bscpkgs/garlic/fig/nbody/freeCpu/plot.R
2020-11-03 19:09:59 +01:00

96 lines
1.9 KiB
R

library(ggplot2)
library(dplyr)
library(scales)
# Load the dataset
#df=read.table("/nix/store/zcyazjbcjn2lhxrpa3bs5y7rw3bbcgnr-plot/data.csv",
df=read.table("data.csv",
col.names=c("variant", "blocksize", "time"))
# Use the blocksize as factor
df$blocksize = as.factor(df$blocksize)
# Split by malloc variant
D=df %>% group_by(variant, 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=variant)) +
# # 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(~variant)
# 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=variant)) +
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()