forked from rarias/bscpkgs
68 lines
1.6 KiB
R
68 lines
1.6 KiB
R
library(ggplot2)
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library(dplyr)
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library(scales)
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library(jsonlite)
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args=commandArgs(trailingOnly=TRUE)
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# Read the timetable from args[1]
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input_file1 = "input1.json"
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if (length(args)>0) { input_file1 = args[1] }
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input_file2 = "input2.json"
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if (length(args)>1) { input_file2 = args[2] }
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# Load the dataset in NDJSON format
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dataset1 = jsonlite::stream_in(file(input_file1)) %>%
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jsonlite::flatten()
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dataset2 = jsonlite::stream_in(file(input_file2)) %>%
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jsonlite::flatten()
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# We only need the nblocks and time
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df1 = select(dataset1, config.nbx, time) %>%
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rename(nb1=config.nbx)
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df2 = select(dataset2, config.nby, time) %>%
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rename(nb2=config.nby)
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df1$nb1 = as.factor(df1$nb1)
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df2$nb2 = as.factor(df2$nb2)
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# Normalize the time by the median
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D1=group_by(df1, nb1)
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D2=group_by(df2, nb2)
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print(D1)
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print(D2)
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ppi=300
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h=5
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w=7
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png("scatter_granularity_and_blocking.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 nblocks
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p = ggplot() +
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geom_point(data=D1, aes(x=nb1, y=time, colour = 'nbx-nby-nbz'), shape=1, size=4) +
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geom_point(data=D2, aes(x=nb2, y=time, colour = 'nby-nbz'), shape=1, size=4) +
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labs(x="nb", y="Time (s)",
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title=sprintf("Saiph-Heat3D granularity & blocking"),
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subtitle=input_file1) +
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theme_bw() +
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theme(plot.subtitle=element_text(size=8)) +
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#theme(legend.position = c(0.5, 0.88)) +
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theme(legend.position = "right") +
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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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scale_colour_discrete("Blocked directions")
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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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