library(ggplot2) library(dplyr) library(scales) library(jsonlite) args=commandArgs(trailingOnly=TRUE) # Read the timetable from args[1] input_file = "input.json" 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 nblocks and time df = select(dataset, config.cbs, config.rbs, time) %>% rename(cbs=config.cbs, rbs=config.rbs) df$cbs = as.factor(df$cbs) df$rbs = as.factor(df$rbs) # Normalize the time by the median df=group_by(df, cbs, rbs) %>% mutate(mtime = median(time)) %>% mutate(tnorm = time / mtime - 1) %>% mutate(logmtime = log(mtime)) %>% ungroup() %>% filter(between(mtime, mean(time) - (1 * sd(time)), mean(time) + (1 * sd(time)))) 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 nblocks p = ggplot(data=df, aes(x=cbs, y=tnorm)) + # Labels labs(x="cbs", y="Normalized time", title=sprintf("Heat normalized time"), subtitle=input_file) + # 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() + #scale_y_continuous(breaks = scales::pretty_breaks(n = 10)) + theme_bw() + theme(plot.subtitle=element_text(size=8)) + theme(legend.position = c(0.85, 0.85)) #+ # 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 nblocks p = ggplot(df, aes(x=cbs, y=time, linetype=rbs, group=rbs)) + labs(x="cbs", y="Time (s)", title=sprintf("Heat granularity"), subtitle=input_file) + theme_bw() + theme(plot.subtitle=element_text(size=8)) + theme(legend.position = c(0.5, 0.88)) + geom_point(shape=21, size=3) + geom_line(aes(y=mtime)) + #scale_x_continuous(trans=log2_trans()) + scale_y_continuous(trans=log2_trans()) # Render the plot print(p) # Save the png image dev.off() png("heatmap.png", width=w*ppi, height=h*ppi, res=ppi) # ## Create the plot with the normalized time vs nblocks p = ggplot(df, aes(x=cbs, y=rbs, fill=logmtime)) + geom_raster() + scale_fill_gradient(high="black", low="white") + coord_fixed() + theme_bw() + theme(plot.subtitle=element_text(size=8)) + labs(x="cbs", y="rbs", title=sprintf("Heat granularity"), subtitle=input_file) # Render the plot print(p) # Save the png image dev.off()