In the example you provided, you tried to broadcase two 1D arrays against
each other, which isn't what you want because all you will get is another
1-D array. Broadcasting automatically repeats data for you along a
dimension. It is rare to actually call np.broadcast() as it usually happens
automatically. Perhaps you should take your questions about broadcasting
over to the numpy discussion mailing list where somebody there might be
able to better explain it than I.
Cheers!
Ben Root
On Mon, Sep 22, 2014 at 6:42 AM, Raffaele Quarta <raffaele.qua...@linksmt.it
> wrote:
> Hi all,
>
> somebody can show me with an example how can I set the numpy's
> broadcasting feature?
>
> Actually, I'm using 'meshgrid' in the script but I knew that it takes a
> lot of time to have the plot.
>
> Thank you.
>
> Raf
>
>
>
> -----Original Message-----
> From: Raffaele Quarta [mailto:raffaele.qua...@linksmt.it
> <raffaele.qua...@linksmt.it>]
> Sent: Tue 9/9/2014 3:55 PM
> To: Benjamin Root; Ryan Nelson
> Cc: Matplotlib Users
> Subject: Re: [Matplotlib-users] Plotting large file (NetCDF)
>
> Hi Ben and Ryan,
>
> I will try to figure out as it works.
>
> Thank you.
>
> Regards,
>
> Raf
>
>
> -----Original Message-----
> From: ben.v.r...@gmail.com on behalf of Benjamin Root
> Sent: Tue 9/9/2014 3:25 PM
> To: Ryan Nelson
> Cc: Raffaele Quarta; Matplotlib Users
> Subject: Re: [Matplotlib-users] Plotting large file (NetCDF)
>
> Most of the time, you will not need to use meshgrid. Take advantage of
> numpy's broadcasting feature:
> http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html
> It saves *significantly* on memory and processing time. Most of
> Matplotlib's plotting functions work well with broadcastable inputs, so
> that is a great way to save on memory. NumPy's ogrid is also a neat tool
> for generating broadcastable grids.
>
> When I get a chance, I'll look through the script for any other obvious
> savers.
>
> Cheers!
> Ben Root
>
>
> On Tue, Sep 9, 2014 at 9:02 AM, Ryan Nelson <rnelsonc...@gmail.com> wrote:
>
> > Raffaele,
> >
> > As Ben pointed out, you might be creating a lot of in memory Numpy arrays
> > that you probably don't need/want.
> >
> > For example, I think (?) slicing all of the variable below:
> > lons = fh.variables['lon'][:]
> > is making a copy of all that (mmap'ed) data as a Numpy array in memory.
> > Get rid of the slice ([:]). Of course, these variables are not Numpy
> > arrays, so you'll have to change some of your code. For example:
> > lon_0 = lons.mean()
> > Will have to become:
> > lon_0 = np.mean( lons )
> >
> > If lats and lons are very large sets of data, then meshgrid will make two
> > very, very large arrays in memory.
> > For example, try this:
> > np.meshgrid(np.arange(5), np.arange(5))
> > The output is two much larger arrays:
> > [array([[0, 1, 2, 3, 4],
> > [0, 1, 2, 3, 4],
> > [0, 1, 2, 3, 4],
> > [0, 1, 2, 3, 4],
> > [0, 1, 2, 3, 4]]),
> > array([[0, 0, 0, 0, 0],
> > [1, 1, 1, 1, 1],
> > [2, 2, 2, 2, 2],
> > [3, 3, 3, 3, 3],
> > [4, 4, 4, 4, 4]])]
> > I don't know Basemap at all, so I don't know if this is necessary. You
> > might be able to force the meshgrid output into a memmap file, but I
> don't
> > know how to do that right now. Perhaps someone else has some suggestions.
> >
> > Hope that helps.
> >
> > Ryan
> >
> >
> >
> >
> > On Tue, Sep 9, 2014 at 4:07 AM, Raffaele Quarta <
> > raffaele.qua...@linksmt.it> wrote:
> >
> >> 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:jkly...@uvic.ca <jkly...@uvic.ca> <
> jkly...@uvic.ca>]
> >> 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.r...@ou.edu> 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 <
> >> raffaele.qua...@linksmt.it> 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 <
> >> raffaele.qua...@linksmt.it>
> >> > 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
> >> > >
> >> > > --
> >> > > This email was Virus checked by Astaro Security Gateway.
> >> http://www.sophos.com
> >> > >
> >> > >
> >> > >
> >> > >
> >>
> ------------------------------------------------------------------------------
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> >> > >
> >>
> http://pubads.g.doubleclick.net/gampad/clk?id=157508191&iu=/4140/ostg.clktrk
> >> > > _______________________________________________
> >> > > Matplotlib-users mailing list
> >> > > Matplotlib-users@lists.sourceforge.net
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> >> > >
> >> >
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> >> >
> >> >
> >> >
> >>
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> >> > When you want reliability, choose Perforce
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> >>
> http://pubads.g.doubleclick.net/gampad/clk?id=157508191&iu=/4140/ostg.clktrk_______________________________________________
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> >> > https://lists.sourceforge.net/lists/listinfo/matplotlib-users
> >>
> >> --
> >> Jody Klymak
> >> http://web.uvic.ca/~jklymak/
> >>
> >>
> >>
> >>
> >>
> >>
> >>
> >>
> >>
> >>
> ------------------------------------------------------------------------------
> >> Want excitement?
> >> Manually upgrade your production database.
> >> When you want reliability, choose Perforce.
> >> Perforce version control. Predictably reliable.
> >>
> >>
> http://pubads.g.doubleclick.net/gampad/clk?id=157508191&iu=/4140/ostg.clktrk
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> >> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
> >>
> >>
> >
> >
> >
> ------------------------------------------------------------------------------
> > Want excitement?
> > Manually upgrade your production database.
> > When you want reliability, choose Perforce.
> > Perforce version control. Predictably reliable.
> >
> >
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> >
> >
>
>
> --
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