matplotlib-users Mailing List for matplotlib

On Sun, Dec 28, 2008 at 12:39 PM, Leotis buchanan
<leo...@gm...> wrote:
> Hey Guys,
>
> I want to use matplotlib to plot data that is changing every second, I am
> thinking that in order to do this i will have to update the data array with
> the new data ,
> and redraw the graph. Is this the recommended way to do it ?
See the animation cookbook and examples
 http://www.scipy.org/Cookbook/Matplotlib/Animations
But note that the section "GUI neutral animation in pylab" is no
longer recommended or supported.
The recommended practice can be found in the examples:
 http://matplotlib.sourceforge.net/examples/animation/index.html
JDH

Leotis buchanan wrote:
> 
> I want to use matplotlib to plot data that is changing every second, I am thinking that in order to do this i will have to update the data array with the new data ,
> and redraw the graph. Is this the recommended way to do it ?
Depending on what environment you are calling from and how complicated the graph 
is, remember to do pylab.ioff() and the pylab.ion() before and after your 
drawing commands to speed up the drawing.
Also, you may want to force the axis size, because axes that change in scale all 
the time can be distracting
-Kaushik

antonv wrote:
> It seems that I just cannot grasp the way the data needs to be formatted for
> this to work...
> I've used the griddata sample that James posted but it takes about 10
> minutes to prep the data for plotting so that solution seems to be out of
> discussion.
>
> I guess my issue is that I don't know what type of data is required by
> contourf function. Also as Jeff was saying earlier, the data is read from a
> grib file so supposedly it's already gridded. I've also looked at the
> basemap demo
> (http://matplotlib.sourceforge.net/users/screenshots.html#basemap-demo) and
> the data is read from 3 files, one for Lat one for Long and the Last for Z
> Data. Is there a way to automatically extract the data from the grib file to
> a format similar to the one used in the basemap example?
> 
Anton: I just looked at your csv file and I think I know what the 
problem is. Whatever program you used to dump the grib data did not 
write all the data - the missing land values were skipped. That means 
you don't have the full rectangular array of data. I think you have two 
choices:
1) insert the missing land values into the array, either in the csv file 
or into the array after it is read in from the csv file. What program 
did you use to dump the GRIB data to a CSV file?
2) use a python grib interface. If you're on Windows, PyNIO won't 
work. I've written my own module (pygrib2 - 
http://code.google.com/p/pygrib2) which you should be able to compile on 
windows. You'll need the png and jasper (jpeg2000) libraries, however.
I recommend (2) - in the time you've already spent messing with that csv 
file, you could have already gotten a real python grib reader working!
-Jeff
>
>
> Jeff Whitaker wrote:
> 
>> Mauro Cavalcanti wrote:
>> 
>>> Dear Anton,
>>>
>>> 2008年12月23日 antonv <vas...@ya...>:
>>> 
>>> 
>>>> Also, because I figured out the data I need and already have the
>>>> scripts in place
>>>> to extract the CSV files I would really like to keep it that way. Would
>>>> it be possible to
>>>> just show me how to get from the csv file to the plot?
>>>> 
>>>> 
>>> Here is a short recipe:
>>>
>>> import numpy as np
>>>
>>> f = open("file.csv", "r")
>>> coords = np.loadtxt(f, delimiter=",", skiprows=1)
>>> lon = coords[:,0]
>>> lat = coords[:,1]
>>> dat = coords[:,2]
>>>
>>> where "file.csv" is a regular comma-separated values file in the format:
>>>
>>> Lat,Lon,Dat
>>> -61.05,10.4,20
>>> -79.43,9.15,50
>>> -70.66,9.53,10
>>> -63.11,7.91,40
>>> ...
>>>
>>> Hope this helps!
>>>
>>> Best regards,
>>>
>>> 
>>> 
>> Since the arrays are 2D (for gridded data), a reshape is also needed, i.e.
>>
>> lon.shape = (nlats,nlons)
>> lat.shape = (nlats,nlons)
>> data.shape = (nlats,nlons)
>>
>> You'll need to know what the grid dimensons (nlats,nlons) are.
>>
>> -Jeff
>>
>> ------------------------------------------------------------------------------
>> _______________________________________________
>> Matplotlib-users mailing list
>> Mat...@li...
>> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>>
>>
>> 
>
> 

Hey Guys,
I want to use matplotlib to plot data that is changing every second, I am
thinking that in order to do this i will have to update the data array with
the new data ,
and redraw the graph. Is this the recommended way to do it ?
Thanks
-- 
Leotis Buchanan
Manager/Electronic Design Systems Engineer
Exterbox.com

It seems that I just cannot grasp the way the data needs to be formatted for
this to work...
I've used the griddata sample that James posted but it takes about 10
minutes to prep the data for plotting so that solution seems to be out of
discussion.
I guess my issue is that I don't know what type of data is required by
contourf function. Also as Jeff was saying earlier, the data is read from a
grib file so supposedly it's already gridded. I've also looked at the
basemap demo
(http://matplotlib.sourceforge.net/users/screenshots.html#basemap-demo) and
the data is read from 3 files, one for Lat one for Long and the Last for Z
Data. Is there a way to automatically extract the data from the grib file to
a format similar to the one used in the basemap example?
Jeff Whitaker wrote:
> 
> Mauro Cavalcanti wrote:
>> Dear Anton,
>>
>> 2008年12月23日 antonv <vas...@ya...>:
>> 
>>> Also, because I figured out the data I need and already have the
>>> scripts in place
>>> to extract the CSV files I would really like to keep it that way. Would
>>> it be possible to
>>> just show me how to get from the csv file to the plot?
>>> 
>>
>> Here is a short recipe:
>>
>> import numpy as np
>>
>> f = open("file.csv", "r")
>> coords = np.loadtxt(f, delimiter=",", skiprows=1)
>> lon = coords[:,0]
>> lat = coords[:,1]
>> dat = coords[:,2]
>>
>> where "file.csv" is a regular comma-separated values file in the format:
>>
>> Lat,Lon,Dat
>> -61.05,10.4,20
>> -79.43,9.15,50
>> -70.66,9.53,10
>> -63.11,7.91,40
>> ...
>>
>> Hope this helps!
>>
>> Best regards,
>>
>> 
> Since the arrays are 2D (for gridded data), a reshape is also needed, i.e.
> 
> lon.shape = (nlats,nlons)
> lat.shape = (nlats,nlons)
> data.shape = (nlats,nlons)
> 
> You'll need to know what the grid dimensons (nlats,nlons) are.
> 
> -Jeff
> 
> ------------------------------------------------------------------------------
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
> 
> 
-- 
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PS. In the code just disregard the line N = 1000 - it does nothing.
Ghose, Kaushik wrote:
> Hi John,
> 
> OK. I've managed to pare it down to the following pattern:
> 
> import pylab
> 
> N = 1000
> x = pylab.zeros(200)
> x[1] = .5
> x[2:24] = 1.0
> x[24] = .5
> x[26] = -.5
> x[27:49] = -1.0
> x[49] = -.5
> x = pylab.tile(x, 100)
> pylab.plot(x)
> 
> 
> The above code is sufficient to repeat the glitch (just resize the window to
> check this). The half-way values (0.5) are important - if we have a straight
> jump the glitch isn't visible.
> 
> I'm sorry but I couldn't find path.py under
> 
> /Library/Frameworks/Python.framework/Versions/2.5/lib/python2.5/site-packages/
> 
> so I couldn't try it out. (Is it under a different place in mac?)
> 
> thanks
> -Kaushik
> 
> 
> 
> John Hunter wrote:
>> On Sat, Dec 27, 2008 at 10:29 AM, Kaushik Ghose
>> <Kau...@hm...> wrote:
>>> Hi Gang,
>>>
>>> I was plotting some data collected from an ADC and noticed an odd aliasing
>>> issue. Please see the images on the following site.
>>>
>>> http://assorted-experience.blogspot.com/2008/12/odd-aliasing-issue-with-matplotlib.html
>>>
>>> I wonder if there is any way to avoid this kind of aliasing. I vaguely remember
>>> our old arch-foe (MATLAB) handles this gracefully. I have found matplotlib's
>>> plotting to be superior to MATLAB's in every way (except for 3D) and it would be
>>> nice if aliasing could be handled gracefully.
>> I'm almost certain this is a result of the path simplification logic.
>> Could you upload some sample data and a self contained script so we
>> can test?
>> You can test this by editing site-packages/path.py and replacing::
>>
>> self.should_simplify = (len(vertices) >= 128 and
>> (codes is None or np.all(codes <= Path.LINETO)))
>>
>> with::
>>
>> self.should_simplify = False
>>
>> Michael, perhaps we could override path.should_simplify with an rc or
>> line property?
>>
>>> Also, thanks for the excellent binary packages for Mac!
>> Thanks for testing them!
> 
> ------------------------------------------------------------------------------
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users

Hi John,
OK. I've managed to pare it down to the following pattern:
import pylab
N = 1000
x = pylab.zeros(200)
x[1] = .5
x[2:24] = 1.0
x[24] = .5
x[26] = -.5
x[27:49] = -1.0
x[49] = -.5
x = pylab.tile(x, 100)
pylab.plot(x)
The above code is sufficient to repeat the glitch (just resize the window to 
check this). The half-way values (0.5) are important - if we have a straight 
jump the glitch isn't visible.
I'm sorry but I couldn't find path.py under
/Library/Frameworks/Python.framework/Versions/2.5/lib/python2.5/site-packages/
so I couldn't try it out. (Is it under a different place in mac?)
thanks
-Kaushik
John Hunter wrote:
> On Sat, Dec 27, 2008 at 10:29 AM, Kaushik Ghose
> <Kau...@hm...> wrote:
>> Hi Gang,
>>
>> I was plotting some data collected from an ADC and noticed an odd aliasing
>> issue. Please see the images on the following site.
>>
>> http://assorted-experience.blogspot.com/2008/12/odd-aliasing-issue-with-matplotlib.html
>>
>> I wonder if there is any way to avoid this kind of aliasing. I vaguely remember
>> our old arch-foe (MATLAB) handles this gracefully. I have found matplotlib's
>> plotting to be superior to MATLAB's in every way (except for 3D) and it would be
>> nice if aliasing could be handled gracefully.
> 
> I'm almost certain this is a result of the path simplification logic.
> Could you upload some sample data and a self contained script so we
> can test?
> You can test this by editing site-packages/path.py and replacing::
> 
> self.should_simplify = (len(vertices) >= 128 and
> (codes is None or np.all(codes <= Path.LINETO)))
> 
> with::
> 
> self.should_simplify = False
> 
> Michael, perhaps we could override path.should_simplify with an rc or
> line property?
> 
>> Also, thanks for the excellent binary packages for Mac!
> 
> Thanks for testing them!