellent issue report; all the relevant information and
> > > nothing extraneous. Hence the quick response.
> > > The second argument to TriLinearInterpolator (and other
> TriInterpolator
> > > classes), i.e. your 'data' array, is expected to be an array of the
> same
> > > size as the 'x' and 'y' arrays. It is not expecting a masked
> array. If
> > a
> > > masked array is used the mask will be ignored, and so the values
> behind
> > > the mask will be used as though they were real values. If my memory
> of
> > > netCDF is correct, this will be whatever 'FillValue' is defined for
> the
> > > file, but it may depend on what is used to generate the netCDF file.
> > > I would normally expect the code to work but produce useless
> output. A
> > > crash is possible though. It would be best if you could post a link
> to
> > > the netCDF file and I will take a closer look to check there is not
> > > something else going wrong.
> > Thanks for the quick response!
> >
> > Here is the data file: http://tinyurl.com/ms7vzxw. I did some more
> > experiments. The picture stays unchanged, even if I fill the masked
> values
> > in the array with some real numbers (I'm not saying that this would give
> > me any sensible results...):
> >
> > import netCDF4
> > import matplotlib.tri as tri
> > var = netCDF4.Dataset('maxele.63.nc').variables
> > x = var['x'][:]
> > y = var['y'][:]
> > data = var['zeta_max'][:]
> > elems = var['element'][:, :]-1
> >
> > triang = tri.Triangulation(x, y, triangles=elems)
> > data = data.filled(0.0)
> >
> > # this still crashes the python interpreter
> > interp = tri.LinearTriInterpolator(triang, data)
> >
> > Thanks again!
> > Regards Hartmut
> > ---------------
> > http://boost-spirit.com
> > http://stellar.cct.lsu.edu
> >
> >
> >
> > ------------------------------------------------------------------------
> --
> > ----
> > _______________________________________________
> > Matplotlib-users mailing list
> > Mat...@li...
> > https://lists.sourceforge.net/lists/listinfo/matplotlib-users
> >
> >
> >
> >
> > --
> > Dr Andrew Dawson
> > Atmospheric, Oceanic & Planetary Physics
> > Clarendon Laboratory
> > Parks Road
> > Oxford OX1 3PU, UK
> > Tel: +44 (0)1865 282438
> > Email: da...@at...
> > Web Site: http://www2.physics.ox.ac.uk/contacts/people/dawson

Andrew,
> I ran the example on my machine (which is a 64-bit Linux box with 8 GB of
> RAM; Python 2.7, matplotlib 1.3.1) and it runs fine. However, it does use
> around 2 GB of memory, perhaps slightly more. I think the memory usage
> might be a problem for you if you are using 32-bit Windows. I'm not
> familiar with the details but I believe the memory available to a single
> 32-bit process on Win32 may be only 2 GB. I'm also not familiar with the
> data you provided, but is it possible to reduce to number of points in
> order to test if memory limitations are the underlying problemhere?
Nod, your suspicion is correct. The python interpreter bails out once the memory footprint reaches 2GBytes. That leaves us with the question if this is a quality of implementation issue - using up 2GBytes of main memory for 1 million node elements seems to be a bit excessive...
Thanks everybody for verifying anyways!
Regards Hartmut
---------------
http://boost-spirit.com
http://stellar.cct.lsu.edu
> 
> 
> 
> On 11 August 2014 14:54, Hartmut Kaiser <har...@gm...> wrote:
> Ian,
> 
> > I'm running into a crash while trying to construct a
> > tri.LinearTriInterpolator. Here is the short version of the code:
> >
> > import netCDF4
> > import matplotlib.tri as tri
> >
> > var = netCDF4.Dataset('filename.cdf').variables
> > x = var['x'][:]
> > y = var['y'][:]
> > data = var['zeta_max'][:]
> > elems = var['element'][:, :]-1
> >
> > triang = tri.Triangulation(x, y, triangles=elems)
> >
> > # this crashes the python interpreter
> > interp = tri.LinearTriInterpolator(triang, data)
> >
> > The data arrays (x, y, data, elems) are fairly large (>1 mio elements),
> > all
> > represented as numpy arrays (as returned by netCDF4). The 'data' array
> is
> > a
> > masked array and contains masked values.
> >
> > If somebody cares, I'd be able to post a link to the netCDF data file
> > causing this.
> >
> > All this happens when using matplotlib 1.3.1, Win32, Python 2.7.
> >
> > Any help would be highly appreciated!
> > Regards Hartmut
> >
> > Hartmut,
> > That is an excellent issue report; all the relevant information and
> > nothing extraneous. Hence the quick response.
> > The second argument to TriLinearInterpolator (and other TriInterpolator
> > classes), i.e. your 'data' array, is expected to be an array of the same
> > size as the 'x' and 'y' arrays. It is not expecting a masked array. If
> a
> > masked array is used the mask will be ignored, and so the values behind
> > the mask will be used as though they were real values. If my memory of
> > netCDF is correct, this will be whatever 'FillValue' is defined for the
> > file, but it may depend on what is used to generate the netCDF file.
> > I would normally expect the code to work but produce useless output. A
> > crash is possible though. It would be best if you could post a link to
> > the netCDF file and I will take a closer look to check there is not
> > something else going wrong.
> Thanks for the quick response!
> 
> Here is the data file: http://tinyurl.com/ms7vzxw. I did some more
> experiments. The picture stays unchanged, even if I fill the masked values
> in the array with some real numbers (I'm not saying that this would give
> me any sensible results...):
> 
> import netCDF4
> import matplotlib.tri as tri
> var = netCDF4.Dataset('maxele.63.nc').variables
> x = var['x'][:]
> y = var['y'][:]
> data = var['zeta_max'][:]
> elems = var['element'][:, :]-1
> 
> triang = tri.Triangulation(x, y, triangles=elems)
> data = data.filled(0.0)
> 
> # this still crashes the python interpreter
> interp = tri.LinearTriInterpolator(triang, data)
> 
> Thanks again!
> Regards Hartmut
> ---------------
> http://boost-spirit.com
> http://stellar.cct.lsu.edu
> 
> 
> 
> --------------------------------------------------------------------------
> ----
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
> 
> 
> 
> 
> --
> Dr Andrew Dawson
> Atmospheric, Oceanic & Planetary Physics
> Clarendon Laboratory
> Parks Road
> Oxford OX1 3PU, UK
> Tel: +44 (0)1865 282438
> Email: da...@at...
> Web Site: http://www2.physics.ox.ac.uk/contacts/people/dawson

Runs to completion without errors on my installation:
OS X 10.9.4
MacBook Air w/ 8GB of memory
Python 2.7 and matplotlib 1.3.1-1 lib 
-Dale
On Aug 10, 2014, at 13:43 , Hartmut Kaiser <har...@gm...> wrote:
> All,
> 
> I'm running into a crash while trying to construct a
> tri.LinearTriInterpolator. Here is the short version of the code:
> 
> import netCDF4
> import matplotlib.tri as tri
> 
> var = netCDF4.Dataset('filename.cdf').variables
> x = var['x'][:]
> y = var['y'][:]
> data = var['attrname'][:] 
> elems = var['element'][:,:]-1
> 
> triang = tri.Triangulation(x, y, triangles=elems)
> 
> # this crashes the python interpreter
> interp = tri.LinearTriInterpolator(triang, data)
> 
> The data arrays (x, y, data, elems) are fairly large (>1 mio elements), all
> represented as numpy arrays (as returned by netCDF4). The 'data' array is a
> masked array and contains masked values.
> 
> If somebody cares, I'd be able to post a link to the netCDF data file
> causing this.
> 
> All this happens when using matplotlib 1.3.1, Win32, Python 2.7.
> 
> Any help would be highly appreciated!
> Regards Hartmut
> ---------------
> http://boost-spirit.com
> http://stellar.cct.lsu.edu
> 
> 
> 
> ------------------------------------------------------------------------------
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users

Hi Hartmut.
I ran the example on my machine (which is a 64-bit Linux box with 8 GB of
RAM; Python 2.7, matplotlib 1.3.1) and it runs fine. However, it does use
around 2 GB of memory, perhaps slightly more. I think the memory usage
might be a problem for you if you are using 32-bit Windows. I'm not
familiar with the details but I believe the memory available to a single
32-bit process on Win32 may be only 2 GB. I'm also not familiar with the
data you provided, but is it possible to reduce to number of points in
order to test if memory limitations are the underlying problemhere?
On 11 August 2014 14:54, Hartmut Kaiser <har...@gm...> wrote:
> Ian,
>
> > I'm running into a crash while trying to construct a
> > tri.LinearTriInterpolator. Here is the short version of the code:
> >
> > import netCDF4
> > import matplotlib.tri as tri
> >
> > var = netCDF4.Dataset('filename.cdf').variables
> > x = var['x'][:]
> > y = var['y'][:]
> > data = var['zeta_max'][:]
> > elems = var['element'][:, :]-1
> >
> > triang = tri.Triangulation(x, y, triangles=elems)
> >
> > # this crashes the python interpreter
> > interp = tri.LinearTriInterpolator(triang, data)
> >
> > The data arrays (x, y, data, elems) are fairly large (>1 mio elements),
> > all
> > represented as numpy arrays (as returned by netCDF4). The 'data' array is
> > a
> > masked array and contains masked values.
> >
> > If somebody cares, I'd be able to post a link to the netCDF data file
> > causing this.
> >
> > All this happens when using matplotlib 1.3.1, Win32, Python 2.7.
> >
> > Any help would be highly appreciated!
> > Regards Hartmut
> >
> > Hartmut,
> > That is an excellent issue report; all the relevant information and
> > nothing extraneous. Hence the quick response.
> > The second argument to TriLinearInterpolator (and other TriInterpolator
> > classes), i.e. your 'data' array, is expected to be an array of the same
> > size as the 'x' and 'y' arrays. It is not expecting a masked array. If
> a
> > masked array is used the mask will be ignored, and so the values behind
> > the mask will be used as though they were real values. If my memory of
> > netCDF is correct, this will be whatever 'FillValue' is defined for the
> > file, but it may depend on what is used to generate the netCDF file.
> > I would normally expect the code to work but produce useless output. A
> > crash is possible though. It would be best if you could post a link to
> > the netCDF file and I will take a closer look to check there is not
> > something else going wrong.
>
> Thanks for the quick response!
>
> Here is the data file: http://tinyurl.com/ms7vzxw. I did some more
> experiments. The picture stays unchanged, even if I fill the masked values
> in the array with some real numbers (I'm not saying that this would give me
> any sensible results...):
>
> import netCDF4
> import matplotlib.tri as tri
>
> var = netCDF4.Dataset('maxele.63.nc').variables
> x = var['x'][:]
> y = var['y'][:]
> data = var['zeta_max'][:]
> elems = var['element'][:, :]-1
>
> triang = tri.Triangulation(x, y, triangles=elems)
>
> data = data.filled(0.0)
>
> # this still crashes the python interpreter
> interp = tri.LinearTriInterpolator(triang, data)
>
> Thanks again!
> Regards Hartmut
> ---------------
> http://boost-spirit.com
> http://stellar.cct.lsu.edu
>
>
>
>
> ------------------------------------------------------------------------------
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>
-- 
Dr Andrew Dawson
Atmospheric, Oceanic & Planetary Physics
Clarendon Laboratory
Parks Road
Oxford OX1 3PU, UK
Tel: +44 (0)1865 282438
Email: da...@at...
Web Site: http://www2.physics.ox.ac.uk/contacts/people/dawson

Ian,
> I'm running into a crash while trying to construct a
> tri.LinearTriInterpolator. Here is the short version of the code:
> 
> import netCDF4
> import matplotlib.tri as tri
> 
> var = netCDF4.Dataset('filename.cdf').variables
> x = var['x'][:]
> y = var['y'][:]
> data = var['zeta_max'][:]
> elems = var['element'][:, :]-1
> 
> triang = tri.Triangulation(x, y, triangles=elems)
> 
> # this crashes the python interpreter
> interp = tri.LinearTriInterpolator(triang, data)
> 
> The data arrays (x, y, data, elems) are fairly large (>1 mio elements),
> all
> represented as numpy arrays (as returned by netCDF4). The 'data' array is
> a
> masked array and contains masked values.
> 
> If somebody cares, I'd be able to post a link to the netCDF data file
> causing this.
> 
> All this happens when using matplotlib 1.3.1, Win32, Python 2.7.
> 
> Any help would be highly appreciated!
> Regards Hartmut
> 
> Hartmut,
> That is an excellent issue report; all the relevant information and
> nothing extraneous. Hence the quick response.
> The second argument to TriLinearInterpolator (and other TriInterpolator
> classes), i.e. your 'data' array, is expected to be an array of the same
> size as the 'x' and 'y' arrays. It is not expecting a masked array. If a
> masked array is used the mask will be ignored, and so the values behind
> the mask will be used as though they were real values. If my memory of
> netCDF is correct, this will be whatever 'FillValue' is defined for the
> file, but it may depend on what is used to generate the netCDF file.
> I would normally expect the code to work but produce useless output. A
> crash is possible though. It would be best if you could post a link to
> the netCDF file and I will take a closer look to check there is not
> something else going wrong.
Thanks for the quick response!
Here is the data file: http://tinyurl.com/ms7vzxw. I did some more experiments. The picture stays unchanged, even if I fill the masked values in the array with some real numbers (I'm not saying that this would give me any sensible results...):
 import netCDF4
 import matplotlib.tri as tri
 var = netCDF4.Dataset('maxele.63.nc').variables
 x = var['x'][:]
 y = var['y'][:]
 data = var['zeta_max'][:]
 elems = var['element'][:, :]-1
 triang = tri.Triangulation(x, y, triangles=elems)
 data = data.filled(0.0)
 # this still crashes the python interpreter
 interp = tri.LinearTriInterpolator(triang, data)
Thanks again!
Regards Hartmut
---------------
http://boost-spirit.com
http://stellar.cct.lsu.edu

On 10 August 2014 18:43, Hartmut Kaiser <har...@gm...> wrote:
> All,
>
> I'm running into a crash while trying to construct a
> tri.LinearTriInterpolator. Here is the short version of the code:
>
> import netCDF4
> import matplotlib.tri as tri
>
> var = netCDF4.Dataset('filename.cdf').variables
> x = var['x'][:]
> y = var['y'][:]
> data = var['attrname'][:]
> elems = var['element'][:,:]-1
>
> triang = tri.Triangulation(x, y, triangles=elems)
>
> # this crashes the python interpreter
> interp = tri.LinearTriInterpolator(triang, data)
>
> The data arrays (x, y, data, elems) are fairly large (>1 mio elements), all
> represented as numpy arrays (as returned by netCDF4). The 'data' array is a
> masked array and contains masked values.
>
> If somebody cares, I'd be able to post a link to the netCDF data file
> causing this.
>
> All this happens when using matplotlib 1.3.1, Win32, Python 2.7.
>
> Any help would be highly appreciated!
> Regards Hartmut
>
Hartmut,
That is an excellent issue report; all the relevant information and nothing
extraneous. Hence the quick response.
The second argument to TriLinearInterpolator (and other TriInterpolator
classes), i.e. your 'data' array, is expected to be an array of the same
size as the 'x' and 'y' arrays. It is not expecting a masked array. If a
masked array is used the mask will be ignored, and so the values behind the
mask will be used as though they were real values. If my memory of netCDF
is correct, this will be whatever 'FillValue' is defined for the file, but
it may depend on what is used to generate the netCDF file.
I would normally expect the code to work but produce useless output. A
crash is possible though. It would be best if you could post a link to the
netCDF file and I will take a closer look to check there is not something
else going wrong.
Ian Thomas

Exec. summary - I was having strange behavior with matshow in a loop and 
also with discrepancies between how iPython Notebook and Python via IDE 
displayed plots.
Solutions:
1) Using pause instead of show fixed matshow in a loop
2) Explicitly invoking %matplotblib qt or generally %matplotlib 
{backend} before importing or using matplotlib fixed various problems 
with plots in notebooks. Now when I create a plot in a notebook, it 
appears, I can work with it, close it when appropriate, and 
simultaneously be able to do other work in notebook cells.
Thanks to everyone for the rapid responses.
JBB
On 7/30/14, 10:04 PM, JBB wrote:
> I've followed up on several suggestions and here is what I've done/found.
>
> (I know I don't use mlab or pylab but I pulled the import lines from
> another source and am leaving them in for the heck of it)
[ Woe/intrigue trimmed ]
>> Is there a pointer to why this worked when my initial approach did not?
>> I thought from the documentation/videos that preparing a plot with
>> relevant commands then issuing the show() command was the preferred
>> approach within Python/Matplotlib.
>>
>> JBB
>>

All,
I'm running into a crash while trying to construct a
tri.LinearTriInterpolator. Here is the short version of the code:
 import netCDF4
 import matplotlib.tri as tri
 var = netCDF4.Dataset('filename.cdf').variables
 x = var['x'][:]
 y = var['y'][:]
 data = var['attrname'][:] 
 elems = var['element'][:,:]-1
 triang = tri.Triangulation(x, y, triangles=elems)
 # this crashes the python interpreter
 interp = tri.LinearTriInterpolator(triang, data)
The data arrays (x, y, data, elems) are fairly large (>1 mio elements), all
represented as numpy arrays (as returned by netCDF4). The 'data' array is a
masked array and contains masked values.
If somebody cares, I'd be able to post a link to the netCDF data file
causing this.
All this happens when using matplotlib 1.3.1, Win32, Python 2.7.
Any help would be highly appreciated!
Regards Hartmut
---------------
http://boost-spirit.com
http://stellar.cct.lsu.edu

Hi,
On Wed, Aug 6, 2014 at 8:15 PM, discolemonade <sch...@gm...> wrote:
> Thanks Paul. I'm new to all of this and the interplay between GTK, it's
> headers and matplotlib is admittedly still a bit of a mystery to me. I have
> GTK installed. I installed it after installing matplotlib because I tried to
> use TKAgg as a backend and ended up running into some problems.
As a matter of interest - what problem did you have? How did you
install matplotlib?
> I'm on
> MacOSX Mavericks. I've been googling around and there don't seem to be any
> direct answers for how to gett these headers installed on Mac OS. What's the
> quickest way to get these headers on my system(via homebrew?)?
Does 'brew install gtk+' work for that?
> And once I
> get them, do I have to reinstall matplotlib so that it can recognize the
> headers?
I'm afraid so. If I were you, I'd check out the source for that, as in:
git clone git://github.com/matplotlib/matplotlib.git
cd matplotlib
git checkout v1.3.1
python setup.py install
Feel free to post again if that doesn't work.
Cheers,
Matthew

I recommend MacPorts [1] to install open source packages on Mac, including matplotlib.
-Sterling
[1] http://www.macports.org/
On Aug 6, 2014, at 8:15PM, discolemonade wrote:
> Thanks Paul. I'm new to all of this and the interplay between GTK, it's
> headers and matplotlib is admittedly still a bit of a mystery to me. I have
> GTK installed. I installed it after installing matplotlib because I tried to
> use TKAgg as a backend and ended up running into some problems. I'm on
> MacOSX Mavericks. I've been googling around and there don't seem to be any
> direct answers for how to gett these headers installed on Mac OS. What's the
> quickest way to get these headers on my system(via homebrew?)? And once I
> get them, do I have to reinstall matplotlib so that it can recognize the
> headers? 
> 
> 
> 
> --
> View this message in context: http://matplotlib.1069221.n5.nabble.com/ImportError-No-module-named-backend-gdk-tp43753p43761.html
> Sent from the matplotlib - users mailing list archive at Nabble.com.
> 
> ------------------------------------------------------------------------------
> Infragistics Professional
> Build stunning WinForms apps today!
> Reboot your WinForms applications with our WinForms controls. 
> Build a bridge from your legacy apps to the future.
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> https://lists.sourceforge.net/lists/listinfo/matplotlib-users

Is test data not installed my default? I didn't used to have to do
anything than pip install then run the tests, but perhaps something has
changed?
Could it be a pip thing somehow? I will try a straight up setup.py
installation.
On Thu, Aug 7, 2014 at 1:07 PM, Benjamin Root <ben...@ou...> wrote:
> Just to note, the first log also has the font-manager issue as well. What
> I see as odd here is that while it was configured to install the tests (and
> presumedly the test data), it doesn't seem to have done that. At the very
> least, the test data wasn't installed.
>
> Ben
>
>
> On Thu, Aug 7, 2014 at 12:15 PM, Russell Warren <ru...@pe...>
> wrote:
>
>> Since my test script was using nose directly, I figured I would try the
>> "proper" `tests.py` script in the repo, but there seems to be a font_manger
>> issue. An exception is thrown with:
>>
>> "<snip> matplotlib/ft2font.so: undefined symbol: inflateEnd"
>>
>> Full log here:
>> http://bpaste.net/raw/MVuf4UlQ1g9xCecivwlQ/
>>
>>
>>
>> On Thu, Aug 7, 2014 at 12:00 PM, Russell Warren <ru...@pe...>
>> wrote:
>>
>>> I'm trying to run the matplotlib unit tests on linux with the agg
>>> backend, and am getting a tonne of errors.
>>>
>>> Here is my test method and output (method is the script created at the
>>> top):
>>> http://bpaste.net/raw/n0JVrWcXnlPVxaAlArHJ/
>>>
>>> It is not clear to me why the tests don't exist. I have run this test
>>> successfully several times on this platform over the last couple of years,
>>> although I haven't tried it in (I'm guessing) a year or so.
>>>
>>> Searching around I found this in the mailing list:
>>> http://goo.gl/9nDILp
>>>
>>> The testing output is similar (bottom of the mail), but perusing the
>>> rest of the thread it does not seem to have a resolution, nor does it seem
>>> like the same issue.
>>>
>>> For reference, matplotlib was installed with `pip install matplotlib`,
>>> and the output log is here:
>>> http://bpaste.net/raw/5tfTFJepFRAwGF5tSkyb/
>>>
>>> Does anybody know what is wrong, or have any tips on where/how I can dig
>>> further?
>>>
>>> Thanks,
>>> Russ
>>>
>>>
>>
>>
>> --
>> Russell Warren
>> Perspexis Technologies Inc.
>>
>> This information is confidential and intended solely for the use of the
>> individual or entity to whom it is addressed.
>> If you have received this email in error, please notify the sender
>> immediately.
>>
>>
>> ------------------------------------------------------------------------------
>> Infragistics Professional
>> Build stunning WinForms apps today!
>> Reboot your WinForms applications with our WinForms controls.
>> Build a bridge from your legacy apps to the future.
>>
>> http://pubads.g.doubleclick.net/gampad/clk?id=153845071&iu=/4140/ostg.clktrk
>> _______________________________________________
>> Matplotlib-users mailing list
>> Mat...@li...
>> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>>
>>
>
-- 
Russell Warren
Perspexis Technologies Inc.
This information is confidential and intended solely for the use of the
individual or entity to whom it is addressed.
If you have received this email in error, please notify the sender
immediately.

Just to note, the first log also has the font-manager issue as well. What I
see as odd here is that while it was configured to install the tests (and
presumedly the test data), it doesn't seem to have done that. At the very
least, the test data wasn't installed.
Ben
On Thu, Aug 7, 2014 at 12:15 PM, Russell Warren <ru...@pe...> wrote:
> Since my test script was using nose directly, I figured I would try the
> "proper" `tests.py` script in the repo, but there seems to be a font_manger
> issue. An exception is thrown with:
>
> "<snip> matplotlib/ft2font.so: undefined symbol: inflateEnd"
>
> Full log here:
> http://bpaste.net/raw/MVuf4UlQ1g9xCecivwlQ/
>
>
>
> On Thu, Aug 7, 2014 at 12:00 PM, Russell Warren <ru...@pe...>
> wrote:
>
>> I'm trying to run the matplotlib unit tests on linux with the agg
>> backend, and am getting a tonne of errors.
>>
>> Here is my test method and output (method is the script created at the
>> top):
>> http://bpaste.net/raw/n0JVrWcXnlPVxaAlArHJ/
>>
>> It is not clear to me why the tests don't exist. I have run this test
>> successfully several times on this platform over the last couple of years,
>> although I haven't tried it in (I'm guessing) a year or so.
>>
>> Searching around I found this in the mailing list:
>> http://goo.gl/9nDILp
>>
>> The testing output is similar (bottom of the mail), but perusing the rest
>> of the thread it does not seem to have a resolution, nor does it seem like
>> the same issue.
>>
>> For reference, matplotlib was installed with `pip install matplotlib`,
>> and the output log is here:
>> http://bpaste.net/raw/5tfTFJepFRAwGF5tSkyb/
>>
>> Does anybody know what is wrong, or have any tips on where/how I can dig
>> further?
>>
>> Thanks,
>> Russ
>>
>>
>
>
> --
> Russell Warren
> Perspexis Technologies Inc.
>
> This information is confidential and intended solely for the use of the
> individual or entity to whom it is addressed.
> If you have received this email in error, please notify the sender
> immediately.
>
>
> ------------------------------------------------------------------------------
> Infragistics Professional
> Build stunning WinForms apps today!
> Reboot your WinForms applications with our WinForms controls.
> Build a bridge from your legacy apps to the future.
>
> http://pubads.g.doubleclick.net/gampad/clk?id=153845071&iu=/4140/ostg.clktrk
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>
>

Since my test script was using nose directly, I figured I would try the
"proper" `tests.py` script in the repo, but there seems to be a font_manger
issue. An exception is thrown with:
"<snip> matplotlib/ft2font.so: undefined symbol: inflateEnd"
Full log here:
http://bpaste.net/raw/MVuf4UlQ1g9xCecivwlQ/
On Thu, Aug 7, 2014 at 12:00 PM, Russell Warren <ru...@pe...> wrote:
> I'm trying to run the matplotlib unit tests on linux with the agg backend,
> and am getting a tonne of errors.
>
> Here is my test method and output (method is the script created at the
> top):
> http://bpaste.net/raw/n0JVrWcXnlPVxaAlArHJ/
>
> It is not clear to me why the tests don't exist. I have run this test
> successfully several times on this platform over the last couple of years,
> although I haven't tried it in (I'm guessing) a year or so.
>
> Searching around I found this in the mailing list:
> http://goo.gl/9nDILp
>
> The testing output is similar (bottom of the mail), but perusing the rest
> of the thread it does not seem to have a resolution, nor does it seem like
> the same issue.
>
> For reference, matplotlib was installed with `pip install matplotlib`, and
> the output log is here:
> http://bpaste.net/raw/5tfTFJepFRAwGF5tSkyb/
>
> Does anybody know what is wrong, or have any tips on where/how I can dig
> further?
>
> Thanks,
> Russ
>
>
-- 
Russell Warren
Perspexis Technologies Inc.
This information is confidential and intended solely for the use of the
individual or entity to whom it is addressed.
If you have received this email in error, please notify the sender
immediately.

I'm trying to run the matplotlib unit tests on linux with the agg backend,
and am getting a tonne of errors.
Here is my test method and output (method is the script created at the top):
http://bpaste.net/raw/n0JVrWcXnlPVxaAlArHJ/
It is not clear to me why the tests don't exist. I have run this test
successfully several times on this platform over the last couple of years,
although I haven't tried it in (I'm guessing) a year or so.
Searching around I found this in the mailing list:
http://goo.gl/9nDILp
The testing output is similar (bottom of the mail), but perusing the rest
of the thread it does not seem to have a resolution, nor does it seem like
the same issue.
For reference, matplotlib was installed with `pip install matplotlib`, and
the output log is here:
http://bpaste.net/raw/5tfTFJepFRAwGF5tSkyb/
Does anybody know what is wrong, or have any tips on where/how I can dig
further?
Thanks,
Russ

Thanks Paul. I'm new to all of this and the interplay between GTK, it's
headers and matplotlib is admittedly still a bit of a mystery to me. I have
GTK installed. I installed it after installing matplotlib because I tried to
use TKAgg as a backend and ended up running into some problems. I'm on
MacOSX Mavericks. I've been googling around and there don't seem to be any
direct answers for how to gett these headers installed on Mac OS. What's the
quickest way to get these headers on my system(via homebrew?)? And once I
get them, do I have to reinstall matplotlib so that it can recognize the
headers? 
--
View this message in context: http://matplotlib.1069221.n5.nabble.com/ImportError-No-module-named-backend-gdk-tp43753p43761.html
Sent from the matplotlib - users mailing list archive at Nabble.com.

Hi Nicolas
I do not have any slides of this material yet. I will try at some point to
upload them on github, although I'm already working on tutorial part two,
so it may take a while.
In the meantime, I have a set of slides from 2012 when I presented this talk:
http://www.cspg.org/cspg/documents/Conventions/Archives/Annual/2012/155_GC2012_A_More_Perceptual_Color_Palette_for_Structure_Maps.pdf
Let me know if you're interested and I will upload them on github.
Hi Damon, yes, I did see your talk and Kristen's and I am really excited
there's good momentum on this topic. I will write you later today about
the matplotlib webpage, great idea.
Thanks to both for your feedback.
Matteo
On Wed, August 6, 2014 2:05 pm, Nicolas P. Rougier wrote:
>
> Really great material (ipython notebook and videos), thanks a lot to you
> all.
>
> Are the slides available somewhere by any chance ?
>
>
>
> Nicolas
>
>
>
> On 06 Aug 2014, at 15:25, Damon McDougall <dam...@gm...>
> wrote:
>
>
>> On Mon, Aug 4, 2014 at 5:20 PM, Matteo Niccoli <ma...@my...>
>> wrote:
>>
>>> Hi All
>>>
>>>
>>> I recently wrote a tutorial on how to evaluate and compare colormaps
>>> using perceptual principle. It is geared towards Matplotlib.
>>>
>>> http://nbviewer.ipython.org/github/mycarta/tutorials/blob/master/1408
>>> _Evaluate_and_compare_colormaps/How_to_evaluate_and_compare_colormaps
>>> .ipynb
>>>
>>>
>>> Although I am a newbie and some of my code may be not all that
>>> pythonic yet, I hope you enjoy the read.
>>>
>>> Any feedback would be welcome.
>>>
>>>
>>> THank you
>>> Matteo
>>>
>>
>> Hi Matteo,
>>
>>
>> Thanks for sharing this resource.
>>
>>
>> Also, I wanted to personally thank you for MyCarta. It's a great
>> resource and Kristen Thyng and I have learned a lot from it. Kristen
>> cited you in a talk she gave at SciPy 2014 last month. We both gave
>> talks at SciPy 2014 about colour maps I think you might find
>> interesting. They were recorded and put on YouTube by Enthought and you
>> can check them out here<https://www.youtube.com/watch?v=Alnc9E1RnD8> and
>> here<https://www.youtube.com/watch?v=rkDgBvT-giw>.
>>
>>
>> I think it would be a good idea to link to your IPython (read Jupyter)
>> notebook, along with some of the work Kristen as done with matplotlib
>> colour maps, from the matplotlib web page. Would you be amenable to
>> this?
>>
>> All the best,
>> Damon
>>
>>
>> --
>> Damon McDougall
>> http://www.damon-is-a-geek.com
>> Institute for Computational Engineering Sciences
>> 201 E. 24th St.
>> Stop C0200
>> The University of Texas at Austin
>> Austin, TX 78712-1229
>>
>>
>> -----------------------------------------------------------------------
>> -------
>> Infragistics Professional
>> Build stunning WinForms apps today!
>> Reboot your WinForms applications with our WinForms controls.
>> Build a bridge from your legacy apps to the future.
>> http://pubads.g.doubleclick.net/gampad/clk?id=153845071&iu=/4140/ostg.cl
>> ktrk _______________________________________________
>> Matplotlib-users mailing list
>> Mat...@li...
>> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>>
>
>

Really great material (ipython notebook and videos), thanks a lot to you all.
Are the slides available somewhere by any chance ?
Nicolas
On 06 Aug 2014, at 15:25, Damon McDougall <dam...@gm...> wrote:
> On Mon, Aug 4, 2014 at 5:20 PM, Matteo Niccoli <ma...@my...> wrote:
>> Hi All
>> 
>> I recently wrote a tutorial on how to evaluate and compare colormaps using
>> perceptual principle. It is geared towards Matplotlib.
>> 
>> http://nbviewer.ipython.org/github/mycarta/tutorials/blob/master/1408_Evaluate_and_compare_colormaps/How_to_evaluate_and_compare_colormaps.ipynb
>> 
>> Although I am a newbie and some of my code may be not all that pythonic
>> yet, I hope you enjoy the read.
>> 
>> Any feedback would be welcome.
>> 
>> THank you
>> Matteo
> 
> Hi Matteo,
> 
> Thanks for sharing this resource.
> 
> Also, I wanted to personally thank you for MyCarta. It's a great
> resource and Kristen Thyng and I have learned a lot from it. Kristen
> cited you in a talk she gave at SciPy 2014 last month. We both gave
> talks at SciPy 2014 about colour maps I think you might find
> interesting. They were recorded and put on YouTube by Enthought and
> you can check them out
> here<https://www.youtube.com/watch?v=Alnc9E1RnD8> and
> here<https://www.youtube.com/watch?v=rkDgBvT-giw>.
> 
> I think it would be a good idea to link to your IPython (read Jupyter)
> notebook, along with some of the work Kristen as done with matplotlib
> colour maps, from the matplotlib web page. Would you be amenable to
> this?
> 
> All the best,
> Damon
> 
> -- 
> Damon McDougall
> http://www.damon-is-a-geek.com
> Institute for Computational Engineering Sciences
> 201 E. 24th St.
> Stop C0200
> The University of Texas at Austin
> Austin, TX 78712-1229
> 
> ------------------------------------------------------------------------------
> Infragistics Professional
> Build stunning WinForms apps today!
> Reboot your WinForms applications with our WinForms controls. 
> Build a bridge from your legacy apps to the future.
> http://pubads.g.doubleclick.net/gampad/clk?id=153845071&iu=/4140/ostg.clktrk
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users

On Mon, Aug 4, 2014 at 5:20 PM, Matteo Niccoli <ma...@my...> wrote:
> Hi All
>
> I recently wrote a tutorial on how to evaluate and compare colormaps using
> perceptual principle. It is geared towards Matplotlib.
>
> http://nbviewer.ipython.org/github/mycarta/tutorials/blob/master/1408_Evaluate_and_compare_colormaps/How_to_evaluate_and_compare_colormaps.ipynb
>
> Although I am a newbie and some of my code may be not all that pythonic
> yet, I hope you enjoy the read.
>
> Any feedback would be welcome.
>
> THank you
> Matteo
Hi Matteo,
Thanks for sharing this resource.
Also, I wanted to personally thank you for MyCarta. It's a great
resource and Kristen Thyng and I have learned a lot from it. Kristen
cited you in a talk she gave at SciPy 2014 last month. We both gave
talks at SciPy 2014 about colour maps I think you might find
interesting. They were recorded and put on YouTube by Enthought and
you can check them out
here<https://www.youtube.com/watch?v=Alnc9E1RnD8> and
here<https://www.youtube.com/watch?v=rkDgBvT-giw>.
I think it would be a good idea to link to your IPython (read Jupyter)
notebook, along with some of the work Kristen as done with matplotlib
colour maps, from the matplotlib web page. Would you be amenable to
this?
All the best,
Damon
-- 
Damon McDougall
http://www.damon-is-a-geek.com
Institute for Computational Engineering Sciences
201 E. 24th St.
Stop C0200
The University of Texas at Austin
Austin, TX 78712-1229