[R] Speed up plotting to MSWindows graphics window

[Mike Marchywka]

----------------------------------------
> Date: Wed, 27 Apr 2011 14:40:23 +0200
> From: jonathan at k-m-p.nl
> To: r-help at r-project.org
> Subject: Re: [R] Speed up plotting to MSWindows graphics window
>
>
> On 27/04/2011 13:18, Mike Marchywka wrote:
> >
> >> > Date: Wed, 27 Apr 2011 11:16:26 +0200
> >> > From:jonathan at k-m-p.nl
> >> > To:r-help at r-project.org
> >> > Subject: [R] Speed up plotting to MSWindows graphics window
> >> >
> >> > Hello,
> >> >
> >> > I am working on a project analysing the performance of motor-vehicles
> >> > through messages logged over a CAN bus.
> >> >

> >> >
> >> > I am currently plotting the data in R, overlaying 5 or more plots of
> >> > data, logged at 1kHz, (using plot.ts() and par(new = TRUE)).
> >> > The aim is to be able to pan, zoom in and out and get values from the
> >> > plotted graph using a custom Qt interface that is used as a front end to
> >> > R.exe (all this works).
> >> > The plot is drawn by R directly to the windows graphic device.
> >> >
> >> > The data is imported from a .csv file (typically around 100MB) to a matrix.
> >> > (timestamp, message ID, byte0, byte1, ..., byte7)
> >> > I then separate this matrix into several by message ID (dimensions are
> >> > in the order of 8cols, 10^6 rows)
> >> >
> >> > The panning is done by redrawing the plots, shifted by a small amount.
> >> > So as to view a window of data from a second to a minute long that can
> >> > travel the length of the logged data.
> >> >
> >> > My problem is that, the redrawing of the plots whilst panning is too
> >> > slow when dealing with this much data.
> >> > i.e.: I can see the last graphs being drawn to the screen in the
> >> > half-second following the view change.
> >> > I need a fluid change from one view to the next.
> >> >
> >> > My question is this:
> >> > Are there ways to speed up the plotting on the MSWindows display?
> >> > By reducing plotted point densities to*sensible* values?
> > Well, hard to know but it would help to know where all the time is going.
> > Usually people start complaining when VM thrashing is common but if you are
> > CPU limited you could try restricting the range of data you want to plot
> > rather than relying on the plot to just clip the largely irrelevant points
> > when you are zoomed in. It should not be too expensive to find the
> > limits either incrementally or with binary search on ordered time series.
> > Presumably subsetting is fast using foo[a:b,]
> >
> > One thing you may want to try for change of scale is wavelet or
> > multi-resolution analysis. You can make a tree ( increasing memory usage
> > but even VM here may not be a big penalty if coherence is high ) and
> > display the resolution appropriate for the current scale.
> >
> >
> I forgot to add, for plotting I use a command similar to:
>
> plot.ts(timestampVector, dataVector, xlim=c(a,b))
>
> a and b are timestamps from timestampVector
>
> Is the xlim parameter sufficient for limiting the scope of the plots?
> Or should I subset the timeseries each time I do a plot?

well, maybe time series knows the data to be ordered, I never use
that, but in general it has to go check each point and clip the out
of range ones. It could I suppose binary search for start/end points
but I don't know.Based on what you said it sounds like it does not.


>
>
> The multi-resolution analysis looks interesting.
> I shall spend some time finding out how to use the wavelets package.
>
> Cheers!
>
> >
> >> > Using something other than plot.ts() - is the lattice package faster?
> >> > I don't need publication quality plots, they can be rougher...
> >> >
> >> > I have tried:
> >> > -Using matrices instead of dataframes - (works for calculations but not
> >> > enough for plots)
> >> > -increasing the max usable memory (max-mem-size) - (no change)
> >> > -increasing the size of the pointer protection stack (max-ppsize) - (no
> >> > change)
> >> > -deleting the unnecessary leftover matrices - (no change)
> >> > -I can't use lines() instead of plot() because of the very different
> >> > scales (rpm-10000, flags -1to3)
> >> >
> >> > I am going to do some resampling of the logged data to reduce the vector
> >> > sizes.
> >> > (removal of*less* important data and use of window.ts())
> >> >
> >> > But I am currently running out of ideas...
> >> > So if sombody could point out something, I would be greatfull.
> >> >
> >> > Thanks,
> >> >
> >> > Jonathan Gabris
> >> >
> >> > ______________________________________________
> >> > R-help at r-project.org mailing list
> >> > https://stat.ethz.ch/mailman/listinfo/r-help
> >> > PLEASE do read the posting guidehttp://www.R-project.org/posting-guide.html
> >> > and provide commented, minimal, self-contained, reproducible code.
> >
>
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