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)
}
}
}

--

--

--

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

Love podcasts or audiobooks? Learn on the go with our new app.

Recommended from Medium

Topic Modelling for Finding Similar Contracts

When working with focus groups, quantitative methods can help

99P Labs Developer Portal: Enabling Sustainable Mobility Research

The Ultimate Interview Prep Guide for Your Next Dream Data Job

Eight things about Future Rising that may surprise you!

Sleepless in Yerevan Municipality

missingno — Convex Hulls

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store
adrian cockcroft

adrian cockcroft

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

More from Medium

Mathematicians’ relations and communities with each other

Resignations Have Increased Every Year Since 2010

Validating bioinformatics methods: How reproducing results identified what I did wrong

Truwl Performance Metrics comparison table.

Building a GIS Career— A Student’s Guide