matplotlib-users Mailing List for matplotlib

On Thursday, October 20, 2011, Friedrich Romstedt <
fri...@gm...> wrote:
> 2011年10月20日 Eric Firing <ef...@ha...>:
>> Friedrich (or others),
>>
>> I hope you will consider the following to be constructive.
>
> I agree :-/ It was very very late in the night [5:30 a.m.], so things
> happen, but I think that's no kind of excuse for my rude tone in that
> email. I'm sorry.
>
>> I understand one use case for a gray switch: sometimes one needs to make
>> gray versions of plots for formal publication, but one wants to have
>> nicer color versions for other purposes.
>
> Exactly, but I believe when we have the functionality the use cases
> will just start to emerge - like it always is.
>
> For instance, but this is just guessing, someone came up with the will
> to write out his figure in CMYK. Others might want to turn black into
> white and vice versa, keeping other colours invariant.
>
>> What is the use case for grayscale support for parts of the Figure? It
>> seems to me that this requires a much more complex and extensive set of
>> changes than a switch for the whole Figure. Is it really worth it?
>
> I think yes.
>
> It is a bit of effort, yes.
>
>> You indicate that some caching will need to be eliminated. Is this
>> correct? If your branch causes a significant loss of speed in
>> displaying and interacting with large data sets, then I will oppose
>> merging it. Speed matters for a significant fraction of mpl usage.
>
> Correct. I think it is measurable, but only of O(2). I believe this
> kind of fluctuation can be called noise :-)
>
> The tests cannot esily resolve this because they do everything once.
>
> And with a bit of work I might be able to keep the caching.
>
>> Improvements in mpl architecture that maintain or increase speed, and
>> that make it more comprehensible, maintainable, and capable, are
>> certainly welcome. Color handling has grown organically, often
>> bedeviled by that pesky alpha, and I have no doubt that it could be
>> cleaned up and improved.
>
> I can capably only speak of colors.py and some conversion functions
> somewhere else. I've noticed a rather small-grained approach. I
> tried to unify things.
>
> But this you already said.
>
> cu,
> Friedrich
>
Friedrich,
Great to hear from you again!
I have been doing some thinking about this problem and how it could relate
to issues I have encountered in my work on mplot3d. The key problem I
encounter there is that shading of the colors needs to be performed based on
perspective (rotation of the scene and such).
In many ways, this is a similar problem to wanting b&w versions of graphs.
The problem I have had in addressing this is that there are two primary ways
to specify color in mpl. First, you can specify it directly with strings or
rgb(a) tuples. Second, you can specify it through a ScalarMappable (eg,
colormap). This makes for two very different code paths to account for, and
it can often be difficult to detect which path is needed.
So, what if we unify everything under ScalarMappable? The use case of
directly specifying the color could be envisioned as the color spec being
the data applied to an "identity colormap".
Then, we extend ScalarMappable such that a chain of transforms could be
performed on the rgba array. I envision this to be very similar to how the
projections framework operates. We can then have a set of stock transforms
that users could select at will (such as color to b&w).
We can use the lessons and successes of the projections framework to guide
our design here.
Thoughts?
Ben Root

Holger Brandsmeier <holger.brandsmeier@...> writes:
> 
> Dear list,
> 
> I would like to have two subplots in a figure one below each other (so
> arranged in a 2x1 matrix). I want the upper subplot to have a very
> small height compared to the lower one, say 10 times smaller than the
> other one.
> 
> How can I do that with matplotlib? I didn't find that with subplot()
> command. I also found the add_subplot() function with many parameters,
> but I didn't see which of those parameter would help me.
> 
> Thanks for any advice,
> Holger
> 
Have a look at http://matplotlib.sourceforge.net/users/gridspec.html
greetings
Till

Hi all,
I've encountered the following problem when trying to use a logarithmic color scale:
The code
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import ticker
x = np.arange(-100,105,5)
X,Y = np.meshgrid(x,x)
Z = X**2 + Y**2 + 0.1
plt.contourf(X,Y,Z.T, locator=ticker.LogLocator())
plt.colorbar()
works fine to create a contour map with logarithmic colorscale. Now I want to refine the scaling by adding more colorlevels. Therefore I add another option for the contourf() function, as follows:
plt.contourf(X,Y,Z.T, np.logspace(-1,5,15), locator=ticker.LogLocator())
However when I do this, all but one tick of the colorbar just vanish. What is the reason for this and how can I fix that?
Kind regards,
Marko

On 2011年10月19日 15:45:07 -0500
 Benjamin Root <ben...@ou...> wrote:
> On Fri, Oct 14, 2011 at 3:42 AM, Nils Wagner
> <nw...@ia...>wrote:
> 
>> Hi all,
>>
>> what is the native "data" coordinate system for Arrows 
>>in
>> a polar plot ?
>>
>> How do I add arrows to a polar plot ?
>>
>> An example would be appreciated.
>> fig = figure(figsize=(12,12))
>> ax = fig.add_subplot(111, polar=True)
>>
>> Nils
>>
>>
> I don't know if it is an existing feature or not. If it 
>isn't, then I think
> a feature request should be filed. If it is, then a 
>request should be filed
> anyway for an example to be added to the gallery.
> 
> Ben Root
Hi Ben,
it seems to work in general, but how can I increase the 
size of the arrowhead ?
from pylab import figure, arrow, show
import numpy as np
fig = figure(figsize=(15,15))
ax = fig.add_subplot(111, polar=True)
ax.arrow(0.0,0.0,np.pi/4,0.3,lw=2)
ax.arrow(0.0,0.0,np.pi/2,0.4)
show()
Nils

Hi Holger,
On Oct 20, 2011, at 11:21 AM, Holger Brandsmeier wrote:
> I would like to have two subplots in a figure one below each other (so
> arranged in a 2x1 matrix). I want the upper subplot to have a very
> small height compared to the lower one, say 10 times smaller than the
> other one.
> 
> How can I do that with matplotlib? I didn't find that with subplot()
> command. I also found the add_subplot() function with many parameters,
> but I didn't see which of those parameter would help me.
Is this what you need? 
http://matplotlib.sourceforge.net/mpl_toolkits/axes_grid/users/overview.html#scatter-hist-py-with-axesdivider
Best regards,
Jean-Louis
> 
> Thanks for any advice,
> Holger
> 
> -- 
> Holger Brandsmeier, SAM, ETH Zürich
> http://www.sam.math.ethz.ch/people/bholger
> 
> ------------------------------------------------------------------------------
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> 

On 2011年10月19日 15:45:07 -0500
 Benjamin Root <ben...@ou...> wrote:
> On Fri, Oct 14, 2011 at 3:42 AM, Nils Wagner
> <nw...@ia...>wrote:
> 
>> Hi all,
>>
>> what is the native "data" coordinate system for Arrows 
>>in
>> a polar plot ?
>>
>> How do I add arrows to a polar plot ?
>>
>> An example would be appreciated.
>> fig = figure(figsize=(12,12))
>> ax = fig.add_subplot(111, polar=True)
>>
>> Nils
>>
>>
> I don't know if it is an existing feature or not. If it 
>isn't, then I think
> a feature request should be filed. If it is, then a 
>request should be filed
> anyway for an example to be added to the gallery.
> 
> Ben Root
Done. See 
https://github.com/matplotlib/matplotlib/issues/542
Nils

Dear list,
I would like to have two subplots in a figure one below each other (so
arranged in a 2x1 matrix). I want the upper subplot to have a very
small height compared to the lower one, say 10 times smaller than the
other one.
How can I do that with matplotlib? I didn't find that with subplot()
command. I also found the add_subplot() function with many parameters,
but I didn't see which of those parameter would help me.
Thanks for any advice,
Holger
-- 
Holger Brandsmeier, SAM, ETH Zürich
http://www.sam.math.ethz.ch/people/bholger

2011年10月20日 Eric Firing <ef...@ha...>:
> Friedrich (or others),
>
> I hope you will consider the following to be constructive.
I agree :-/ It was very very late in the night [5:30 a.m.], so things
happen, but I think that's no kind of excuse for my rude tone in that
email. I'm sorry.
> I understand one use case for a gray switch: sometimes one needs to make
> gray versions of plots for formal publication, but one wants to have
> nicer color versions for other purposes.
Exactly, but I believe when we have the functionality the use cases
will just start to emerge - like it always is.
For instance, but this is just guessing, someone came up with the will
to write out his figure in CMYK. Others might want to turn black into
white and vice versa, keeping other colours invariant.
> What is the use case for grayscale support for parts of the Figure? It
> seems to me that this requires a much more complex and extensive set of
> changes than a switch for the whole Figure. Is it really worth it?
I think yes.
It is a bit of effort, yes.
> You indicate that some caching will need to be eliminated. Is this
> correct? If your branch causes a significant loss of speed in
> displaying and interacting with large data sets, then I will oppose
> merging it. Speed matters for a significant fraction of mpl usage.
Correct. I think it is measurable, but only of O(2). I believe this
kind of fluctuation can be called noise :-)
The tests cannot esily resolve this because they do everything once.
And with a bit of work I might be able to keep the caching.
> Improvements in mpl architecture that maintain or increase speed, and
> that make it more comprehensible, maintainable, and capable, are
> certainly welcome. Color handling has grown organically, often
> bedeviled by that pesky alpha, and I have no doubt that it could be
> cleaned up and improved.
I can capably only speak of colors.py and some conversion functions
somewhere else. I've noticed a rather small-grained approach. I
tried to unify things.
But this you already said.
cu,
Friedrich

On 10/19/2011 05:15 PM, Friedrich Romstedt wrote:
> Hi,
>
> I appreciate all the effort and, lastly, the large functionality
> offered by matplotlib. But I found no way around formulating things a
> bit provocative on
> http://friedrichromstedt.github.com/matplotlib-grayscale/index.html.
>
> The project is about matplotlib-grayscale, a matplotlib patch that
> aims at grayscale toggle-switch behaviour as well as colour filtering
> / conversion.
>
> The project has a proof of concept (see under the URL above), but I
> disregard this since it has major design flaws, although *it works*.
> But as most things in matplotlib, *which work*, I would rather prefer
> a well thought-through solution. No offense, it's just the way it
> goes with matplotlib. I have some examples in the roadmap.
>
> I'm telling upfront that I don't want to get engaged with mpl, I just
> want to get this my project getting an ex-project. So I want to do it
> quickly and properly, let it go to the main repository, and forget
> about it except bug-fixes, the usual maintenance.
>
> Time frame, is, including good testing, say 3 monthes. I want to get
> it started quickly and just want to let you all let know about this
> project. I'll do it irrespective of if there's clubbing, it's up to
> the maintainers to accept or reject it in the end.
>
> I'm asking for ideas about the general design, which do not go too far
> with mpl, but use the existing framework to its best. I've put some
> of my ideas on the web page noted above.
>
> I did not dive into it again yet after the few monthes of letting it
> put aside. I just dug out my old commit messages and have put them
> together on the web page together with the superficial things I
> remembered. But I think the detail knowledge can be recovered easily.
>
> Only constructive feedback please.
>
> Especially a documenter/tester with a fresh eye on the things I know
> now would be great. Same applies for testing. The testing done for
> the pre-github proof-of-concept by Benjamin Root must be noted here,
> it made flaws and side-tracks apparent I either didn't know about or
> did overlook. Documenting and testing of the technical things is up
> to me, only the interface documentation and testing boredom.
>
> Last time I kept the project secret, I'm not gonna do commit that mistake again.
>
> Without matplotlib, lastly, also this patch would simply not exist,
> neither in my mind nor in reality.
>
> cu,
> Friedrich
>
> P.S.: I keep the right of dropping the project at any time. I'll do
> my diploma next monthes, so time might be short. But I think it might
> serve its purpose.
>
Friedrich (or others),
I hope you will consider the following to be constructive.
I understand one use case for a gray switch: sometimes one needs to make 
gray versions of plots for formal publication, but one wants to have 
nicer color versions for other purposes.
What is the use case for grayscale support for parts of the Figure? It 
seems to me that this requires a much more complex and extensive set of 
changes than a switch for the whole Figure. Is it really worth it?
You indicate that some caching will need to be eliminated. Is this 
correct? If your branch causes a significant loss of speed in 
displaying and interacting with large data sets, then I will oppose 
merging it. Speed matters for a significant fraction of mpl usage.
Improvements in mpl architecture that maintain or increase speed, and 
that make it more comprehensible, maintainable, and capable, are 
certainly welcome. Color handling has grown organically, often 
bedeviled by that pesky alpha, and I have no doubt that it could be 
cleaned up and improved.
Eric

JJ,
The workaround works. Thanks.
-Sterling
On Oct 17, 2011, at 6:58PM, Jae-Joon Lee wrote:
> Meanwhile, you may do
> 
> from matplotlib.legend import Legend
> l = Legend(fig, h, l, loc='lower right')
> fig.legends.append(l)
> 
> This should be equivalent to fig.legend(h,l,loc='lower right').
> 
> Or, if you don't need axes legend, you may do
> 
> legend(h,l,loc='lower right', bbox_to_anchor=[0,0,1,1],
> bbox_transform=fig.transFigure)
> 
> Regards,
> 
> -JJ
> 
> On Tue, Oct 18, 2011 at 6:59 AM, Sterling Smith <sm...@fu...> wrote:
>> Let me first say that I appreciate the work that the developers have put into matplotlib. You're doing a great job.
>> 
>> I have filed a bug report at
>> 
>> https://github.com/matplotlib/matplotlib/issues/533
>> 
>> wherein I post the following
>> 
>> Consider:
>> 
>> from pylab import *
>> x = arange(0,1,.01)
>> y = x**2
>> fig = figure(2)
>> ax = fig.add_subplot(111)
>> errorbar(x,y,yerr=x/10.,label='$x^2$')
>> errorbar(x,y**3,yerr=x/10.,label='$x^6$')
>> legend(loc='upper center')
>> h,l = ax.get_legend_handles_labels()
>> fig.legend(h,l,loc='lower right')
>> 
>> 
>> I am getting the right legend for the axes based legend, but the figure based legend seems to be using the different parts of the errorbar for subsequent handles, instead of using them as a group. From what I can tell, this has appeared since the upgrade to version 1.1.0.
>> 
>> I am running on Linux, python 2.7, gtkAgg backend.
>> 
>> Thanks,
>> Sterling
>> 
>> 
>> ------------------------------------------------------------------------------
>> All the data continuously generated in your IT infrastructure contains a
>> definitive record of customers, application performance, security
>> threats, fraudulent activity and more. Splunk takes this data and makes
>> sense of it. Business sense. IT sense. Common sense.
>> http://p.sf.net/sfu/splunk-d2d-oct
>> _______________________________________________
>> Matplotlib-users mailing list
>> Mat...@li...
>> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>> 

Hi,
I appreciate all the effort and, lastly, the large functionality
offered by matplotlib. But I found no way around formulating things a
bit provocative on
http://friedrichromstedt.github.com/matplotlib-grayscale/index.html.
The project is about matplotlib-grayscale, a matplotlib patch that
aims at grayscale toggle-switch behaviour as well as colour filtering
/ conversion.
The project has a proof of concept (see under the URL above), but I
disregard this since it has major design flaws, although *it works*.
But as most things in matplotlib, *which work*, I would rather prefer
a well thought-through solution. No offense, it's just the way it
goes with matplotlib. I have some examples in the roadmap.
I'm telling upfront that I don't want to get engaged with mpl, I just
want to get this my project getting an ex-project. So I want to do it
quickly and properly, let it go to the main repository, and forget
about it except bug-fixes, the usual maintenance.
Time frame, is, including good testing, say 3 monthes. I want to get
it started quickly and just want to let you all let know about this
project. I'll do it irrespective of if there's clubbing, it's up to
the maintainers to accept or reject it in the end.
I'm asking for ideas about the general design, which do not go too far
with mpl, but use the existing framework to its best. I've put some
of my ideas on the web page noted above.
I did not dive into it again yet after the few monthes of letting it
put aside. I just dug out my old commit messages and have put them
together on the web page together with the superficial things I
remembered. But I think the detail knowledge can be recovered easily.
Only constructive feedback please.
Especially a documenter/tester with a fresh eye on the things I know
now would be great. Same applies for testing. The testing done for
the pre-github proof-of-concept by Benjamin Root must be noted here,
it made flaws and side-tracks apparent I either didn't know about or
did overlook. Documenting and testing of the technical things is up
to me, only the interface documentation and testing boredom.
Last time I kept the project secret, I'm not gonna do commit that mistake again.
Without matplotlib, lastly, also this patch would simply not exist,
neither in my mind nor in reality.
cu,
Friedrich
P.S.: I keep the right of dropping the project at any time. I'll do
my diploma next monthes, so time might be short. But I think it might
serve its purpose.
-- 
I want to be honest and open to you from the beginning who I am and
what I'm doing. So here's a small collection of informative links:
http://github.com/friedrichromstedt/ (github code sharing);
http://friedrichr.blogspot.com/2011/10/home-dashboard.html
(All-Purpose Blog); http://picasaweb.google.com/friedrichromstedt/
(photos which are public); http://vimeo.com/friedrichr/ (videos which
are public). There's a legacy web page
http://www.friedrichromstedt.org/ which is to be replaced. There's a
legacy YouTube channel http://www.youtube.com/user/friedrichromstedt/
which is unmaintained. And there's a news feed on Twitter:
@f_romstedt.

On 10/19/11 4:37 PM, questions anon wrote:
> thank you, I am not quite sure how to 'draw' the shapefile
 from matplotlib.collections import LineCollection
 ax = plt.gca() # get current axes instance
# 'DSE_REGIONS' instance variable created by readshapefile method call.
 lines = LineCollection(map.DSE_REGIONS)
 ax.add_collection(lines)
-Jeff
> but making those changes and removing the shapefile has sped the 
> processing up considerably!
> Thank you for your help
>
> On Wed, Oct 19, 2011 at 11:42 PM, Jeff Whitaker <js...@fa... 
> <mailto:js...@fa...>> wrote:
>
> On 10/18/11 8:55 PM, questions anon wrote:
>> Thanks Jeff, that certainly speeds it up! But when I take them
>> out of the loop and place them elsewhere they are no longer added
>> to the map.
>> Is there someway I can call them in the loop but still get it to
>> run quickly?
>> Thanks
>
> Just the Basemap instance creation and the transformation of
> coordinates to projection space should be hoisted out of the loop
>
>
> map =
> Basemap(projection='merc',llcrnrlat=-40,urcrnrlat=-33,
> 
> llcrnrlon=139.0,urcrnrlon=151.0,lat_ts=0,resolution='i')
> x,y=map(*N.meshgrid(LON,LAT))
>
> you can leave the other statements in.
>
> If you still have memory issues, bring the readshapefile call out,
> and draw the shapes whose coordinates are stored in the instance
> variable map.DSE_REGIONS manually in the loop.
>
> -Jeff
>
>>
>> On Fri, Oct 14, 2011 at 10:54 PM, Jeff Whitaker
>> <js...@fa... <mailto:js...@fa...>> wrote:
>>
>> On 10/12/11 8:20 PM, questions anon wrote:
>>> Hi All,
>>> I keep receiving a memory error when processing many netcdf
>>> files. I assumed it had something to do with how I loop
>>> things and maybe needed to close things off properly but I
>>> recently received an error that made me think it might be
>>> because of matplotlib.
>>>
>>> In the code below I am looping through a bunch of netcdf
>>> files (each file is hourly data for one month) and within
>>> each netcdf file I am outputting a *png file every three
>>> hours. This works for one netcdf file (therefore one month)
>>> but when it begins to process the next netcdf file I receive
>>> a memory error (see below). Since I have tidied some of my
>>> code up it seems to process partly into the second file but
>>> then I still receive the memory error.
>>> I have tried a few suggestions such as:
>>> -Combining the dataset using MFDataset (using NETCDF4) is
>>> not an option because the files do not have unlimited
>>> dimension.
>>> - gc.collect() but that just results in a /GEOS_ERROR: bad
>>> allocation error/.
>>> -only open LAT and LON once (which worked)
>>>
>>> System Details:
>>> Python 2.7.2 |EPD 7.1-2 (32-bit)| (default, Jul 3 2011,
>>> 15:13:59) [MSC v.1500 32 bit (Intel)] on win32
>>>
>>> Any feedback will be greatly appreciated as I seem to keep
>>> ending up with memory errors when working with netcdf files
>>> this even happens if I am using a much better computer.
>>>
>>> *Most recent error: *
>>> Traceback (most recent call last):
>>> File
>>> "C:\plot_netcdf_merc_multiplot_across_multifolders_TSFC.py",
>>> line 78, in <module>
>>> plt.savefig((os.path.join(outputfolder,
>>> 'TSFC'+date_string+'UTC.png')))
>>> File "C:\Python27\lib\site-packages\matplotlib\pyplot.py",
>>> line 363, in savefig
>>> return fig.savefig(*args, **kwargs)
>>> File "C:\Python27\lib\site-packages\matplotlib\figure.py",
>>> line 1084, in savefig
>>> self.canvas.print_figure(*args, **kwargs)
>>> File
>>> "C:\Python27\lib\site-packages\matplotlib\backends\backend_wxagg.py",
>>> line 100, in print_figure
>>> FigureCanvasAgg.print_figure(self, filename, *args,
>>> **kwargs)
>>> File
>>> "C:\Python27\lib\site-packages\matplotlib\backend_bases.py",
>>> line 1923, in print_figure
>>> **kwargs)
>>> File
>>> "C:\Python27\lib\site-packages\matplotlib\backends\backend_agg.py",
>>> line 438, in print_png
>>> FigureCanvasAgg.draw(self)
>>> File
>>> "C:\Python27\lib\site-packages\matplotlib\backends\backend_agg.py",
>>> line 393, in draw
>>> self.renderer = self.get_renderer()
>>> File
>>> "C:\Python27\lib\site-packages\matplotlib\backends\backend_agg.py",
>>> line 404, in get_renderer
>>> self.renderer = RendererAgg(w, h, self.figure.dpi)
>>> File
>>> "C:\Python27\lib\site-packages\matplotlib\backends\backend_agg.py",
>>> line 59, in __init__
>>> self._renderer = _RendererAgg(int(width), int(height),
>>> dpi, debug=False)
>>> RuntimeError: Could not allocate memory for image
>>>
>>> *Error when I added gc.collect()*
>>> GEOS_ERROR: bad allocation
>>>
>>> *Old error (before adding gc.collect() )*
>>> /Traceback (most recent call last):
>>> File
>>> "d:/plot_netcdf_merc_multiplot_across_multifolders__memoryerror.py",
>>> line 44, in <module>
>>> TSFC=ncfile.variables['T_SFC'][1::3]
>>> File "netCDF4.pyx", line 2473, in
>>> netCDF4.Variable.__getitem__ (netCDF4.c:23094)
>>> MemoryError/
>>>
>>>
>>>
>>> from netCDF4 import Dataset
>>> import numpy as N
>>> import matplotlib.pyplot as plt
>>> from mpl_toolkits.basemap import Basemap
>>> from netcdftime import utime
>>> from datetime import datetime
>>> import os
>>> import gc
>>>
>>>
>>> shapefile1="E:/
>>>
>>> griddeddatasamples/GIS/DSE_REGIONS"
>>> MainFolder=r"E:/griddeddatasamples/GriddedData/InputsforValidation/T_SFC/"
>>> OutputFolder=r"E:/griddeddatasamples/GriddedData/OutputsforValidation"
>>> fileforlatlon=Dataset("E:/griddeddatasamples/GriddedData/InputsforValidation/T_SFC/TSFC_1974_01/IDZ00026_VIC_ADFD_T_SFC.nc",
>>> 'r+', 'NETCDF4')
>>> LAT=fileforlatlon.variables['latitude'][:]
>>> LON=fileforlatlon.variables['longitude'][:]
>>>
>>> for (path, dirs, files) in os.walk(MainFolder):
>>> for dir in dirs:
>>> print dir
>>> path=path+'/'
>>> for ncfile in files:
>>> if ncfile[-3:]=='.nc':
>>> print "dealing with ncfiles:", ncfile
>>> ncfile=os.path.join(path,ncfile)
>>> ncfile=Dataset(ncfile, 'r+', 'NETCDF4')
>>> TSFC=ncfile.variables['T_SFC'][1::3]
>>> TIME=ncfile.variables['time'][1::3]
>>> ncfile.close()
>>> gc.collect()
>>>
>>> for TSFC, TIME in zip((TSFC[:]),(TIME[:])):
>>> cdftime=utime('seconds since 1970年01月01日
>>> 00:00:00')
>>> ncfiletime=cdftime.num2date(TIME)
>>> print ncfiletime
>>> timestr=str(ncfiletime)
>>> d = datetime.strptime(timestr, '%Y-%m-%d
>>> %H:%M:%S')
>>> date_string = d.strftime('%Y%m%d_%H%M')
>>>
>>> map =
>>> Basemap(projection='merc',llcrnrlat=-40,urcrnrlat=-33,
>>> 
>>> llcrnrlon=139.0,urcrnrlon=151.0,lat_ts=0,resolution='i')
>>> x,y=map(*N.meshgrid(LON,LAT))
>>> map.drawcoastlines(linewidth=0.5)
>>> map.readshapefile(shapefile1, 'DSE_REGIONS')
>>> map.drawstates()
>>>
>>> plt.title('Surface temperature at %s
>>> UTC'%ncfiletime)
>>> ticks=[-5,0,5,10,15,20,25,30,35,40,45,50]
>>> CS = map.contourf(x,y,TSFC, ticks,
>>> cmap=plt.cm.jet)
>>> l,b,w,h =0.1,0.1,0.8,0.8
>>> cax = plt.axes([l+w+0.025, b, 0.025, h], )
>>> cbar=plt.colorbar(CS, cax=cax,
>>> drawedges=True)
>>>
>>> plt.savefig((os.path.join(OutputFolder,
>>> 'TSFC'+date_string+'UTC.png')))
>>> plt.close()
>>> gc.collect()
>>>
>>
>> Try moving these lines
>>
>>
>> map =
>> Basemap(projection='merc',llcrnrlat=-40,urcrnrlat=-33,
>> 
>> llcrnrlon=139.0,urcrnrlon=151.0,lat_ts=0,resolution='i')
>> x,y=map(*N.meshgrid(LON,LAT))
>> map.drawcoastlines(linewidth=0.5)
>> map.readshapefile(shapefile1, 'DSE_REGIONS')
>> map.drawstates()
>>
>> out of the loop.
>>
>> -Jeff
>>
>>
>
>