Hi:
Goal: use R to turn a matrix of 1's and 0's
into a corresponding image (e.g. png)
of black and white pixels.Why R: Yes, I can do this more efficiently and precisely
with a perl module like Image::PBM. Been there,
done that many times, etc. (Just humor me.
I'm trying to do this with R for a number of reasons.)Problem: Difficult to get a perfect rasterization. There can
be appended or removed pixel columns or pixel rows
depending on plot region dimensions. I witness this
with both R version 1.8.1 and R version 2.0.print($out "bitmap('/usr/local/mycrow/tmp/out.png', type = 'png256', height = ".(int($height*68/64)/64).", width = ".(int($width*69/64)/64).", res = 64, pointsize=0)\n");
print($out 'par(mar=c(0,0,0,0))'."\n");
my $width1 = $width-1;
my $height1 = $height-1;
print($out <<END);
plot.new()
plot.window(c(0,$width1),c(0,$height1))
rect(m[,1], m[,2], m[,1], m[,2], col="black", border="black")
There are alternatives to rect (plot with type="p", pch=".", etc) and I have also tried png() instead of bitmap(). (I do prefer bitmap so this can run without x11.)
I am guessing that R's internal region calculations are vector based, which generally makes sense for most statistical plots. However, I do have some ideas for R and the presentation of cellular automata results.
Any tips out there? (Is it just a matter of height=50px to overcome the inches default, etc?).....
Regards, Scott
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