68 lines
1.8 KiB
R
68 lines
1.8 KiB
R
library(ggplot2)
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library(dplyr, warn.conflicts = FALSE)
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library(scales)
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library(jsonlite)
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library(viridis, warn.conflicts = FALSE)
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library(stringr)
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args = commandArgs(trailingOnly=TRUE)
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# Set the input dataset if given in argv[1], or use "input" as default
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if (length(args)>0) { input_file = args[1] } else { input_file = "input" }
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df = jsonlite::stream_in(file(input_file), verbose=FALSE) %>%
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jsonlite::flatten() %>%
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select(unit,
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config.cbs,
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config.rbs,
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time,
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total_time) %>%
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rename(cbs=config.cbs,
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rbs=config.rbs) %>%
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# Convert to factors
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mutate(cbs = as.factor(cbs)) %>%
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mutate(rbs = as.factor(rbs)) %>%
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mutate(unit = as.factor(unit)) %>%
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# Compute median times
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group_by(unit) %>%
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mutate(median.time = median(time)) %>%
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mutate(normalized.time = time / median.time - 1) %>%
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mutate(log.median.time = log(median.time)) %>%
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ungroup()
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dpi = 300
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h = 6
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w = 6
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# ---------------------------------------------------------------------
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p = ggplot(df, aes(x=cbs, y=normalized.time)) +
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geom_boxplot() +
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geom_hline(yintercept=c(-0.01, 0.01), linetype="dashed", color="red") +
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theme_bw() +
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labs(y="Normalized time",
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title="Heat granularity: normalized time",
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subtitle=input_file) +
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theme(plot.subtitle=element_text(size=8))
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ggsave("normalized.time.png", plot=p, width=w, height=h, dpi=dpi)
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ggsave("normalized.time.pdf", plot=p, width=w, height=h, dpi=dpi)
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# ---------------------------------------------------------------------
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p = ggplot(df, aes(x=cbs, y=time)) +
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geom_point(shape=21, size=3) +
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geom_line(aes(y=median.time, group=0)) +
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theme_bw() +
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labs(y="Time (s)", title="Heat granularity: time",
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subtitle=input_file) +
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theme(plot.subtitle=element_text(size=8))
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ggsave("time.png", plot=p, width=w, height=h, dpi=dpi)
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ggsave("time.pdf", plot=p, width=w, height=h, dpi=dpi)
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