Re: [Matplotlib-users] Plotting large file (NetCDF)

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Hi Jody and Ben,

thanks for your answers. 
I tried to use pcolormesh instead of pcolor and the result is very good! For what concern with the memory system problem, I wasn't able to solve it. When I tried to use the bigger file, I got the same problem. Attached you will find the script that I'm using to make the plot. May be, I didn't understand very well how can I use the mmap function. 

Regards,

Raffaele.

-----Original Message-----
From: Jody Klymak [mailto:jk...@uv...]
Sent: Mon 9/8/2014 5:46 PM
To: Benjamin Root
Cc: Raffaele Quarta; Matplotlib Users
Subject: Re: [Matplotlib-users] Plotting large file (NetCDF)

It looks like you are calling `pcolor`.  Can I suggest you try `pcolormesh`?  ii

75 Mb is not a big file!

Cheers,   Jody

On Sep 8, 2014, at  7:38 AM, Benjamin Root <ben...@ou...> wrote:

> (Keeping this on the mailing list so that others can benefit)
> 
> What might be happening is that you are keeping around too many numpy arrays in memory than you actually need. Take advantage of memmapping, which most netcdf tools provide by default. This keeps the data on disk rather than in RAM. Second, for very large images, I would suggest either pcolormesh() or just simply imshow() instead of pcolor() as they are more way more efficient than pcolor(). In addition, it sounds like you are dealing with re-sampled data ("at different zoom levels"). Does this mean that you are re-running contour on re-sampled data? I am not sure what the benefit of doing that is if one could just simply do the contour once at the highest resolution.
> 
> Without seeing any code, though, I can only provide generic suggestions.
> 
> Cheers!
> Ben Root
> 
> 
> On Mon, Sep 8, 2014 at 10:12 AM, Raffaele Quarta <raf...@li...> wrote:
> Hi Ben,
> 
> sorry for the few details that I gave to you. I'm trying to make a contour plot of a variable at different zoom levels by using high resolution data. The aim is to obtain .PNG output images. Actually, I'm working with big data (NetCDF file, dimension is about 75Mb). The current Matplotlib version on my UBUNTU 14.04 machine is the 1.3.1 one. My system has a RAM capacity of 8Gb.
> Actually, I'm dealing with memory system problems when I try to make a plot. I got the error message as follow:
> 
> --------------------------------------------
>      cs = m.pcolor(xi,yi,np.squeeze(t))
>   File "/usr/lib/pymodules/python2.7/mpl_toolkits/basemap/__init__.py", line 521, in with_transform
>     return plotfunc(self,x,y,data,*args,**kwargs)
>   File "/usr/lib/pymodules/python2.7/mpl_toolkits/basemap/__init__.py", line 3375, in pcolor
>     x = ma.masked_values(np.where(x > 1.e20,1.e20,x), 1.e20)
>   File "/usr/lib/python2.7/dist-packages/numpy/ma/core.py", line 2195, in masked_values
>     condition = umath.less_equal(mabs(xnew - value), atol + rtol * mabs(value))
> MemoryError  
> --------------------------------------------
> 
> Otherwise, when I try to make a plot of smaller file (such as 5Mb), it works very well. I believe that it's not something of wrong in the script. It might be a memory system problem.
> I hope that my message is more clear now.
> 
> Thanks for the help.
> 
> Regards,
> 
> Raffaele
> 
> -----------------------------------------
> 
> Sent: Mon 9/8/2014 3:19 PM
> To: Raffaele Quarta
> Cc: Matplotlib Users
> Subject: Re: [Matplotlib-users] Plotting large file (NetCDF)
> 
> 
> 
> You will need to be more specific... much more specific. What kind of plot
> are you making? How big is your data? What version of matplotlib are you
> using? How much RAM do you have available compared to the amount of data
> (most slowdowns are actually due to swap-thrashing issues). Matplotlib can
> be used for large data, but there exists some speciality tools for the
> truly large datasets. The solution depends on the situation.
> 
> Ben Root
> 
> On Mon, Sep 8, 2014 at 7:45 AM, Raffaele Quarta <raf...@li...>
> wrote:
> 
> >  Hi,
> >
> > I'm working with NetCDF format. When I try to make a plot of very large
> > file, I have to wait for a long time for plotting. How can I solve this?
> > Isn't there a solution for this problem?
> >
> > Raffaele
> >
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--
Jody Klymak    
http://web.uvic.ca/~jklymak/