From 47c9d5df446cd06ae566126135e26cb9279b35f2 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Bj=C3=B6rn=20B=2E=20Brandenburg?= Date: Fri, 19 Mar 2010 10:22:37 -0400 Subject: Add small example. --- README | 33 +++++++++++++++++++++++++++++++++ 1 file changed, 33 insertions(+) create mode 100644 README (limited to 'README') diff --git a/README b/README new file mode 100644 index 0000000..3abde32 --- /dev/null +++ b/README @@ -0,0 +1,33 @@ +This is a simple, quick-n-dirty wrapper for plotting .csv files with +Gnuplot. + +The tool will remove comments (lines starting with a '#' character) before +passing the data to Gnuplot. This makes it possible to have experimental +setup and date information encoded in the .csv file, which is beneficial +for archival reasons. + +You can plot the example file to a PNG graph using the following command: + +./gnuplot.py -o example -f png --xlabel "Time" --ylabel "Supply Bound" \ + example.csv 1 2 "Supply Bound Function" + + +In general, each curve that you want to plot in a graph is defined by four +arguments: + +./gnuplot.py [OPTIONS] \ + \ + \ + ... and so on ... + +For large experiments with many graphs, it is more convenient to wrap gnuplot.py +in a project-specific plotting script. Have a look at plot.py and plot_ohead.py +to see how I did it. Basically, the scrips infer what to plot and how to name +and label the graphs from the name of each .csv file. + +For example, the file name 'hard_rtss09_dist=uni_medium_250.csv' encodes the +following information: 1) it uses hard real-time schedulability tests, it was +using the RTSS'09 experimental setup and overheads, the task sets were generated +with uniform utilization from the range [0.1, 0.4] (medium), and periods were +distributed uniformly in [50, 250]. This is parsed by plot.py, which then +calls gnuplot.py to generate a graph with the correct labels and settings. -- cgit v1.2.2