Nai

paper/main.tex

@@ -45,7 +45,8 @@
 \usepackage{graphicx}
 \graphicspath{
 	{./pictures/},
-  {../fig/}
+  {../fig/},
+  {../}
 }
 
 \usepackage[backend=bibtex, style=numeric]{biblatex}
@@ -159,9 +160,11 @@ Potentially, analyze how the rankings of different similarities look like.
 
 \begin{figure}
 \begin{subfigure}{0.8\textwidth}
+\centering
 \includegraphics[width=\textwidth]{job-timeseries4296426}
 \caption{Job-S} \label{fig:job-S}
 \end{subfigure}
+\centering
 
 \caption{Reference jobs: timeline of mean IO activity}
 \label{fig:refJobs}
@@ -171,19 +174,76 @@ Potentially, analyze how the rankings of different similarities look like.
 \begin{figure}\ContinuedFloat
 
 \begin{subfigure}{0.8\textwidth}
+\centering
 \includegraphics[width=\textwidth]{job-timeseries5024292}
 \caption{Job-M} \label{fig:job-M}
 \end{subfigure}
+\centering
 
 \begin{subfigure}{0.8\textwidth}
+\centering
 \includegraphics[width=\textwidth]{job-timeseries7488914-30.pdf}
 \caption{Job-L (first 30 segments of 400; remaining segments are similar)}
 \label{fig:job-L}
 \end{subfigure}
+\centering
 \caption{Reference jobs: timeline of mean IO activity; non-shown timelines are 0}
 \end{figure}
 
 
+
+\begin{figure}
+
+\begin{subfigure}{0.8\textwidth}
+\centering
+\includegraphics[width=\textwidth]{job_similarities_4296426-out/ecdf.png}
+\caption{Job-S} \label{fig:ecdf-job-S}
+\end{subfigure}
+\centering
+
+\begin{subfigure}{0.8\textwidth}
+\centering
+\includegraphics[width=\textwidth]{job_similarities_5024292-out/ecdf.png}
+\caption{Job-M} \label{fig:ecdf-job-M}
+\end{subfigure}
+\centering
+
+\begin{subfigure}{0.8\textwidth}
+\centering
+\includegraphics[width=\textwidth]{job_similarities_7488914-out/ecdf.png}
+\caption{Job-L} \label{fig:ecdf-job-L}
+\end{subfigure}
+\centering
+\caption{Empirical cumulative density function}
+\label{fig:ecdf}
+\end{figure}
+
+
+\begin{figure}
+
+\begin{subfigure}{0.5\textwidth}
+\centering
+\includegraphics[width=\textwidth]{job_similarities_4296426-out/hist-sim}
+\caption{Job-S} \label{fig:hist-job-S}
+\end{subfigure}
+\begin{subfigure}{0.5\textwidth}
+\centering
+\includegraphics[width=\textwidth]{job_similarities_5024292-out/hist-sim}
+\caption{Job-M} \label{fig:hist-job-M}
+\end{subfigure}
+
+\begin{subfigure}{0.5\textwidth}
+\centering
+\includegraphics[width=\textwidth]{job_similarities_7488914-out/hist-sim}
+\caption{Job-L} \label{fig:hist-job-L}
+\end{subfigure}
+\centering
+\caption{Histogram for the number of jobs (bin width: 2.5\%, numbers are the actual job counts)}
+\label{fig:ecdf}
+\end{figure}
+
+
+
 \section{Summary and Conclusion}
 \label{sec:summary}
 

scripts/plot.R

@@ -3,16 +3,15 @@
 library(ggplot2)
 library(dplyr)
 require(scales)
-#library(hrbrthemes)
 
-file = "job_similarities_5024292.csv"
+plotjobs = FALSE
-file = "job_similarities_7488914.csv"
 
 # Color scheme
 plotcolors <- c("#CC0000", "#FFA500", "#FFFF00", "#008000", "#9999ff", "#000066")
 
 # Parse job from command line
 args = commandArgs(trailingOnly = TRUE)
+file = "job_similarities_5024292.csv" # for manual execution
 file = args[1]
 
 data = read.csv(file)
@@ -22,7 +21,7 @@ data$alg_id = as.factor(data$alg_id)
 cat("Job count:")
 cat(nrow(data))
 
-# empirical cummulative density function (ECDF)
+# empirical cumulative density function (ECDF)
 ggplot(data, aes(similarity, color=alg_name, group=alg_name)) + stat_ecdf(geom = "step") + xlab("SIM") + ylab("Fraction of jobs") + theme(legend.position=c(0.9, 0.4)) + scale_color_brewer(palette = "Set2")
 ggsave("ecdf.png", width=8, height=3)
 
@@ -52,8 +51,11 @@ plotJobs = function(jobs){
 
     # print the job timelines
     r = e[ordered, ]
-    #prefix = do.call("sprintf", list("%s-%.0f-", level, r$similarity))
+
-    #system(sprintf("scripts/plot-single-job.py %s %s", paste(r$jobid, collapse=","), paste(prefix, collapse=",")))
+    if (plotjobs) {
+      prefix = do.call("sprintf", list("%s-%.0f-", level, r$similarity))
+      system(sprintf("scripts/plot-single-job.py %s %s", paste(r$jobid, collapse=","), paste(prefix, collapse=",")))
+    }
   }
 
 # Store the job ids in a table, each column is one algorithm