From e0fbbe32a68783d93b0fd8aaf48b56f6b035db06 Mon Sep 17 00:00:00 2001
From: Rodrigo Arias Mallo <rodrigo.arias@bsc.es>
Date: Tue, 23 Feb 2021 12:13:52 +0100
Subject: [PATCH] saiph: update granularity experiment and R script

---
 garlic/fig/saiph/granularity.R             |  14 +--
 garlic/fig/saiph/granularityPerNumBlocks.R | 100 +++++++++++++++++++++
 2 files changed, 107 insertions(+), 7 deletions(-)
 create mode 100644 garlic/fig/saiph/granularityPerNumBlocks.R

diff --git a/garlic/fig/saiph/granularity.R b/garlic/fig/saiph/granularity.R
index ba093a8..d3ed00d 100644
--- a/garlic/fig/saiph/granularity.R
+++ b/garlic/fig/saiph/granularity.R
@@ -42,7 +42,7 @@ png("box.png", width=w*ppi, height=h*ppi, res=ppi)
 p = ggplot(data=D, aes(x=nby, y=tnorm, color=bad)) +
 
 	# Labels
-	labs(x="nby", y="Normalized time",
+	labs(x="nb{y-z}", y="Normalized time",
               title=sprintf("Saiph-Heat3D normalized time"), 
               subtitle=input_file) +
 
@@ -82,16 +82,16 @@ png("scatter.png", width=w*ppi, height=h*ppi, res=ppi)
 ## Create the plot with the normalized time vs nblocks
 p = ggplot(D, aes(x=nby, y=time)) +
 
-	labs(x="nby", y="Time (s)",
-              title=sprintf("Saiph-Heat3D granularity"), 
-              subtitle=input_file) +
+	labs(x="nb{y-z}", y="Time (s)",
+               title=sprintf("Saiph-Heat3D granularity"), 
+               subtitle=input_file) +
 	theme_bw() +
 	theme(plot.subtitle=element_text(size=8)) +
 	theme(legend.position = c(0.5, 0.88)) +
 
-	geom_point(shape=21, size=3) +
-	#scale_x_continuous(trans=log2_trans()) +
-	scale_y_continuous(trans=log2_trans())
+	geom_point(shape=21, size=3)
+	#+ scale_x_continuous(trans=log2_trans())
+	#+ scale_y_continuous(trans=log2_trans())
 
 # Render the plot
 print(p)
diff --git a/garlic/fig/saiph/granularityPerNumBlocks.R b/garlic/fig/saiph/granularityPerNumBlocks.R
new file mode 100644
index 0000000..4e28f1d
--- /dev/null
+++ b/garlic/fig/saiph/granularityPerNumBlocks.R
@@ -0,0 +1,100 @@
+library(ggplot2)
+library(dplyr)
+library(scales)
+library(jsonlite)
+
+args=commandArgs(trailingOnly=TRUE)
+
+# Read the timetable from args[1]
+input_file = "nov24Gran.json"
+if (length(args)>0) { input_file = args[1] }
+
+# Load the dataset in NDJSON format
+dataset = jsonlite::stream_in(file(input_file)) %>%
+	jsonlite::flatten()
+
+
+# We only need the nblocks and time
+df = select(dataset, config.nby, time) %>%
+	rename(nby=config.nby)
+
+df$nby = as.factor(df$nby)
+
+# Normalize the time by the median
+D=group_by(df, nby) %>%
+	mutate(tnorm = time / median(time) - 1) %>%
+	mutate(bad = max(ifelse(abs(tnorm) >= 0.01, 1, 0)))
+
+D$bad = as.factor(D$bad)
+
+
+print(D)
+
+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 nblocks
+p = ggplot(data=D, aes(x=nby, y=tnorm, color=bad)) +
+
+	# Labels
+	labs(x="nb{y-z}", y="Normalized time",
+              title=sprintf("Saiph-Heat3D normalized time"), 
+              subtitle=input_file) +
+
+	# Center the title
+	#theme(plot.title = element_text(hjust = 0.5)) +
+
+	# Black and white mode (useful for printing)
+	#theme_bw() +
+
+	# Add the maximum allowed error lines
+	geom_hline(yintercept=c(-0.01, 0.01),
+		linetype="dashed", color="gray") +
+
+	# Draw boxplots
+	geom_boxplot() +
+	scale_color_manual(values=c("black", "brown")) +
+
+	#scale_y_continuous(breaks = scales::pretty_breaks(n = 10)) +
+
+	theme_bw() +
+
+	theme(plot.subtitle=element_text(size=8)) +
+        theme(legend.position = "none")
+	#theme(legend.position = c(0.85, 0.85))
+
+
+
+
+# Render the plot
+print(p)
+
+## Save the png image
+dev.off()
+#
+png("scatter.png", width=w*ppi, height=h*ppi, res=ppi)
+#
+## Create the plot with the normalized time vs nblocks
+p = ggplot(D, aes(x=nby, y=time)) +
+
+	labs(x="nb{y-z}", y="Time (s)",
+              title=sprintf("Saiph-Heat3D granularity"), 
+              subtitle=input_file) +
+	theme_bw() +
+	theme(plot.subtitle=element_text(size=8)) +
+	theme(legend.position = c(0.5, 0.88)) +
+
+	geom_point(shape=21, size=3) +
+	#scale_x_continuous(trans=log2_trans()) +
+	scale_y_continuous(trans=log2_trans())
+
+# Render the plot
+print(p)
+
+# Save the png image
+dev.off()