Move the plot script to R

garlic/fig/nbody/test/plot.R

@@ -0,0 +1,74 @@
+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)
+
+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="Blocksize", y="Normalized time",
+              title="Nbody granularity",
+              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()
+
+D=group_by(df, blocksize) %>% mutate(tnorm = time / median(time) - 1)
+
+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)) +
+
+	labs(x="Blocksize", y="Time (s)",
+              title="Nbody granularity",
+              subtitle="@expResult@") +
+
+	geom_point(
+		   #position=position_jitter(width=0.2, heigh=0)
+		   shape=21, size=1.5) +
+	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()