On 27/04/2011 13:18, Mike Marchywka wrote:
>
>> > Date: Wed, 27 Apr 2011 11:16:26 +0200
>> > From:jonat...@k-m-p.nl
>> > To:r-help@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 using R 2.12 on Windows XP and 7
>> >
>> > 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?
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@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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