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.blocksize, config.gitBranch, time) %>% rename(blocksize=config.blocksize, gitBranch=config.gitBranch) %>% group_by(blocksize, gitBranch) %>% mutate(mtime = median(time)) %>% ungroup() df$gitBranch = as.factor(df$gitBranch) df$blocksize = as.factor(df$blocksize) ppi=300 h=5 w=5 #################################################################### ### Line Graph #################################################################### png("mtime.png", width=w*ppi, height=h*ppi, res=ppi) ## Create the plot with the normalized time vs nblocks p = ggplot(df, aes(x = blocksize, y=mtime, group=gitBranch, color=gitBranch)) + geom_point() + geom_line() + theme_bw() + labs(x="Blocksize", y="Median Time (s)", title="FWI granularity", subtitle=input_file) + theme(plot.subtitle=element_text(size=8)) + theme(legend.position = c(0.5, 0.88)) # Render the plot print(p) # Save the png image dev.off() #################################################################### ### Line Graph #################################################################### png("time.png", width=w*ppi, height=h*ppi, res=ppi) ## Create the plot with the normalized time vs nblocks p = ggplot(df, aes(x = blocksize, y=time, group=gitBranch, color=gitBranch)) + geom_point() + geom_line() + theme_bw() + labs(x="Blocksize", y="Time (s)", title="FWI granularity", subtitle=input_file) + theme(plot.subtitle=element_text(size=8)) + theme(legend.position = c(0.5, 0.88)) # Render the plot print(p) # Save the png image dev.off()