96 lines
1.9 KiB
R
96 lines
1.9 KiB
R
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library(ggplot2)
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library(dplyr)
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library(scales)
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# Load the dataset
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#df=read.table("/nix/store/zcyazjbcjn2lhxrpa3bs5y7rw3bbcgnr-plot/data.csv",
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df=read.table("data.csv",
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col.names=c("variant", "blocksize", "time"))
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# Use the blocksize as factor
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df$blocksize = as.factor(df$blocksize)
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# Split by malloc variant
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D=df %>% group_by(variant, blocksize) %>%
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mutate(tnorm = time / median(time) - 1)
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bs_unique = unique(df$blocksize)
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nbs=length(bs_unique)
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print(D)
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ppi=300
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h=5
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w=5
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png("box.png", width=w*ppi, height=h*ppi, res=ppi)
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#
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#
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#
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# Create the plot with the normalized time vs blocksize
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p = ggplot(data=D, aes(x=blocksize, y=tnorm)) +
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# Labels
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labs(x="Block size", y="Normalized time",
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title="Nbody normalized time",
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subtitle="@expResult@/data.csv") +
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# Center the title
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#theme(plot.title = element_text(hjust = 0.5)) +
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# Black and white mode (useful for printing)
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#theme_bw() +
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# Add the maximum allowed error lines
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geom_hline(yintercept=c(-0.01, 0.01),
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linetype="dashed", color="red") +
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# Draw boxplots
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geom_boxplot(aes(fill=variant)) +
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# # Use log2 scale in x
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# scale_x_continuous(trans=log2_trans(),
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# breaks=bs_unique) +
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#
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scale_y_continuous(breaks = scales::pretty_breaks(n = 10)) +
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theme_bw() +
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theme(plot.subtitle=element_text(size=10)) +
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theme(legend.position = c(0.85, 0.85)) #+
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# Place each variant group in one separate plot
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#facet_wrap(~variant)
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# Render the plot
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print(p)
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## Save the png image
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dev.off()
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#
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png("scatter.png", width=w*ppi, height=h*ppi, res=ppi)
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#
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## Create the plot with the normalized time vs blocksize
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p = ggplot(D, aes(x=blocksize, y=time, color=variant)) +
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labs(x="Block size", y="Time (s)",
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title="Nbody granularity",
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subtitle="@expResult@") +
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theme_bw() +
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geom_point(shape=21, size=3) +
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#scale_x_continuous(trans=log2_trans()) +
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scale_y_continuous(trans=log2_trans())
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# Render the plot
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print(p)
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# Save the png image
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dev.off()
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