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# Copyright 2017 Open Networking Foundation (ONF)
#
# Please refer questions to either the onos test mailing list at <onos-test@onosproject.org>,
# the System Testing Plans and Results wiki page at <https://wiki.onosproject.org/x/voMg>,
# or the System Testing Guide page at <https://wiki.onosproject.org/x/WYQg>
#
# TestON is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 2 of the License, or
# (at your option) any later version.
#
# TestON is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# 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: j_ronquillo@u.pacific.edu
# **********************************************************
# STEP 1: File management.
# **********************************************************
print( "STEP 1: File management." )
# Command line arguments are read.
print( "Reading commmand-line args." )
args <- commandArgs( trailingOnly=TRUE )
# Import libraries to be used for graphing and organizing data, respectively.
# Find out more about ggplot2: https://github.com/tidyverse/ggplot2
# reshape2: https://github.com/hadley/reshape
print( "Importing libraries." )
library( ggplot2 )
library( reshape2 )
library( RPostgreSQL ) # For databases
# Normal usage
# Check if sufficient args are provided.
if ( is.na( args[ 9 ] ) ){
print( "Usage: Rscript SCPFflowTp1g.R <has-flow-obj> <database-host> <database-port> <database-user-id> <database-password> <test-name> <branch-name> <has-neighbors> <directory-to-save-graphs>" )
q() # basically exit(), but in R
}
# paste() is used to concatenate strings.
errBarOutputFile <- paste( args[ 9 ], args[ 6 ], sep="" )
errBarOutputFile <- paste( errBarOutputFile, args[ 7 ], sep="_" )
if ( args[ 8 ] == 'y' ){
errBarOutputFile <- paste( errBarOutputFile, "all-neighbors", sep="_" )
} else {
errBarOutputFile <- paste( errBarOutputFile, "no-neighbors", sep="_" )
}
if ( args[ 1 ] == 'y' ){
errBarOutputFile <- paste( errBarOutputFile, "flowObj", sep="_")
}
errBarOutputFile <- paste( errBarOutputFile, "_graph.jpg", sep="" )
print( "Reading from databases." )
con <- dbConnect( dbDriver( "PostgreSQL" ), dbname="onostest", host=args[ 2 ], port=strtoi( args[ 3 ] ), user=args[ 4 ],password=args[ 5 ] )
commandNeighborModifier <- ""
flowObjModifier <- ""
if ( args[ 1 ] == 'y' ){
flowObjModifier <- "_fobj"
}
if ( args[ 8 ] == 'y' ){
commandNeighborModifier <- "scale=1 OR NOT "
}
command <- paste( "SELECT scale, avg( avg ), avg( std ) FROM flow_tp", flowObjModifier, sep="" )
command <- paste( command, "_tests WHERE (", sep="" )
command <- paste( command, commandNeighborModifier, sep="" )
command <- paste( command, "neighbors = 0 ) AND branch = '", sep="" )
command <- paste( command, args[ 7 ], sep="" )
command <- paste( command, "' AND date IN ( SELECT max( date ) FROM flow_tp", sep="" )
command <- paste( command, flowObjModifier, sep="" )
command <- paste( command, "_tests WHERE branch='", sep="" )
command <- paste( command, args[ 7 ], sep="" )
command <- paste( command, "' ) GROUP BY scale ORDER BY scale", sep="" )
print( paste( "Sending SQL command:", command ) )
fileData <- dbGetQuery( con, command )
title <- paste( args[ 6 ], args[ 7 ], sep="_" )
# **********************************************************
# STEP 2: Organize data.
# **********************************************************
print( "STEP 2: Organize data." )
# Create lists c() and organize data into their corresponding list.
print( "Sorting data." )
colnames( fileData ) <- c( "scale", "avg", "std" )
avgs <- c( fileData[ 'avg' ] )
# 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$std <- fileData$std
colnames( dataFrame ) <- c( "throughput", "type", "scale", "std" )
dataFrame <- na.omit( dataFrame ) # Omit any data that doesn't exist
print( "Data Frame Results:" )
print( dataFrame )
# **********************************************************
# STEP 3: Generate graphs.
# **********************************************************
print( "STEP 3: Generate graphs." )
# 1. Graph fundamental data is generated first.
# These are variables that apply to all of the graphs being generated, regardless of type.
#
# 2. Type specific graph data is generated.
# Data specific for the error bar and stacked bar graphs are generated.
#
# 3. Generate and save the graphs.
# Graphs are saved to the filename above, in the directory provided in command line args
print( "Generating fundamental graph data." )
# 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:
# - 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)
theme_set( theme_grey( base_size = 20 ) ) # set the default text size of the graph.
mainPlot <- ggplot( data = dataFrame, aes( x = scale, y = throughput, ymin = throughput - std, ymax = throughput + std, fill = type ) )
# Formatting the plot
width <- 0.7 # Width of the bars.
xScaleConfig <- scale_x_continuous( breaks = dataFrame$scale, label = dataFrame$scale )
xLabel <- xlab( "Scale" )
yLabel <- ylab( "Throughput (,000 Flows/sec)" )
fillLabel <- labs( fill="Type" )
chartTitle <- "Flow Throughput Test"
if ( args[ 1 ] == 'y' ){
chartTitle <- paste( chartTitle, " with Flow Objectives", sep="" )
}
chartTitle <- paste( chartTitle, "\nNeighbors =", sep="" )
if ( args[ 8 ] == 'y' ){
chartTitle <- paste( chartTitle, "Cluster Size - 1" )
} else {
chartTitle <- paste( chartTitle, "0" )
}
theme <- theme( plot.title=element_text( hjust = 0.5, size = 28, face='bold' ) )
# Store plot configurations as 1 variable
fundamentalGraphData <- mainPlot + xScaleConfig + xLabel + yLabel + fillLabel + theme
# Create the stacked bar graph with error bars.
# geom_bar contains:
# - stat: data formatting (usually "identity")
# - width: the width of the bar types (declared above)
# geom_errorbar contains similar arguments as geom_bar.
print( "Generating bar graph with error bars." )
barGraphFormat <- geom_bar( stat = "identity", width = width, fill="#FFA94F" )
errorBarFormat <- geom_errorbar( width = width, position=position_dodge(), color=rgb( 140,140,140, maxColorValue=255 ) )
values <- geom_text( aes( x=dataFrame$scale, y=dataFrame$throughput + 0.04 * max( dataFrame$throughput ), label = format( dataFrame$throughput, digits=3, big.mark = ",", scientific = FALSE ) ), size = 5, fontface = "bold" )
title <- ggtitle( paste( chartTitle, "" ) )
result <- fundamentalGraphData + barGraphFormat + errorBarFormat + title + values
# Save graph to file
print( paste( "Saving bar chart with error bars to", errBarOutputFile ) )
ggsave( errBarOutputFile, width = 10, height = 6, dpi = 200 )
print( paste( "Successfully wrote bar chart with error bars out to", errBarOutputFile ) )