bscpkgs/garlic/fig/nbody/test.R
Rodrigo Arias Mallo 4beb069627 WIP: postprocessing pipeline
Now each run is executed in a independent folder
2020-11-03 19:09:59 +01:00

83 lines
1.8 KiB
R

library(ggplot2)
library(dplyr)
library(scales)
# Load the dataset
df=read.table("data.csv", col.names=c("blocksize", "time"))
bs_unique = unique(df$blocksize)
nbs=length(bs_unique)
# Normalize the time by the median
D=group_by(df, blocksize) %>%
mutate(tnorm = time / median(time) - 1) # %>%
# mutate(bad = (abs(tnorm) >= 0.01)) %>%
# mutate(color = ifelse(bad,"red","black"))
D$bad = cut(abs(D$tnorm), breaks=c(-Inf, 0.01, +Inf), labels=c("good", "bad"))
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(D, aes(x=blocksize, y=tnorm)) +
# Labels
labs(x="Block size", y="Normalized time",
title="Nbody normalized time",
subtitle="@expResult@") +
# Center the title
#theme(plot.title = element_text(hjust = 0.5)) +
# Black and white mode (useful for printing)
#theme_bw() +
# Draw boxplots
geom_boxplot(aes(group=blocksize)) +
# Use log2 scale in x
scale_x_continuous(trans=log2_trans(),
breaks=bs_unique) +
scale_y_continuous(breaks = scales::pretty_breaks(n = 10)) +
# Add the maximum allowed error lines
geom_hline(yintercept=c(-0.01, 0.01),
linetype="dashed", color="red")
# 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=bad)) +
#
# labs(x="Blocksize", y="Time (s)",
# title="Nbody granularity",
# subtitle="@expResult@") +
#
# geom_point(shape=21, size=1.5) +
# scale_color_manual(values=c("black", "red")) +
# scale_x_continuous(trans=log2_trans(),
# breaks=bs_unique) +
# scale_y_continuous(trans=log2_trans())
#
## Render the plot
#print(p)
# Save the png image
#dev.off()