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#!/usr/bin/env Rscript
# (c) Giorgio Gonnella, ZBH, Uni Hamburg, 2017
script.name = "./gfapy-plot-benchmarkdata.R"
args <- commandArgs(trailingOnly=TRUE)
if (is.na(args[3])) {
cat("Usage: ",script.name, " <inputfile> <outpfx> <variable>", "\n")
cat("variable: either 'segments' or 'connectivity'\n")
stop("Too few command-line parameters")
}
infname <- args[1]
cat("input data: ",infname,"\n")
outpfx <- args[2]
cat("output prefix:", outpfx, "\n")
xvar <- args[3]
if (xvar != 'segments' && xvar != 'connectivity') {
stop("variable must be one of: segments, connectivity")
}
library("ggplot2")
#
# The following function is described here:
# http://www.cookbook-r.com/Graphs/Plotting_means_and_error_bars_(ggplot2)/#Helper%20functions
# Licence: CC0 (https://creativecommons.org/publicdomain/zero/1.0/)
#
## Gives count, mean, standard deviation, standard error of the mean, and
## confidence interval (default 95%).
## data: a data frame.
## measurevar: the name of a column that contains the var to be summariezed
## groupvars: a vector containing names of columns that contain grouping vars
## na.rm: a boolean that indicates whether to ignore NA's
## conf.interval: the percent range of the confidence interval (default 95%)
summarySE <- function(data=NULL, measurevar, groupvars=NULL, na.rm=FALSE,
conf.interval=.95, .drop=TRUE) {
library(plyr)
# New version of length which can handle NA's: if na.rm==T, don't count them
length2 <- function (x, na.rm=FALSE) {
if (na.rm) sum(!is.na(x))
else length(x)
}
# This does the summary. For each group's data frame, return a vector with
# N, mean, and sd
datac <- ddply(data, groupvars, .drop=.drop,
.fun = function(xx, col) {
c(N = length2(xx[[col]], na.rm=na.rm),
mean = mean (xx[[col]], na.rm=na.rm),
sd = sd (xx[[col]], na.rm=na.rm)
)
},
measurevar
)
# Rename the "mean" column
datac <- rename(datac, c("mean" = measurevar))
datac$se <- datac$sd / sqrt(datac$N) # Calculate standard error of the mean
# Confidence interval multiplier for standard error
# Calculate t-statistic for confidence interval:
# e.g., if conf.interval is .95, use .975 (above/below), and use df=N-1
ciMult <- qt(conf.interval/2 + .5, datac$N-1)
datac$ci <- datac$se * ciMult
return(datac)
}
data <- read.table(infname, header=T, sep="\t")
if (xvar == "segments") {
xvarname = "lines"
xlab="Lines (segments 1/3; dovetails 2/3)"
} else {
xvarname = "mult"
xlab="Dovetails/segment (segments=4000)"
data[c("lines")] = (data[c("mult")]+1)*4000
}
time.data <- summarySE(data, measurevar="time", groupvars=c(xvarname))
outfname = paste0(outpfx,"_time.log")
sink(outfname)
print(time.data)
time.lm <- lm(time ~ lines, data=data)
summary(time.lm)
time.nls <- nls(time ~ b + a * lines,
data=data, start=list(a=0,b=0),
algorithm="port", lower=c(0,0))
print(time.nls)
sink()
outfname = paste0(outpfx,"_space.log")
sink(outfname)
space.data <- summarySE(data, measurevar="space", groupvars=c(xvarname))
print(space.data)
space.lm <- lm(space ~ lines, data=data)
summary(space.lm)
space.nls <- nls(space ~ b + a * lines,
data=data, start=list(a=0,b=0),
algorithm="port", lower=c(0,0))
print(space.nls)
sink()
outfname = paste0(outpfx,"_time.pdf")
pdf(outfname)
print(ggplot(time.data, aes_string(x=xvarname, y="time")) +
geom_errorbar(aes(ymin=time-se, ymax=time+se), width=2) +
geom_line(size=0.2) + geom_point(size=3) +
ylab("Total elapsed time (s)") +
xlab(xlab))
outfname = paste0(outpfx,"_space.pdf")
pdf(outfname)
print(ggplot(space.data, aes_string(x=xvarname, y="space")) +
geom_errorbar(aes(ymin=space-se, ymax=space+se), width=2) +
geom_line(size=0.2) + geom_point(size=3) +
ylab("Memory peak (MB)") +
xlab(xlab))
dev.off()
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