Enhanced headroom plot in R

> names(sops)
[1] "DateTime" "vxstat_dg_operationsRead"
[3] "vxstat_dg_operationsWrite"
> chp(sops[1:2880,2],stime[1:2880,2])
> chp(sops[1:2880,2],stime[1:2880,2],q=1.0)
> chp(sops[1:2880,2],stime[1:2880,2],q=1.0,splits=8)
> chp <-function(throughput,response, q=0.95, qx=F, xl="Throughput",yl="Response",tl="Throughput Over Time",
ml="Headroom Plot", fit=T, max=T, splits=0) {
# remove zero throughput and response values
nonzer <- (throughput != 0) & (response != 0) # array of true/false
y <- response[nonzer]
x <- throughput[nonzer]
# remove outliers, keep response time points inside 95% by default
if (q != 1.0) {
quant <- (y < quantile(y,q))
# optionally trim throughput outliers as well
if (qx) quant <- quant & (x < quantile(x, q))
x <- x[quant]
y <- y[quant]
}
# make histograms and record end points for scaling
xhist <- hist(x,plot=FALSE)
yhist <- hist(y,plot=FALSE)
xbf <- xhist$breaks[1] # first
ybf <- yhist$breaks[1] # first
xbl <- xhist$breaks[length(xhist$breaks)] # last
ybl <- yhist$breaks[length(yhist$breaks)] # last
xcl <- length(xhist$counts) # count length
ycl <- length(yhist$counts) # count length
xrange <- c(0.0,xbl)
yrange <- c(0.0,ybl)
xlen <- length(x)
# make a multi-region layout
nf <- layout(matrix(c(1,3,4,2),2,2,byrow=TRUE), c(3,1), c(1,3), TRUE)
layout.show(nf)
# set plot margins for throughput histogram and plot it
par(mar=c(0,4,3,0))
barplot(xhist$counts, axes=FALSE,
xlim=c(xcl*0.00-xbf/((xbl-xbf)/(xcl-0.5)),xcl*1.00),
ylim=c(0, max(xhist$counts)), space=0, main=ml)
# set plot margins for response histogram and plot it sideways
par(mar=c(5,0,0,1))
barplot(yhist$counts, axes=FALSE, xlim=c(0,max(yhist$counts)),
ylim=c(ycl*0.00-ybf/((ybl-ybf)/(ycl-0.5)),ycl*1.00),
space=0, horiz=TRUE)
# set plot margins for time series plot
par(mar=c(2.5,1.7,3,1))
plot(x, main=tl, cex.axis=0.8, cex.main=0.8, type="S")
if (splits > 0) {
step <- xlen/splits
for(n in 0:(splits-1)) {
lines((1+n*step):min((n+1)*step,xlen), x[(1+n*step):min((n+1)*step,xlen)], col=4+n)
}
}
# set plot margins for main plot area
par(mar=c(5,4,0,0))
plot(x, y, xlim=xrange, ylim=yrange, xlab=xl, ylab=yl, pch=20)
if (max) {
# max curve
b <- xhist$breaks
i <- b[2] - b[1] # interval
maxl <- list(y[b[1] < x & x <= (b[1]+i)])
for(n in b[c(-1,-length(b))]) maxl <- c(maxl,list(y[n < x & x <= (n+i)]))
#print(maxl)
maxv <- unlist(lapply(maxl,max)) # apply max function to elements of list
#print(maxv)
#lines(xhist$mids,maxv,col=2) # join the dots
#staircase plot showing the range for each max response
lines(rep(b,1,each=2)[2:(2*length(maxv)+1)],rep(maxv,1,each=2),col=3)

}
if (fit) {
# fit curve, weighted to predict high throughput
# create persistent chpfit object using <<-
chpfit <- glm(y ~ x, inverse.gaussian, weights=as.numeric(x))
# add fitted values to plot, sorted by throughput
lines(x[order(x)],chpfit$fitted.values[order(x)],col=2)
}
if (splits > 0) {
step <- xlen/splits
for(n in 0:(splits-1)) {
Sys.sleep(1)
points(x[(1+n*step):min((n+1)*step,xlen)],y[(1+n*step):min((n+1)*step,xlen)], xlim=xrange, ylim=yrange, col=4+n)
}
}
}

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Work: Amazon Sustainability (ex AWS, Battery Ventures, Netflix, eBay, Sun Microsystems, CCL)

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Work: Amazon Sustainability (ex AWS, Battery Ventures, Netflix, eBay, Sun Microsystems, CCL)

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