matplotlib-users Mailing List for matplotlib

Hi Lubos,
Lubos Vrbka wrote:
>> 1. Would it be possible to do only shallow copy of the arrays that are
>> being plotted so that on redrawing the figure, chanes in the datasets
>> would be picked up automatically? If not, is Line2D.set_data(...) the
>> right approach?
>> 
> isn't this the way how the plotting is done? in my experience (iirc), 
> the following thing works (x, y1, y2 are numpy arrays):
>
> pylab.ion()
>
> a, = pylab.plot(x,y1)
> b, = pylab.plot(x,y2)
> ...
> y1 += 10
> y2 += 20
>
> a.set_ydata(y1)
> b.set_ydata(y2)
> pylab.draw()
>
> y1 += 10
> y2 += 20
> a.set_ydata(y1)
> pylab.draw()
>
> in my experience BOTH plots get updated in this procedure, so i have to 
> do first a deep copy in my case to get rid of these 'unwanted effects'...
> 
If I understand correctly the len of X and Y will be changing, 
therefore you may have to use set_data() function of Line2D
set_data(self, *args)
Set the x and y data
ACCEPTS: (array xdata, array ydata)
I seem to remember getting a size mismatch if X and Y grew in length and 
I tried to use set_xdata() or set_ydata() separately.
YMMV
Steve

Hi,
Try
 mylines = plot1.get_lines()
 plot2.legend(mylines, [p.get_label() for p in mylines])
When you call the legend method with two arguments, first argument is
a list of lines and second argument is a list of labels. It does not
seem to matter whether lines are from same axes.
Hope this helps.
-JJ
On Mon, Jul 14, 2008 at 12:47 PM, David Lonie <lon...@gm...> wrote:
> I have an application that has 2 subplots, and I'd like to use the second to
> display the legend for the first. For example, for subplots plot1 and plot2:
>
> plot1.plot((1,2,3,4), label='up', color = 'g')
> plot1.plot((4,3,2,1), label='down', color = 'b')
>
> plot2.legend("What can I put here to display a legend for plot1 on
> plot2???")
>
> Can this be done?
>
> Thanks,
>
> Dave
> -------------------------------------------------------------------------
> Sponsored by: SourceForge.net Community Choice Awards: VOTE NOW!
> Studies have shown that voting for your favorite open source project,
> along with a healthy diet, reduces your potential for chronic lameness
> and boredom. Vote Now at http://www.sourceforge.net/community/cca08
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>
>

On Mon, Jul 14, 2008 at 3:05 PM, Michael Droettboom <md...@st...> wrote:
> I can confirm this.
>
> Commenting out "del Gcf.figs[num]" in Gcf.destroy (in _pylab_helpers.py)
> also seems to resolve the leak. But I have no idea why, so I won't
> commit it just yet. I don't have much time to look deeper now. Does
> anyone (who probably understands figure management better than me) have
> an idea what might cause this?
Can you post the script you are using to test -- I am a little
confused from reading this thread by whether or not twinx is
implicated. Also, I saw that you committed some changes vis-a-vis the
twinx leak
 r5747 | mdboom | 2008年07月11日 13:21:53 -0500 (2008年7月11日) | 2 lines
 Fix memory leak when using shared axes.
so I thought that part was resolved already...
JDH

I can confirm this.
Commenting out "del Gcf.figs[num]" in Gcf.destroy (in _pylab_helpers.py) 
also seems to resolve the leak. But I have no idea why, so I won't 
commit it just yet. I don't have much time to look deeper now. Does 
anyone (who probably understands figure management better than me) have 
an idea what might cause this?
Cheers,
Mike
Manuel Metz wrote:
> Michael Droettboom wrote:
>> Which backend?
>
> GTK, GTKAgg, TK, but not with any backend without a window: Agg, 
> Cairo, PS, PDF, SVG ...
>
> Cheers,
> Manuel
>
>> Cheers,
>> Mike
>>
>> Manuel Metz wrote:
>>> Michael Droettboom wrote:
>>>> Thanks for the report. So we can diagnose this, what version of 
>>>> matplotlib are you reporting this for?
>>>>
>>>> Also, you may be interested in the following FAQ (and the one 
>>>> following it):
>>>>
>>>> http://matplotlib.sourceforge.net/faq.html#LEAKS
>>>
>>> Hi,
>>>
>>> I tested this with the lastest svn, and I do also see a leak. But 
>>> it's not related to twinx, but to pylab.close(). If I just comment 
>>> out this one line, the memleak disappears ...
>>>
>>> Manuel
>>>
>>>> Cheers,
>>>> Mike
>>>>
>>>> laurent oget wrote:
>>>>> i forgot two imports.
>>>>>
>>>>> import math
>>>>> import gc
>>>>> import pylab as PL
>>>>>
>>>>>
>>>>> def looptest():
>>>>> while(1):
>>>>> fig=PL.figure(1)
>>>>> ax=fig.add_subplot(211)
>>>>> ax.set_position((0,0,0.9,0.45))
>>>>> ax1=PL.twinx(ax)
>>>>> t=range(1000)
>>>>> st=[math.sin(x*0.01) for x in t]
>>>>> ax.plot(t,st)
>>>>> fig.clf()
>>>>> PL.close(1)
>>>>> gc.collect()
>>>>> print "GC"
>>>>> print len(gc.get_objects())
>>>>> print len(gc.garbage)
>>>>> looptest()
>>>>> 2008年7月11日 laurent oget <la...@og... <mailto:la...@og...>>:
>>>>>
>>>>> I think i narrowed down the memory leak i have been chasing for a
>>>>> while.
>>>>> If i remove the call to twinx i get a slow leak, which would 
>>>>> cause
>>>>> me trouble
>>>>> after a very long time. With the call to twinx, however i am
>>>>> losing thousands of objects
>>>>> at each loop.
>>>>>
>>>>> Thanks,
>>>>>
>>>>> Laurent
>>>>>
>>>>>
>>>>> >>>>>>>>>>>>>>>>>>>>>>>>> import pylab as PL
>>>>> def looptest():
>>>>> while(1):
>>>>> fig=PL.figure(1)
>>>>> ax=fig.add_subplot(211)
>>>>> ax.set_position((0,0,0.9,0.45))
>>>>> ax1=PL.twinx(ax)
>>>>> t=range(1000)
>>>>> st=[math.sin(x*0.01) for x in t]
>>>>> ax.plot(t,st)
>>>>> fig.clf()
>>>>> PL.close(1)
>>>>> gc.collect()
>>>>> print "GC"
>>>>> print len(gc.get_objects())
>>>>> print len(gc.garbage)
>>>>> looptest()
>>>>> >>>>>>>>>>>>>>>>>>>>>>>>>>>
>>>>>
>>>>>
>>>>> ------------------------------------------------------------------------ 
>>>>>
>>>>>
>>>>> ------------------------------------------------------------------------- 
>>>>>
>>>>> Sponsored by: SourceForge.net Community Choice Awards: VOTE NOW!
>>>>> Studies have shown that voting for your favorite open source project,
>>>>> along with a healthy diet, reduces your potential for chronic 
>>>>> lameness
>>>>> and boredom. Vote Now at http://www.sourceforge.net/community/cca08
>>>>> ------------------------------------------------------------------------ 
>>>>>
>>>>>
>>>>> _______________________________________________
>>>>> Matplotlib-users mailing list
>>>>> Mat...@li...
>>>>> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>>>>> 
>>>>
>>>
>>
-- 
Michael Droettboom
Science Software Branch
Operations and Engineering Division
Space Telescope Science Institute
Operated by AURA for NASA

Michael Droettboom wrote:
> Which backend?
GTK, GTKAgg, TK, but not with any backend without a window: Agg, Cairo, 
PS, PDF, SVG ...
Cheers,
 Manuel
> Cheers,
> Mike
> 
> Manuel Metz wrote:
>> Michael Droettboom wrote:
>>> Thanks for the report. So we can diagnose this, what version of 
>>> matplotlib are you reporting this for?
>>>
>>> Also, you may be interested in the following FAQ (and the one 
>>> following it):
>>>
>>> http://matplotlib.sourceforge.net/faq.html#LEAKS
>>
>> Hi,
>>
>> I tested this with the lastest svn, and I do also see a leak. But 
>> it's not related to twinx, but to pylab.close(). If I just comment out 
>> this one line, the memleak disappears ...
>>
>> Manuel
>>
>>> Cheers,
>>> Mike
>>>
>>> laurent oget wrote:
>>>> i forgot two imports.
>>>>
>>>> import math
>>>> import gc
>>>> import pylab as PL
>>>>
>>>>
>>>> def looptest():
>>>> while(1):
>>>> fig=PL.figure(1)
>>>> ax=fig.add_subplot(211)
>>>> ax.set_position((0,0,0.9,0.45))
>>>> ax1=PL.twinx(ax)
>>>> t=range(1000)
>>>> st=[math.sin(x*0.01) for x in t]
>>>> ax.plot(t,st)
>>>> fig.clf()
>>>> PL.close(1)
>>>> gc.collect()
>>>> print "GC"
>>>> print len(gc.get_objects())
>>>> print len(gc.garbage)
>>>> looptest()
>>>> 2008年7月11日 laurent oget <la...@og... <mailto:la...@og...>>:
>>>>
>>>> I think i narrowed down the memory leak i have been chasing for a
>>>> while.
>>>> If i remove the call to twinx i get a slow leak, which would cause
>>>> me trouble
>>>> after a very long time. With the call to twinx, however i am
>>>> losing thousands of objects
>>>> at each loop.
>>>>
>>>> Thanks,
>>>>
>>>> Laurent
>>>>
>>>>
>>>> >>>>>>>>>>>>>>>>>>>>>>>>> import pylab as PL
>>>> def looptest():
>>>> while(1):
>>>> fig=PL.figure(1)
>>>> ax=fig.add_subplot(211)
>>>> ax.set_position((0,0,0.9,0.45))
>>>> ax1=PL.twinx(ax)
>>>> t=range(1000)
>>>> st=[math.sin(x*0.01) for x in t]
>>>> ax.plot(t,st)
>>>> fig.clf()
>>>> PL.close(1)
>>>> gc.collect()
>>>> print "GC"
>>>> print len(gc.get_objects())
>>>> print len(gc.garbage)
>>>> looptest()
>>>> >>>>>>>>>>>>>>>>>>>>>>>>>>>
>>>>
>>>>
>>>> ------------------------------------------------------------------------ 
>>>>
>>>>
>>>> ------------------------------------------------------------------------- 
>>>>
>>>> Sponsored by: SourceForge.net Community Choice Awards: VOTE NOW!
>>>> Studies have shown that voting for your favorite open source project,
>>>> along with a healthy diet, reduces your potential for chronic lameness
>>>> and boredom. Vote Now at http://www.sourceforge.net/community/cca08
>>>> ------------------------------------------------------------------------ 
>>>>
>>>>
>>>> _______________________________________________
>>>> Matplotlib-users mailing list
>>>> Mat...@li...
>>>> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>>>> 
>>>
>>
> 

Which backend?
Cheers,
Mike
Manuel Metz wrote:
> Michael Droettboom wrote:
>> Thanks for the report. So we can diagnose this, what version of 
>> matplotlib are you reporting this for?
>>
>> Also, you may be interested in the following FAQ (and the one 
>> following it):
>>
>> http://matplotlib.sourceforge.net/faq.html#LEAKS
>
> Hi,
>
> I tested this with the lastest svn, and I do also see a leak. But 
> it's not related to twinx, but to pylab.close(). If I just comment out 
> this one line, the memleak disappears ...
>
> Manuel
>
>> Cheers,
>> Mike
>>
>> laurent oget wrote:
>>> i forgot two imports.
>>>
>>> import math
>>> import gc
>>> import pylab as PL
>>>
>>>
>>> def looptest():
>>> while(1):
>>> fig=PL.figure(1)
>>> ax=fig.add_subplot(211)
>>> ax.set_position((0,0,0.9,0.45))
>>> ax1=PL.twinx(ax)
>>> t=range(1000)
>>> st=[math.sin(x*0.01) for x in t]
>>> ax.plot(t,st)
>>> fig.clf()
>>> PL.close(1)
>>> gc.collect()
>>> print "GC"
>>> print len(gc.get_objects())
>>> print len(gc.garbage)
>>> looptest()
>>> 2008年7月11日 laurent oget <la...@og... <mailto:la...@og...>>:
>>>
>>> I think i narrowed down the memory leak i have been chasing for a
>>> while.
>>> If i remove the call to twinx i get a slow leak, which would cause
>>> me trouble
>>> after a very long time. With the call to twinx, however i am
>>> losing thousands of objects
>>> at each loop.
>>>
>>> Thanks,
>>>
>>> Laurent
>>>
>>>
>>> >>>>>>>>>>>>>>>>>>>>>>>>> import pylab as PL
>>> def looptest():
>>> while(1):
>>> fig=PL.figure(1)
>>> ax=fig.add_subplot(211)
>>> ax.set_position((0,0,0.9,0.45))
>>> ax1=PL.twinx(ax)
>>> t=range(1000)
>>> st=[math.sin(x*0.01) for x in t]
>>> ax.plot(t,st)
>>> fig.clf()
>>> PL.close(1)
>>> gc.collect()
>>> print "GC"
>>> print len(gc.get_objects())
>>> print len(gc.garbage)
>>> looptest()
>>> >>>>>>>>>>>>>>>>>>>>>>>>>>>
>>>
>>>
>>> ------------------------------------------------------------------------ 
>>>
>>>
>>> ------------------------------------------------------------------------- 
>>>
>>> Sponsored by: SourceForge.net Community Choice Awards: VOTE NOW!
>>> Studies have shown that voting for your favorite open source project,
>>> along with a healthy diet, reduces your potential for chronic lameness
>>> and boredom. Vote Now at http://www.sourceforge.net/community/cca08
>>> ------------------------------------------------------------------------ 
>>>
>>>
>>> _______________________________________________
>>> Matplotlib-users mailing list
>>> Mat...@li...
>>> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>>> 
>>
>
-- 
Michael Droettboom
Science Software Branch
Operations and Engineering Division
Space Telescope Science Institute
Operated by AURA for NASA

Michael Droettboom wrote:
> Thanks for the report. So we can diagnose this, what version of 
> matplotlib are you reporting this for?
> 
> Also, you may be interested in the following FAQ (and the one following it):
> 
> http://matplotlib.sourceforge.net/faq.html#LEAKS
Hi,
 I tested this with the lastest svn, and I do also see a leak. But 
it's not related to twinx, but to pylab.close(). If I just comment out 
this one line, the memleak disappears ...
Manuel
> Cheers,
> Mike
> 
> laurent oget wrote:
>> i forgot two imports.
>>
>> import math
>> import gc
>> import pylab as PL
>>
>>
>> def looptest():
>> while(1):
>> fig=PL.figure(1)
>> ax=fig.add_subplot(211)
>> ax.set_position((0,0,0.9,0.45))
>> ax1=PL.twinx(ax)
>> t=range(1000)
>> st=[math.sin(x*0.01) for x in t]
>> ax.plot(t,st)
>> fig.clf()
>> PL.close(1)
>> gc.collect()
>> print "GC"
>> print len(gc.get_objects())
>> print len(gc.garbage)
>> looptest()
>> 2008年7月11日 laurent oget <la...@og... <mailto:la...@og...>>:
>>
>> I think i narrowed down the memory leak i have been chasing for a
>> while.
>> If i remove the call to twinx i get a slow leak, which would cause
>> me trouble
>> after a very long time. With the call to twinx, however i am
>> losing thousands of objects
>> at each loop.
>>
>> Thanks,
>>
>> Laurent
>>
>>
>> >>>>>>>>>>>>>>>>>>>>>>>>> 
>> import pylab as PL
>> def looptest():
>> while(1):
>> fig=PL.figure(1)
>> ax=fig.add_subplot(211)
>> ax.set_position((0,0,0.9,0.45))
>> ax1=PL.twinx(ax)
>> t=range(1000)
>> st=[math.sin(x*0.01) for x in t]
>> ax.plot(t,st)
>> fig.clf()
>> PL.close(1)
>> gc.collect()
>> print "GC"
>> print len(gc.get_objects())
>> print len(gc.garbage)
>> looptest()
>> >>>>>>>>>>>>>>>>>>>>>>>>>>>
>>
>>
>> ------------------------------------------------------------------------
>>
>> -------------------------------------------------------------------------
>> Sponsored by: SourceForge.net Community Choice Awards: VOTE NOW!
>> Studies have shown that voting for your favorite open source project,
>> along with a healthy diet, reduces your potential for chronic lameness
>> and boredom. Vote Now at http://www.sourceforge.net/community/cca08
>> ------------------------------------------------------------------------
>>
>> _______________________________________________
>> Matplotlib-users mailing list
>> Mat...@li...
>> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>> 
> 

Hello Jeff,
> Timme: Here's one way to do it
many thanks so far. I still have to inspect and improve my script. But 
at least your code lead me to some contourd surface.
I will come back and tell if it worked. Unfortunately I cannot disclose 
the data nor the results because of copyright issues.
So this mail is just to thank you for your responsiveness to my questions.
Timmie

I have an application that has 2 subplots, and I'd like to use the second to
display the legend for the first. For example, for subplots plot1 and plot2:
plot1.plot((1,2,3,4), label='up', color = 'g')
plot1.plot((4,3,2,1), label='down', color = 'b')
plot2.legend("What can I put here to display a legend for plot1 on
plot2???")
Can this be done?
Thanks,
Dave

Tim Michelsen wrote:
> Hello,
> thanks.
> I checked again from contour_demo.py of the basemap distribution.
>
> There lats, lons are uniquely monoton increasing from 0-360 and from -90 to 90.
> In my case data is written row-by-row:
> * increasing from lowest latitude western most longitude to easternmost
> longitude and then increasing each rows in the same manner to the northermost
> latitude (see below). 
>
> So, as you said, it's a question of re-aranging the data. that it fits the to
> the way m.contour expects the 2-D array.
> Also, since the grid is still coarse, I would need to apply some smoothing
> afterwards. What do you recommend for that?
> 
Timme: Here's one way to do it
from matplotlib.mlab import load
import matplotlib.pyplot as plt
import numpy as np
data = load("data.txt")
# need to know nlons and nlats beforehand!
nlons = 8; nlats = 25
X = data[0::nlats,0]
Y = data[0:nlats,1]
# data is in nlons,nlats order in file, need to transpose
Z = data[:,2].reshape(nlons,nlats).transpose()
X,Y = np.meshgrid(X,Y)
CS = plt.contourf(X,Y,Z,20)
plt.show()
I don't have any recommendations for smoothing - why don't you plot the 
raw data first and see if you really need it? 
> I don't know how I can do this easily by hand. May you give me some guidance
> here, please?
>
> But I may just convert it to a shape file using GIS then load it with the
> shapefile interface you wrote.
> What would you see as most convenient way?
> If I produce maps with a GIS but want to use matplotlib for the map plotting,
> what would be the preferred export format? Any gdal format?
> 
I prefer netCDF format for gridded data (basemap contains a function for 
reading netCDF files - NetCDFFile).
-Jeff
> Many thanks in advance,
> Timmie
>
> ### data example
>
> Latitude	Longitude	value
> 45	7	7.65251434
> 45	7.25	6.841345477
> 45	7.5	3.923153289
> 45	7.75	3.644313708
> 45	8	3.550977951
> 45	8.25	3.352525137
> 45	8.5	3.080082094
> 45	8.75	2.971992657
> 45	9	2.998723785
> 45	9.25	3.080082094
> 45	9.5	3.185687405
> 45	9.75	3.102075854
> 45	10	3.185687405
> 45	10.25	3.213960325
> 45	10.5	3.32326373
> 45	10.75	3.465643983
> 45	11	3.612980369
> 45	11.25	3.644313708
> 45	11.5	3.701277511
> 45	11.75	3.923153289
> 45	12	3.797848342
> 45	12.25	3.612980369
> 45	12.5	3.435577844
> 45	12.75	3.294210812
> 45	13	3.26536503
> 45.25	7	6.485050223
> 45.25	7.25	6.343081631
> 45.25	7.5	3.856783573
> 45.25	7.75	3.405725407
> 45.25	8	3.550977951
> 45.25	8.25	3.294210812
> 45.25	8.5	3.294210812
> 45.25	8.75	3.185687405
> 45.25	9	3.15761656
> 45.25	9.25	3.213960325
> 45.25	9.5	3.15761656
> 45.25	9.75	3.32326373
> 45.25	10	3.405725407
> 45.25	10.25	3.495925216
> 45.25	10.5	3.465643983
> 45.25	10.75	3.550977951
> 45.25	11	3.465643983
> 45.25	11.25	3.765429652
> 45.25	11.5	3.95669157
> 45.25	11.75	3.797848342
> 45.25	12	3.923153289
> 45.25	12.25	3.733239867
> 45.25	12.5	3.550977951
> 45.25	12.75	3.520306012
> 45.25	13	3.376085288
> 45.5	7	6.383367092
> 45.5	7.25	6.383367092
> 45.5	7.5	6.009422688
> 45.5	7.75	4.679469855
> 45.5	8	3.435577844
> 45.5	8.25	3.435577844
> 45.5	8.5	3.236725042
> 45.5	8.75	3.236725042
> 45.5	9	3.185687405
> 45.5	9.25	3.102075854
> 45.5	9.5	3.102075854
> 45.5	9.75	3.185687405
> 45.5	10	3.352525137
> 45.5	10.25	3.405725407
> 45.5	10.5	3.376085288
> 45.5	10.75	3.612980369
> 45.5	11	3.520306012
> 45.5	11.25	3.352525137
> 45.5	11.5	3.823949103
> 45.5	11.75	3.856783573
> 45.5	12	3.856783573
> 45.5	12.25	3.765429652
> 45.5	12.5	3.669541114
> 45.5	12.75	3.550977951
> 45.5	13	3.435577844
> 45.75	7	5.309043916
> 45.75	7.25	6.057519881
> 45.75	7.5	5.030958443
> 45.75	7.75	4.836570243
> 45.75	8	4.836570243
> 45.75	8.25	2.724965001
> 45.75	8.5	2.607751091
> 45.75	8.75	3.26536503
> 45.75	9	2.898163214
> 45.75	9.25	2.872155245
> 45.75	9.5	1.893252754
> 45.75	9.75	2.043669061
> 45.75	10	1.75488883
> 45.75	10.25	2.004264146
> 45.75	10.5	2.971992657
> 45.75	10.75	1.804949998
> 45.75	11	2.846334614
> 45.75	11.25	5.519419657
> 45.75	11.5	2.517818813
> 45.75	11.75	3.733239867
> 45.75	12	3.376085288
> 45.75	12.25	3.550977951
> 45.75	12.5	3.612980369
> 45.75	12.75	3.520306012
> 45.75	13	3.495925216
> 46	7	5.06399168
> 46	7.25	4.949174095
> 46	7.5	5.266087828
> 46	7.75	5.352298328
> 46	8	4.757472437
> 46	8.25	2.800325674
> 46	8.5	3.612980369
> 46	8.75	3.185687405
> 46	9	2.323282473
> 46	9.25	1.671485743
> 46	9.5	3.856783573
> 46	9.75	4.572079662
> 46	10	4.679469855
> 46	10.25	4.679469855
> 46	10.5	5.309043916
> 46	10.75	3.294210812
> 46	11	3.405725407
> 46	11.25	3.669541114
> 46	11.5	3.495925216
> 46	11.75	4.255093726
> 46	12	3.495925216
> 46	12.25	3.185687405
> 46	12.5	3.213960325
> 46	12.75	3.550977951
> 46	13	3.520306012
> 46.25	7	1.969297411
> 46.25	7.25	4.908706364
> 46.25	7.5	3.052767233
> 46.25	7.75	3.765429652
> 46.25	8	3.95669157
> 46.25	8.25	5.06399168
> 46.25	8.5	5.266087828
> 46.25	8.75	3.669541114
> 46.25	9	3.185687405
> 46.25	9.25	3.797848342
> 46.25	9.5	3.352525137
> 46.25	9.75	5.439709782
> 46.25	10	5.69098301
> 46.25	10.25	4.949174095
> 46.25	10.5	5.736883145
> 46.25	10.75	5.105542055
> 46.25	11	4.255093726
> 46.25	11.25	3.701277511
> 46.25	11.5	4.255093726
> 46.25	11.75	4.572079662
> 46.25	12	3.98369323
> 46.25	12.25	4.148941623
> 46.25	12.5	3.129746478
> 46.25	12.75	3.236725042
> 46.25	13	3.550977951
> 46.5	7	2.872155245
> 46.5	7.25	3.701277511
> 46.5	7.5	3.15761656
> 46.5	7.75	3.765429652
> 46.5	8	5.18951259
> 46.5	8.25	6.105948261
> 46.5	8.5	5.266087828
> 46.5	8.75	5.69098301
> 46.5	9	6.009422688
> 46.5	9.25	5.147381739
> 46.5	9.5	5.829636932
> 46.5	9.75	5.654489904
> 46.5	10	6.243327668
> 46.5	10.25	5.395852976
> 46.5	10.5	5.736883145
> 46.5	10.75	6.057519881
> 46.5	11	5.147381739
> 46.5	11.25	3.520306012
> 46.5	11.5	3.856783573
> 46.5	11.75	4.148941623
> 46.5	12	4.71833512
> 46.5	12.25	4.71833512
> 46.5	12.5	3.701277511
> 46.5	12.75	3.889851131
> 46.5	13	3.32326373
> 46.75	7	1.859766825
> 46.75	7.25	2.198852355
> 46.75	7.5	2.345277833
> 46.75	7.75	2.517818813
> 46.75	8	3.856783573
> 46.75	8.25	3.856783573
> 46.75	8.5	5.06399168
> 46.75	8.75	4.184077131
> 46.75	9	5.829636932
> 46.75	9.25	3.644313708
> 46.75	9.5	3.765429652
> 46.75	9.75	5.309043916
> 46.75	10	6.009422688
> 46.75	10.25	5.147381739
> 46.75	10.5	5.609155594
> 46.75	10.75	5.783100444
> 46.75	11	5.147381739
> 46.75	11.25	3.581868928
> 46.75	11.5	4.908706364
> 46.75	11.75	3.465643983
> 46.75	12	3.465643983
> 46.75	12.25	4.148941623
> 46.75	12.5	3.98369323
> 46.75	12.75	3.581868928
> 46.75	13	3.644313708
>
>
> -------------------------------------------------------------------------
> Sponsored by: SourceForge.net Community Choice Awards: VOTE NOW!
> Studies have shown that voting for your favorite open source project,
> along with a healthy diet, reduces your potential for chronic lameness
> and boredom. Vote Now at http://www.sourceforge.net/community/cca08
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
> 
-- 
Jeffrey S. Whitaker Phone : (303)497-6313
Meteorologist FAX : (303)497-6449
NOAA/OAR/PSD R/PSD1 Email : Jef...@no...
325 Broadway Office : Skaggs Research Cntr 1D-113
Boulder, CO, USA 80303-3328 Web : http://tinyurl.com/5telg

On Mon, Jul 14, 2008 at 9:49 AM, Ben Axelrod <bax...@co...> wrote:
> The RectangleSelector has parameters for the min span in the x and y
> directions of the rectangle. The units of these are the axes units. It
> would be nice if there was an additional similar min size requirement, but
> in units of pixels. This way it would be independent of the axes scale.
Good idea -- I added this to svn r5769. Here is some example usage:
LS = RectangleSelector(current_ax, line_select_callback,
 drawtype='box',useblit=True,
 minspanx=5,minspany=5,spancoords='pixels')

Hello,
thanks.
I checked again from contour_demo.py of the basemap distribution.
There lats, lons are uniquely monoton increasing from 0-360 and from -90 to 90.
In my case data is written row-by-row:
* increasing from lowest latitude western most longitude to easternmost
longitude and then increasing each rows in the same manner to the northermost
latitude (see below). 
So, as you said, it's a question of re-aranging the data. that it fits the to
the way m.contour expects the 2-D array.
Also, since the grid is still coarse, I would need to apply some smoothing
afterwards. What do you recommend for that?
I don't know how I can do this easily by hand. May you give me some guidance
here, please?
But I may just convert it to a shape file using GIS then load it with the
shapefile interface you wrote.
What would you see as most convenient way?
If I produce maps with a GIS but want to use matplotlib for the map plotting,
what would be the preferred export format? Any gdal format?
Many thanks in advance,
Timmie
### data example
Latitude	Longitude	value
45	7	7.65251434
45	7.25	6.841345477
45	7.5	3.923153289
45	7.75	3.644313708
45	8	3.550977951
45	8.25	3.352525137
45	8.5	3.080082094
45	8.75	2.971992657
45	9	2.998723785
45	9.25	3.080082094
45	9.5	3.185687405
45	9.75	3.102075854
45	10	3.185687405
45	10.25	3.213960325
45	10.5	3.32326373
45	10.75	3.465643983
45	11	3.612980369
45	11.25	3.644313708
45	11.5	3.701277511
45	11.75	3.923153289
45	12	3.797848342
45	12.25	3.612980369
45	12.5	3.435577844
45	12.75	3.294210812
45	13	3.26536503
45.25	7	6.485050223
45.25	7.25	6.343081631
45.25	7.5	3.856783573
45.25	7.75	3.405725407
45.25	8	3.550977951
45.25	8.25	3.294210812
45.25	8.5	3.294210812
45.25	8.75	3.185687405
45.25	9	3.15761656
45.25	9.25	3.213960325
45.25	9.5	3.15761656
45.25	9.75	3.32326373
45.25	10	3.405725407
45.25	10.25	3.495925216
45.25	10.5	3.465643983
45.25	10.75	3.550977951
45.25	11	3.465643983
45.25	11.25	3.765429652
45.25	11.5	3.95669157
45.25	11.75	3.797848342
45.25	12	3.923153289
45.25	12.25	3.733239867
45.25	12.5	3.550977951
45.25	12.75	3.520306012
45.25	13	3.376085288
45.5	7	6.383367092
45.5	7.25	6.383367092
45.5	7.5	6.009422688
45.5	7.75	4.679469855
45.5	8	3.435577844
45.5	8.25	3.435577844
45.5	8.5	3.236725042
45.5	8.75	3.236725042
45.5	9	3.185687405
45.5	9.25	3.102075854
45.5	9.5	3.102075854
45.5	9.75	3.185687405
45.5	10	3.352525137
45.5	10.25	3.405725407
45.5	10.5	3.376085288
45.5	10.75	3.612980369
45.5	11	3.520306012
45.5	11.25	3.352525137
45.5	11.5	3.823949103
45.5	11.75	3.856783573
45.5	12	3.856783573
45.5	12.25	3.765429652
45.5	12.5	3.669541114
45.5	12.75	3.550977951
45.5	13	3.435577844
45.75	7	5.309043916
45.75	7.25	6.057519881
45.75	7.5	5.030958443
45.75	7.75	4.836570243
45.75	8	4.836570243
45.75	8.25	2.724965001
45.75	8.5	2.607751091
45.75	8.75	3.26536503
45.75	9	2.898163214
45.75	9.25	2.872155245
45.75	9.5	1.893252754
45.75	9.75	2.043669061
45.75	10	1.75488883
45.75	10.25	2.004264146
45.75	10.5	2.971992657
45.75	10.75	1.804949998
45.75	11	2.846334614
45.75	11.25	5.519419657
45.75	11.5	2.517818813
45.75	11.75	3.733239867
45.75	12	3.376085288
45.75	12.25	3.550977951
45.75	12.5	3.612980369
45.75	12.75	3.520306012
45.75	13	3.495925216
46	7	5.06399168
46	7.25	4.949174095
46	7.5	5.266087828
46	7.75	5.352298328
46	8	4.757472437
46	8.25	2.800325674
46	8.5	3.612980369
46	8.75	3.185687405
46	9	2.323282473
46	9.25	1.671485743
46	9.5	3.856783573
46	9.75	4.572079662
46	10	4.679469855
46	10.25	4.679469855
46	10.5	5.309043916
46	10.75	3.294210812
46	11	3.405725407
46	11.25	3.669541114
46	11.5	3.495925216
46	11.75	4.255093726
46	12	3.495925216
46	12.25	3.185687405
46	12.5	3.213960325
46	12.75	3.550977951
46	13	3.520306012
46.25	7	1.969297411
46.25	7.25	4.908706364
46.25	7.5	3.052767233
46.25	7.75	3.765429652
46.25	8	3.95669157
46.25	8.25	5.06399168
46.25	8.5	5.266087828
46.25	8.75	3.669541114
46.25	9	3.185687405
46.25	9.25	3.797848342
46.25	9.5	3.352525137
46.25	9.75	5.439709782
46.25	10	5.69098301
46.25	10.25	4.949174095
46.25	10.5	5.736883145
46.25	10.75	5.105542055
46.25	11	4.255093726
46.25	11.25	3.701277511
46.25	11.5	4.255093726
46.25	11.75	4.572079662
46.25	12	3.98369323
46.25	12.25	4.148941623
46.25	12.5	3.129746478
46.25	12.75	3.236725042
46.25	13	3.550977951
46.5	7	2.872155245
46.5	7.25	3.701277511
46.5	7.5	3.15761656
46.5	7.75	3.765429652
46.5	8	5.18951259
46.5	8.25	6.105948261
46.5	8.5	5.266087828
46.5	8.75	5.69098301
46.5	9	6.009422688
46.5	9.25	5.147381739
46.5	9.5	5.829636932
46.5	9.75	5.654489904
46.5	10	6.243327668
46.5	10.25	5.395852976
46.5	10.5	5.736883145
46.5	10.75	6.057519881
46.5	11	5.147381739
46.5	11.25	3.520306012
46.5	11.5	3.856783573
46.5	11.75	4.148941623
46.5	12	4.71833512
46.5	12.25	4.71833512
46.5	12.5	3.701277511
46.5	12.75	3.889851131
46.5	13	3.32326373
46.75	7	1.859766825
46.75	7.25	2.198852355
46.75	7.5	2.345277833
46.75	7.75	2.517818813
46.75	8	3.856783573
46.75	8.25	3.856783573
46.75	8.5	5.06399168
46.75	8.75	4.184077131
46.75	9	5.829636932
46.75	9.25	3.644313708
46.75	9.5	3.765429652
46.75	9.75	5.309043916
46.75	10	6.009422688
46.75	10.25	5.147381739
46.75	10.5	5.609155594
46.75	10.75	5.783100444
46.75	11	5.147381739
46.75	11.25	3.581868928
46.75	11.5	4.908706364
46.75	11.75	3.465643983
46.75	12	3.465643983
46.75	12.25	4.148941623
46.75	12.5	3.98369323
46.75	12.75	3.581868928
46.75	13	3.644313708

The RectangleSelector has parameters for the min span in the x and y directions of the rectangle. The units of these are the axes units. It would be nice if there was an additional similar min size requirement, but in units of pixels. This way it would be independent of the axes scale.
Thanks,
-Ben

On Thu, Jul 10, 2008 at 6:42 AM, Angela Rivera Campos <riv...@in...> wrote:
> Hi,
>
> I'm quite a newbie on matplotlib.
>
> I'm trying to get some data from a file. I've got a function that reads
> the data from the file and stores it in a tuple as a set of floats. When
> I use this without importing pylab it just go well but when I do it
> after importing this module there's a rounding and I don't get the
> proper data:
My guess is there is something triggered by a pylab
numpy/numerix/Numeric import, but w/o kjnowing more about your
matplotlib and other software versions is it difficult to guess.
Could you put these lines into a test script and run them with
> python myscript.py --verbose-debug
and paste the output.
Florian Koelling recently reported a problem that sounded very similar
under the heading "mad interference between matplotlib and openbabel".
 Apparently some pylab import is doing something funky with some third
party libs.
Could you test just the numpy imports to see if that makes a
difference. Ie, instead of importing pylab before your module code,
do the following:
import numpy
import numpy.fft
importnumpy.random
import numpy.linalg
and let us know if you see similar problems.
JDH
JDH

Tim Michelsen wrote:
> Hello Jeff, 
>
> 
>>>> - Points stored in the above descripbed format (lat, lon, value)?
>>>> 
> This one I solved using a m.scatter() function
>
> 
>>>> 	- Interpolate a grid of data points by using different interpolation
>>>> methods like inverse distance wheighting, natural neighbor 
>>>> 
> interpolation, etc. to get a contour map?
> 
>>> For interpolation of irregular, randomly distributed data points see
>>> http://www.scipy.org/Cookbook/Matplotlib/
>>> 
> Gridding_irregularly_spaced_data.
> 
>>> However, if there is some structure to the data grid then it's probably
>>> better not to use these approaches.
>>> 
> The problem is that although regular spaced the grid is still to large to
> countour to a nice map. I will play a bit more with contour and other
> interpolation functions.
>
> I tried griddata:
>
> 
>> 2) using the griddata package
>> Here I was nearly without orientation how to call griddata correctly.
>> 
> I tried again.
>
> Here is what I got:
> x = data[:,1]
> y = data[:,0]
> z = data[:,2]
> X, Y = mlab.meshgrid(x, y)
> X, Z = mlab.meshgrid(x, y)
> # zi = griddata(x,y,z,xi,yi,**kwargs)
> Z = grid.griddata(x,y,z, X, Y)
> 	plt.contour(X,Y, Z)
> 	
> => ValueError: output grid defined by xi,yi must be monotone increasing
>
> The coordinates are stored in a way that first longitude (x) increases and
> then the latitude (y) increases.
> 10 6.0 4
> 10 6.25 3
> 10 6.50 2
> 10 6.75 1
> 10 6.0 6
> 11 6.25 7
> 11 6.50 6
> 11 6.75 9
> 12 6.0 4
>
> What how do I need to arrange my data to get it monotone increasing for
> griddata?
>
> Thanks for your help. One settled I will send you another example for the
> examples package.
> 
> Kind regards,
> Timmie
>
> 
Timmie: You shouldn't use griddata. You have a regular lat/lon grid, 
so it's just a matter of loading the data into the proper 2-d array. 
Please send a self-contained script (and post the data somewhere) and 
then we can help you.
-Jeff
-- 
Jeffrey S. Whitaker Phone : (303)497-6313
NOAA/OAR/CDC R/PSD1 FAX : (303)497-6449
325 Broadway Boulder, CO, USA 80305-3328

Le lundi 14 juillet 2008 à 09:08 -0400, Michael Droettboom a écrit :
> Do any other developers have better suggestions? We may have to do some 
> magic in at drawing time (or convince the autoscaler to ignore the stem 
> lines) -- but I'd like to find a way that minimizes additional complexity.
> 
Is it possible do proceed as using axvline, i.e. specifying the axes
coordinates? In this case, it would require a mix of data coordinate
(the value to display) and of axes coordinate (the bottom of the curve).
When would values to render be converted ?
-- 
Fabrice Silva <si...@lm...>
LMA UPR CNRS 7051 - équipe S2M

Hello Jeff, 
> >> - Points stored in the above descripbed format (lat, lon, value)?
This one I solved using a m.scatter() function
> >> 	- Interpolate a grid of data points by using different interpolation
>>> methods like inverse distance wheighting, natural neighbor 
interpolation, etc. to get a contour map?
> > For interpolation of irregular, randomly distributed data points see
> > http://www.scipy.org/Cookbook/Matplotlib/
Gridding_irregularly_spaced_data.
> >
> > However, if there is some structure to the data grid then it's probably
> > better not to use these approaches.
The problem is that although regular spaced the grid is still to large to
countour to a nice map. I will play a bit more with contour and other
interpolation functions.
I tried griddata:
> 2) using the griddata package
> Here I was nearly without orientation how to call griddata correctly.
I tried again.
Here is what I got:
 x = data[:,1]
 y = data[:,0]
 z = data[:,2]
 X, Y = mlab.meshgrid(x, y)
 X, Z = mlab.meshgrid(x, y)
 # zi = griddata(x,y,z,xi,yi,**kwargs)
 Z = grid.griddata(x,y,z, X, Y)
	plt.contour(X,Y, Z)
	
=> ValueError: output grid defined by xi,yi must be monotone increasing
The coordinates are stored in a way that first longitude (x) increases and
then the latitude (y) increases.
10 6.0 4
10 6.25 3
10 6.50 2
10 6.75 1
10 6.0 6
11 6.25 7
11 6.50 6
11 6.75 9
12 6.0 4
What how do I need to arrange my data to get it monotone increasing for
griddata?
Thanks for your help. One settled I will send you another example for the
examples package.
 
Kind regards,
Timmie

This is a tricky one. It appears this bug also exists in 0.91.x, 
perhaps earlier as well, so it isn't a regression.
I don't like the idea of setting the minimum to "1", especially for when 
the scale isn't log. Setting it to a really small positive value (like 
1e-9) is better, but the autoscaling of the log plot then goes down to 
1e-9 as well.
Do any other developers have better suggestions? We may have to do some 
magic in at drawing time (or convince the autoscaler to ignore the stem 
lines) -- but I'd like to find a way that minimizes additional complexity.
Cheers,
Mike
Dir...@in... wrote:
> Hello,
>
> I tried to do a stem plot on an Axes with logarithmic scale, experiencing that the stemlines were not drawn...
>
>
> ====axes.py (svn trunk, 2008年07月08日):
> def stem(self, x, y, linefmt='b-', markerfmt='bo', basefmt='r-'):
> [...]
> stemlines = []
> for thisx, thisy in zip(x, y):
> l, = self.plot([thisx,thisx], [0, thisy], linefmt)
> stemlines.append(l)
>
>
> After Axes.set_yscale('log'), Axes.stem() no longer draws the stemlines because it tries to draw from y=0, thereby apparently triggering some guard which prevents plotting log(0), hence suppressing the complete stemline.
>
> Dirty hacking, I replace [0, thisy] with [1, thisy] in case yscale=='log' and it works for my plots, but for sure there is more to do to make it robust...
>
> Please give me a heads-up if I just oversaw the Right Way to do a stem plot with logarithmic yscale.
>
> Cheers,
> Dirk
>
> -------------------------------------------------------------------------
> Sponsored by: SourceForge.net Community Choice Awards: VOTE NOW!
> Studies have shown that voting for your favorite open source project,
> along with a healthy diet, reduces your potential for chronic lameness
> and boredom. Vote Now at http://www.sourceforge.net/community/cca08
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
> 
-- 
Michael Droettboom
Science Software Branch
Operations and Engineering Division
Space Telescope Science Institute
Operated by AURA for NASA

I'm surprised. That works for me. Can you (again) set verbose.level to 
"debug-annoying" and send the output?
Cheers,
Mike
David M. Kaplan wrote:
> Hi,
>
> Thanks for the suggestions. I have stopped using the usetex option. To
> make math and normal text match, I tried the following:
>
> rcParams['font.family'] = 'serif'
> rcParams['mathtext.fontset'] = 'stix'
>
> This didn't make them match - normal text looked to me like it was still
> sans-serif, while mathtext was with serif. Is there something else I
> should be doing to make this happen?
>
> Thanks again for your help.
>
> Cheers,
> David
>
>
> On Thu, 2008年07月10日 at 11:52 -0400, Darren Dale wrote:
> 
>> Hi David,
>>
>> On Thursday 10 July 2008 11:15:37 am David M. Kaplan wrote:
>> 
>>> 2) I have noticed that the font used for the xticklabels and the font
>>> used for the xlabel and contour labels appears to be different (example
>>> attached). One appears to be serif and the other sans-serif. This
>>> seems to be due to using tex for text rendering. I am not sure if this
>>> also occurred before the update, but I didn't notice it previously.
>>> 
>> It has always been this way. We tried a workaround once a couple years back 
>> and it turned into a real mess.
>>
>> 
>>> Looking at the properties of the different text objects, it isn't
>>> apparent that there should be a difference - both have font properties
>>> that indicate sans-serif, but the text of tick labels appears to be
>>> surrounded by $'s forcing it through the text parser, while that of the
>>> contour labels is not. Is this difference normal or expected? Is there
>>> a way around this? In particular, I would like to use sans-serif for
>>> everything - is this possible while still using tex?
>>> 
>> I think there is a package, sansmath or something like that, that will allow 
>> latex to use sans-serif fonts in math mode. You could try adding it to the 
>> text.latex.preamble rc setting, but that option is not officially supported.
>>
>> If you don't like the limitations of latex, you might want to turning off 
>> usetex and just use matplotlibs mathtext, which recently got a significant 
>> rewrite and is now quite capable thanks to Mike Droettboom. Here's some 
>> documentation too:
>> http://matplotlib.sourceforge.net/doc/html/users/mathtext.html
>>
>> Darren
>> 
-- 
Michael Droettboom
Science Software Branch
Operations and Engineering Division
Space Telescope Science Institute
Operated by AURA for NASA

Jeff Whitaker wrote:
> Tim Michelsen wrote:
> 
>> Dear Matplotlib-Users,
>> I am tryring to create a contour plot over a basemap.
>>
>> My main problem is creating the array for the Z values as a basis for the
>> plt.contour command from a CSV file where latitude, longitude and value are
>> stored column-wise:
>>
>> lat;	lon;	value
>> 50;	10;	6
>> ...
>>
>> The data represents a regular spaced grid with a datapoint each 0.25 degrees.
>>
>> I tried various possibilities but didn't have success:
>>
>> 1) following simpletest.py from the basemap examples:
>> X, Y = meshgrid(data[:,1], data[:,0])
>>
>> Z = data[:,2]
>> 
>> 
> Timmie: Try:
>
> X, Y = meshgrid(data[:,1], data[:,0])
> Z = data[:,2]
> nlons = X.shape[1]; nlats = X.shape[0]
> Z = Z.reshape(nlats,nlons)
> 
Timmie: Sorry, but upon further reflection I don't think this will 
work. You'll need to know the number of
lats and the number of lons on the grid beforehand. Then you should be 
able to do
X = X.reshape(nlats,nlons)
Y = Y.reshape(nlats,nlons)
Z = Z.reshape(nlats,nlons)
after reading the data in.
(skip the meshgrid call, that's only useful when X is a vector with length nlons and Y is a vector with length nlats).
If you still have problems, send us a full example.
-Jeff
> 
>> m.contourf(x,y, Z)
>>
>> => Error: Z must be a 2D array
>> -> How do I get Z to be a 2D array?
>>
>> 2) using the griddata package
>> Here I was nearly without orientation how to call griddata correctly.
>> 
>> 
> You don't need to use griddata since you have regularly gridded data.
> 
>> 3) Using the python bindings of ogr
>> Any examples on this one?
>> 
>> 
> Again, no need. A simple reshape will get you the 2d lat/lon array you 
> need.
>
> 
>> >From my above demonstrated methods the following questions arrise:
>> What is the preferred way to plot
>> 	- Points stored in the above descripbed format (lat, lon, value)?
>> 	- Interpolate a grid of data points by using different interpolation methods
>> like inverse distance wheighting, natural neighbor interpolation, etc. to get a
>> contour map?
>> 
>> 
>
> For interpolation of irregular, randomly distributed data points see
> http://www.scipy.org/Cookbook/Matplotlib/Gridding_irregularly_spaced_data.
>
> However, if there is some structure to the data grid then it's probably 
> better not to use these approaches.
>
> -Jeff
>
>
>
> 
-- 
Jeffrey S. Whitaker Phone : (303)497-6313
NOAA/OAR/CDC R/PSD1 FAX : (303)497-6449
325 Broadway Boulder, CO, USA 80305-3328

Tim Michelsen wrote:
> Dear Matplotlib-Users,
> I am tryring to create a contour plot over a basemap.
>
> My main problem is creating the array for the Z values as a basis for the
> plt.contour command from a CSV file where latitude, longitude and value are
> stored column-wise:
>
> lat;	lon;	value
> 50;	10;	6
> ...
>
> The data represents a regular spaced grid with a datapoint each 0.25 degrees.
>
> I tried various possibilities but didn't have success:
>
> 1) following simpletest.py from the basemap examples:
> X, Y = meshgrid(data[:,1], data[:,0])
>
> Z = data[:,2]
> 
Timmie: Try:
X, Y = meshgrid(data[:,1], data[:,0])
Z = data[:,2]
nlons = X.shape[1]; nlats = X.shape[0]
Z = Z.reshape(nlats,nlons)
> m.contourf(x,y, Z)
>
> => Error: Z must be a 2D array
> -> How do I get Z to be a 2D array?
>
> 2) using the griddata package
> Here I was nearly without orientation how to call griddata correctly.
> 
You don't need to use griddata since you have regularly gridded data.
>
> 3) Using the python bindings of ogr
> Any examples on this one?
> 
Again, no need. A simple reshape will get you the 2d lat/lon array you 
need.
> >From my above demonstrated methods the following questions arrise:
> What is the preferred way to plot
> 	- Points stored in the above descripbed format (lat, lon, value)?
> 	- Interpolate a grid of data points by using different interpolation methods
> like inverse distance wheighting, natural neighbor interpolation, etc. to get a
> contour map?
> 
For interpolation of irregular, randomly distributed data points see
http://www.scipy.org/Cookbook/Matplotlib/Gridding_irregularly_spaced_data.
However, if there is some structure to the data grid then it's probably 
better not to use these approaches.
-Jeff
-- 
Jeffrey S. Whitaker Phone : (303)497-6313
NOAA/OAR/CDC R/PSD1 FAX : (303)497-6449
325 Broadway Boulder, CO, USA 80305-3328

Dear Matplotlib-Users,
I am tryring to create a contour plot over a basemap.
My main problem is creating the array for the Z values as a basis for the
plt.contour command from a CSV file where latitude, longitude and value are
stored column-wise:
lat;	lon;	value
50;	10;	6
...
The data represents a regular spaced grid with a datapoint each 0.25 degrees.
I tried various possibilities but didn't have success:
1) following simpletest.py from the basemap examples:
X, Y = meshgrid(data[:,1], data[:,0])
Z = data[:,2]
m.contourf(x,y, Z)
=> Error: Z must be a 2D array
-> How do I get Z to be a 2D array?
2) using the griddata package
Here I was nearly without orientation how to call griddata correctly.
3) Using the python bindings of ogr
Any examples on this one?
>From my above demonstrated methods the following questions arrise:
What is the preferred way to plot
	- Points stored in the above descripbed format (lat, lon, value)?
	- Interpolate a grid of data points by using different interpolation methods
like inverse distance wheighting, natural neighbor interpolation, etc. to get a
contour map?
Thanks in advance for your help & kind regards,
Timmie

> Try
> 
> import pylab
> 
> instead of
> 
> from pylab import *
> 
> Manuel
> 
I've already tried using
	
	import pylab
and also just importing the functions that I'm using, but the result is 
always the same.
AR

I've been trying to build and install matplotlib now for a few days, and 
have met with no success. I'm running OSX 10.5.4 on a MacBook Pro, 
Python 2.5.2 from MacPython, gcc 4.0.1, gfortran 4.3.0, and the latest 
version of Apple's XCode (3.1, which, if I understand correctly, has the 
necessary libpng and freetype libraries). I've got numpy 1.2.0 and scipy 
0.6.0 installed so far. I'd read that I should set the following 
environment variables prior to building:
export MACOSX_DEPLOYMENT_TARGET=10.5
export CFLAGS="-arch i386 -arch ppc -isysroot 
/Developer/SDKs/MacOSX10.5.sdk"
export LDFLAGS="-arch i386 -arch ppc 
-isyslibroot,/Developer/SDKs/MacOSX10.5.sdk"
Building starts off OK:
============================================================================
BUILDING MATPLOTLIB
 matplotlib: 0.98.1
 python: 2.5.2 (r252:60911, Feb 22 2008, 07:57:53) [GCC
 4.0.1 (Apple Computer, Inc. build 5363)]
 platform: darwin
REQUIRED DEPENDENCIES
 numpy: 1.2.0.dev5312
 freetype2: 9.16.3
OPTIONAL BACKEND DEPENDENCIES
 libpng: 1.2.8
 Tkinter: Tkinter: 50704, Tk: 8.4, Tcl: 8.4
 wxPython: 2.8.3.0
 * WxAgg extension not required for wxPython >= 2.8
 Gtk+: no
 * Building for Gtk+ requires pygtk; you must be 
able
 * to "import gtk" in your build/install environment
 Qt: no
 Qt4: no
 Cairo: no
OPTIONAL DATE/TIMEZONE DEPENDENCIES
 datetime: present, version unknown
 dateutil: matplotlib will provide
 pytz: matplotlib will provide
OPTIONAL USETEX DEPENDENCIES
 dvipng: 1.5
 ghostscript: 8.61
 latex: 3.141592
EXPERIMENTAL CONFIG PACKAGE DEPENDENCIES
 configobj: matplotlib will provide
 enthought.traits: 2.6b1-mpl
[Edit setup.cfg to suppress the above messages]
============================================================================
... but eventually chokes:
g++ -arch i386 -arch ppc -isysroot /Developer/SDKs/MacOSX10.4u.sdk -g 
-bundle -undefined dynamic_lookup -arch i386 -arch ppc 
-isyslibroot,/Developer/SDKs/MacOSX10.5.sdk -arch i386 -arch ppc 
-isysroot /Developer/SDKs/MacOSX10.5.sdk 
build/temp.macosx-10.5-fat-2.5/src/ft2font.o 
build/temp.macosx-10.5-fat-2.5/src/mplutils.o 
build/temp.macosx-10.5-fat-2.5/CXX/cxx_extensions.o 
build/temp.macosx-10.5-fat-2.5/CXX/cxxsupport.o 
build/temp.macosx-10.5-fat-2.5/CXX/IndirectPythonInterface.o 
build/temp.macosx-10.5-fat-2.5/CXX/cxxextensions.o -L/usr/X11/lib 
-L/sw/lib/freetype219/lib -L/usr/local/lib -L/usr/lib -L/sw/lib 
-L/usr/X11R6/lib -lfreetype -lz -lz -lstdc++ -lm -o 
build/lib.macosx-10.5-fat-2.5/matplotlib/ft2font.so 
-Wl,-framework,CoreServices,-framework,ApplicationServices
ld warning: in 
/Developer/SDKs/MacOSX10.4u.sdk/usr/local/lib/libgcc_s.10.5.dylib, 
missing required architecture i386 in file
ld: in /Developer/SDKs/MacOSX10.4u.sdk/usr/local/lib/libgcc_s.1.dylib, 
missing required architecture i386 in file for architecture i386
collect2: ld returned 1 exit status
lipo: can't open input file: 
/var/folders/mX/mXK0L5vQF7elQu1RMPhkyU+++TI/-Tmp-//cc1JpFBv.out (No such 
file or directory)
error: command 'g++' failed with exit status 1
It looks to me as if ld is trying to link to the wrong SDK (10.4 instead 
of 10.5) which might not be built for Intel Macs... is that right? Is 
that actually what's causing the build to fail? I'm totally stuck, and 
would appreciate any advice.
-P
-- 
Peter A. Selkin
Assistant Professor, Environmental Geophysics
IAS/Environmental Science
University of Washington, Tacoma
1900 Commerce St., Box 358436
Tacoma, WA 98402
paselkin@u.washington.edu