forked from rarias/bscpkgs
78 lines
1.8 KiB
R
78 lines
1.8 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_file = "input1.json"
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if (length(args)>0) { input_file = args[1] }
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input_file2 = "input2.json"
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if (length(args)>0) { input_file2 = args[1] }
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# Load the dataset in NDJSON format
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dataset = jsonlite::stream_in(file(input_file)) %>%
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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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df = select(dataset, config.nby, time) %>%
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rename(nby=config.nby)
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df$nby = as.factor(df$nby)
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df2 = select(dataset2, config.nbz, time) %>%
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rename(nbz=config.nbz)
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df2$nbz = as.factor(df2$nbz)
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# Normalize the time by the median
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D=group_by(df, nby) %>%
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mutate(tnorm = time / median(time) - 1) %>%
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mutate(bad = max(ifelse(abs(tnorm) >= 0.01, 1, 0)))
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D$bad = as.factor(D$bad)
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print(D)
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D2=group_by(df2, nbz) %>%
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mutate(tnorm = time / median(time) - 1) %>%
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mutate(bad = max(ifelse(abs(tnorm) >= 0.01, 1, 0)))
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D2$bad = as.factor(D2$bad)
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print(D)
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print(D2)
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png("scatter-blockY8Z_yZ8.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=D, aes(x=nby, y=time, colour="nby blocks - nbz = 8"), shape=1, size=3) +
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geom_point(data=D2, aes(x=nbz, y=time, colour="nby = 8 - nbz blocks"), shape=1, size=3) +
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labs(x="nb", y="Time (s)",
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title=sprintf("Saiph-Heat3D blockingY/Z"),
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subtitle=input_file) +
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theme_bw() +
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theme(plot.subtitle=element_text(size=8)) +
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theme(legend.position = "right") +
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geom_point(shape=21, size=3) +
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scale_colour_discrete("Blocked directions")
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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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