Minor graph fixes, see details below
- Fix to SCPFmastershipFailoverLat.R unreferenced variable
- Removed misleading comments
- Fix to maximum y-axis in various SCPF scripts
- Minor code refractoring
- Changed contact info to university email
Change-Id: I825f435827f824c094b61afc9abe2559e989cdf3
diff --git a/TestON/JenkinsFile/scripts/SCPFscaleTopo.R b/TestON/JenkinsFile/scripts/SCPFscaleTopo.R
index cbc0291..de22c3a 100644
--- a/TestON/JenkinsFile/scripts/SCPFscaleTopo.R
+++ b/TestON/JenkinsFile/scripts/SCPFscaleTopo.R
@@ -18,7 +18,7 @@
# along with TestON. If not, see <http://www.gnu.org/licenses/>.
#
# If you have any questions, or if you don't understand R,
-# please contact Jeremy Ronquillo: jeremyr@opennetworking.org
+# please contact Jeremy Ronquillo: j_ronquillo@u.pacific.edu
# **********************************************************
# STEP 1: File management.
@@ -26,9 +26,7 @@
print( "STEP 1: File management." )
-# Command line arguments are read. Args usually include the database filename and the output
-# directory for the graphs to save to.
-# ie: Rscript SCPFgraphGenerator SCPFsampleDataDB.csv ~/tmp/
+# Command line arguments are read.
print( "Reading commmand-line args." )
args <- commandArgs( trailingOnly=TRUE )
@@ -46,9 +44,7 @@
q() # basically exit(), but in R
}
-# Filenames for output graphs include the testname and the graph type.
-# See the examples below. paste() is used to concatenate strings.
-
+# paste() is used to concatenate strings
outputFile <- paste( args[ 7 ], args[ 5 ], sep="" )
outputFile <- paste( outputFile, args[ 6 ], sep="_" )
outputFile <- paste( outputFile, "_graph.jpg", sep="" )
@@ -80,7 +76,7 @@
# Parse lists into data frames.
dataFrame <- melt( avgs ) # This is where reshape2 comes in. Avgs list is converted to data frame
-dataFrame$scale <- fileData$scale # Add node scaling to the data frame.
+dataFrame$scale <- fileData$scale # Add node scaling to the data frame.
colnames( dataFrame ) <- c( "ms", "type", "scale")
@@ -114,20 +110,12 @@
print( "Generating fundamental graph data." )
-# Calculate window to display graph, based on the lowest and highest points of the data.
-if ( min( avgsSum ) < 0){
- yWindowMin <- min( avgsSum ) * 1.05
-} else {
- yWindowMin <- 0
-}
-yWindowMax <- max( avgsSum ) * 1.2
-
theme_set( theme_grey( base_size = 20 ) ) # set the default text size of the graph.
# Create the primary plot here.
# ggplot contains the following arguments:
# - data: the data frame that the graph will be based off of
-# - aes: the asthetics of the graph which require:
+# - aes: the asthetics of the graph which require:
# - x: x-axis values (usually node scaling)
# - y: y-axis values (usually time in milliseconds)
# - fill: the category of the colored side-by-side bars (usually type)
@@ -136,7 +124,6 @@
# Formatting the plot
width <- 0.6 # Width of the bars.
xScaleConfig <- scale_x_continuous( breaks = dataFrame$iterative, label = dataFrame$scale )
-yLimit <- ylim( yWindowMin, yWindowMax )
xLabel <- xlab( "Scale" )
yLabel <- ylab( "Latency (ms)" )
fillLabel <- labs( fill="Type" )
@@ -144,7 +131,7 @@
theme <- theme( plot.title=element_text( hjust = 0.5, size = 28, face='bold' ) )
# Store plot configurations as 1 variable
-fundamentalGraphData <- mainPlot + xScaleConfig + yLimit + xLabel + yLabel + fillLabel + theme
+fundamentalGraphData <- mainPlot + xScaleConfig + xLabel + yLabel + fillLabel + theme
# Create the stacked bar graph with error bars.
# geom_bar contains: