## Sample points uniformly within a fixed radius nrand=1000 maxstep=10 ## Sample data ## NB: To get a truly uniform sample over the circle, you must ## sample the square of the distance and then transform back. tempdat<-data.frame(X0=0,Y0=0, bearing0=0, bad.dist= runif(nrand)*maxstep, dist2=sqrt(runif(nrand)*maxstep^2), turningangle=runif(nrand)*2*pi-pi) ##convert Turning angle to bearing (in this case no change) tempdat$bearing=tempdat$bearing0+tempdat$turningangle ## Convert from polar to cartesian coordinates tempdat$X<-tempdat$X0+tempdat$dist2*sin(tempdat$bearing) tempdat$Y<-tempdat$Y0+tempdat$dist2*cos(tempdat$bearing) tempdat$Xbad<-tempdat$X0+tempdat$bad.dist*sin(tempdat$bearing) tempdat$Ybad<-tempdat$Y0+tempdat$bad.dist*cos(tempdat$bearing) ##make plots png(filename="sampleplots.png",width=500,height=1000) par(mfrow=c(2,1)) plot(Ybad~Xbad, data=tempdat, asp=1, main="Center is oversampled") plot(Y~X, data=tempdat, asp=1, main="Uniform across space") dev.off()
Friday, May 16, 2014
Sample uniformly within a fixed radius.
I was asked how to do this today and thought that I would share the answer:
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1 comment:
Hi Forester,
Thanks for your post! Here's different solution for uniform sampling that's a bit more slipshod (you end up having less control), but I thought you'd be interested in the nature of the solution:
Mark
df = data.frame(
x1 = runif(1000,-1,1),
y1 = runif(1000,-1,1)
)
df$ok = apply(df,1,function(x){as.numeric(x[1]^2 + x[2]^2 < 1)})
sub = df[df$ok == 1,]
plot(sub$x1, sub$y1)
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