library(ggplot2) library(dplyr) library(scales) library(jsonlite) args=commandArgs(trailingOnly=TRUE) # Read the timetable from args[1] input_file = "timetable.json.gz" if (length(args)>0) { input_file = args[1] } # Load the dataset in NDJSON format dataset = jsonlite::stream_in(file(input_file)) %>% jsonlite::flatten() # We only need the cpu bind, blocksize and time df = select(dataset, config.freeCpu, config.blocksize, time) %>% rename(blocksize=config.blocksize, freeCpu=config.freeCpu) # Use the blocksize as factor df$blocksize = as.factor(df$blocksize) df$freeCpu = as.factor(df$freeCpu) # Split by malloc variant D=df %>% group_by(freeCpu, blocksize) %>% mutate(tnorm = time / median(time) - 1) bs_unique = unique(df$blocksize) nbs=length(bs_unique) print(D) ppi=300 h=5 w=5 png("box.png", width=w*ppi, height=h*ppi, res=ppi) # # # # Create the plot with the normalized time vs blocksize p = ggplot(data=D, aes(x=blocksize, y=tnorm)) + # Labels labs(x="Block size", y="Normalized time", title="Nbody normalized time", subtitle="@expResult@/data.csv") + # Center the title #theme(plot.title = element_text(hjust = 0.5)) + # Black and white mode (useful for printing) #theme_bw() + # Add the maximum allowed error lines geom_hline(yintercept=c(-0.01, 0.01), linetype="dashed", color="red") + # Draw boxplots geom_boxplot(aes(fill=freeCpu)) + # # Use log2 scale in x # scale_x_continuous(trans=log2_trans(), # breaks=bs_unique) + # scale_y_continuous(breaks = scales::pretty_breaks(n = 10)) + theme_bw() + theme(plot.subtitle=element_text(size=10)) + theme(legend.position = c(0.85, 0.85)) #+ # Place each variant group in one separate plot #facet_wrap(~freeCpu) # Render the plot print(p) ## Save the png image dev.off() # png("scatter.png", width=w*ppi, height=h*ppi, res=ppi) # ## Create the plot with the normalized time vs blocksize p = ggplot(D, aes(x=blocksize, y=time, color=freeCpu)) + labs(x="Block size", y="Time (s)", title="Nbody granularity", subtitle="@expResult@") + theme_bw() + geom_point(shape=21, size=3) + #scale_x_continuous(trans=log2_trans()) + scale_y_continuous(trans=log2_trans()) # Render the plot print(p) # Save the png image dev.off()