2 files changed, 139 insertions, 0 deletions
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] \
+   <file1> <xcolumn1> <ycolumn1> <title1> \
+   <file2> <xcolumn2> <ycolumn2> <title2> \
+   ... 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.
diff --git a/example.csv b/example.csv
new file mode 100644
index 0000000..0c70bd7
--- /dev/null
+++ b/example.csv
@@ -0,0 +1,106 @@
+# This is the result of sampling a simple supply bound function.
+# As defined in Sanjoy's G-EDF paper.
+# first column: time
+# second column: supply bound
+0, 0.000000
+1, 0.000000
+2, 0.000000
+3, 0.000000
+4, 0.000000
+5, 0.000000
+6, 0.000000
+7, 0.000000
+8, 0.000000
+9, 0.000000
+10, 0.000000
+11, 0.000000
+12, 0.000000
+13, 0.000000
+14, 0.000000
+15, 1.000000
+16, 5.000000
+17, 9.000000
+18, 9.000000
+19, 9.000000
+20, 9.000000
+21, 9.000000
+22, 9.000000
+23, 9.000000
+24, 9.000000
+25, 10.000000
+26, 14.000000
+27, 18.000000
+28, 18.000000
+29, 18.000000
+30, 18.000000
+31, 18.000000
+32, 18.000000
+33, 18.000000
+34, 18.000000
+35, 19.000000
+36, 23.000000
+37, 27.000000
+38, 27.000000
+39, 27.000000
+40, 27.000000
+41, 27.000000
+42, 27.000000
+43, 27.000000
+44, 27.000000
+45, 28.000000
+46, 32.000000
+47, 36.000000
+48, 36.000000
+49, 36.000000
+50, 36.000000
+51, 36.000000
+52, 36.000000
+53, 36.000000
+54, 36.000000
+55, 37.000000
+56, 41.000000
+57, 45.000000
+58, 45.000000
+59, 45.000000
+60, 45.000000
+61, 45.000000
+62, 45.000000
+63, 45.000000
+64, 45.000000
+65, 46.000000
+66, 50.000000
+67, 54.000000
+68, 54.000000
+69, 54.000000
+70, 54.000000
+71, 54.000000
+72, 54.000000
+73, 54.000000
+74, 54.000000
+75, 55.000000
+76, 59.000000
+77, 63.000000
+78, 63.000000
+79, 63.000000
+80, 63.000000
+81, 63.000000
+82, 63.000000
+83, 63.000000
+84, 63.000000
+85, 64.000000
+86, 68.000000
+87, 72.000000
+88, 72.000000
+89, 72.000000
+90, 72.000000
+91, 72.000000
+92, 72.000000
+93, 72.000000
+94, 72.000000
+95, 73.000000
+96, 77.000000
+97, 81.000000
+98, 81.000000
+99, 81.000000

diff --git a/README b/README new file mode 100644 index 0000000..3abde32 --- /dev/null +++ b/README
@@ -0,0 +1,33 @@
	1	This is a simple, quick-n-dirty wrapper for plotting .csv files with
	2	Gnuplot.
	3
	4	The tool will remove comments (lines starting with a '#' character) before
	5	passing the data to Gnuplot. This makes it possible to have experimental
	6	setup and date information encoded in the .csv file, which is beneficial
	7	for archival reasons.
	8
	9	You can plot the example file to a PNG graph using the following command:
	10
	11	./gnuplot.py -o example -f png --xlabel "Time" --ylabel "Supply Bound" \
	12	example.csv 1 2 "Supply Bound Function"
	13
	14
	15	In general, each curve that you want to plot in a graph is defined by four
	16	arguments:
	17
	18	./gnuplot.py [OPTIONS] \
	19	<file1> <xcolumn1> <ycolumn1> <title1> \
	20	<file2> <xcolumn2> <ycolumn2> <title2> \
	21	... and so on ...
	22
	23	For large experiments with many graphs, it is more convenient to wrap gnuplot.py
	24	in a project-specific plotting script. Have a look at plot.py and plot_ohead.py
	25	to see how I did it. Basically, the scrips infer what to plot and how to name
	26	and label the graphs from the name of each .csv file.
	27
	28	For example, the file name 'hard_rtss09_dist=uni_medium_250.csv' encodes the
	29	following information: 1) it uses hard real-time schedulability tests, it was
	30	using the RTSS'09 experimental setup and overheads, the task sets were generated
	31	with uniform utilization from the range [0.1, 0.4] (medium), and periods were
	32	distributed uniformly in [50, 250]. This is parsed by plot.py, which then
	33	calls gnuplot.py to generate a graph with the correct labels and settings.


diff --git a/example.csv b/example.csv new file mode 100644 index 0000000..0c70bd7 --- /dev/null +++ b/example.csv
@@ -0,0 +1,106 @@
	1	# This is the result of sampling a simple supply bound function.
	2	# As defined in Sanjoy's G-EDF paper.
	3
	4	# first column: time
	5	# second column: supply bound
	6	0, 0.000000
	7	1, 0.000000
	8	2, 0.000000
	9	3, 0.000000
	10	4, 0.000000
	11	5, 0.000000
	12	6, 0.000000
	13	7, 0.000000
	14	8, 0.000000
	15	9, 0.000000
	16	10, 0.000000
	17	11, 0.000000
	18	12, 0.000000
	19	13, 0.000000
	20	14, 0.000000
	21	15, 1.000000
	22	16, 5.000000
	23	17, 9.000000
	24	18, 9.000000
	25	19, 9.000000
	26	20, 9.000000
	27	21, 9.000000
	28	22, 9.000000
	29	23, 9.000000
	30	24, 9.000000
	31	25, 10.000000
	32	26, 14.000000
	33	27, 18.000000
	34	28, 18.000000
	35	29, 18.000000
	36	30, 18.000000
	37	31, 18.000000
	38	32, 18.000000
	39	33, 18.000000
	40	34, 18.000000
	41	35, 19.000000
	42	36, 23.000000
	43	37, 27.000000
	44	38, 27.000000
	45	39, 27.000000
	46	40, 27.000000
	47	41, 27.000000
	48	42, 27.000000
	49	43, 27.000000
	50	44, 27.000000
	51	45, 28.000000
	52	46, 32.000000
	53	47, 36.000000
	54	48, 36.000000
	55	49, 36.000000
	56	50, 36.000000
	57	51, 36.000000
	58	52, 36.000000
	59	53, 36.000000
	60	54, 36.000000
	61	55, 37.000000
	62	56, 41.000000
	63	57, 45.000000
	64	58, 45.000000
	65	59, 45.000000
	66	60, 45.000000
	67	61, 45.000000
	68	62, 45.000000
	69	63, 45.000000
	70	64, 45.000000
	71	65, 46.000000
	72	66, 50.000000
	73	67, 54.000000
	74	68, 54.000000
	75	69, 54.000000
	76	70, 54.000000
	77	71, 54.000000
	78	72, 54.000000
	79	73, 54.000000
	80	74, 54.000000
	81	75, 55.000000
	82	76, 59.000000
	83	77, 63.000000
	84	78, 63.000000
	85	79, 63.000000
	86	80, 63.000000
	87	81, 63.000000
	88	82, 63.000000
	89	83, 63.000000
	90	84, 63.000000
	91	85, 64.000000
	92	86, 68.000000
	93	87, 72.000000
	94	88, 72.000000
	95	89, 72.000000
	96	90, 72.000000
	97	91, 72.000000
	98	92, 72.000000
	99	93, 72.000000
	100	94, 72.000000
	101	95, 73.000000
	102	96, 77.000000
	103	97, 81.000000
	104	98, 81.000000
	105	99, 81.000000
	106