TestON/JenkinsFile/scripts/SCPFscalingMaxIntents.R

# 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: jeremyr@opennetworking.org

# **********************************************************
# STEP 1: File management.
# **********************************************************

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/
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[ 8 ] ) ){
    print( "Usage: Rscript SCPFInstalledIntentsFlows <has-flowObj> <database-host> <database-port> <database-user-id> <database-password> <test-name> <branch-name> <directory-to-save-graphs>" )
    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.

outputFile <- paste( args[ 8 ], args[ 6 ], sep="" )
if ( args[ 1 ] == "y" ){
    outputFile <- paste( outputFile, "flowObj", sep="_" )
}
outputFile <- paste( outputFile, args[ 7 ], sep="_" )
outputFile <- paste( outputFile, "_errGraph.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 ] )

command  <- "SELECT * FROM max_intents_"
if ( args[ 1 ] == "y" ){
    command <- paste( command, "fobj_", sep="" )
}
command <- paste( command, "tests WHERE branch = '", sep = "" )
command <- paste( command, args[ 7 ], sep="" )
command <- paste( command, "' AND date IN ( SELECT MAX( date ) FROM max_intents_", sep="" )
if ( args[ 1 ] == "y" ){
    command <- paste( command, "fobj_", sep="" )
}
command <- paste( command, "tests WHERE branch = '", sep = "" )
command <- paste( command, args[ 7 ], sep = "" )
command <- paste( command, "' ) ", sep="" )

print( paste( "Sending SQL command:", command ) )

fileData <- dbGetQuery( con, command )

if ( args[ 1 ] == "y" ){
    chartTitle <- "Number of Installed Intents & Flows with Flow Objectives"
} else {
    chartTitle <- "Number of Installed Intents & Flows"
}

# **********************************************************
# STEP 2: Organize data.
# **********************************************************

fileDataNames <- names( fileData )

avgs <- c()

print( "Sorting data." )
avgs <- c( fileData[ 'max_intents_ovs' ], fileData[ 'max_flows_ovs' ] )

dataFrame <- melt( avgs )
dataFrame$scale <- fileData$scale

colnames( dataFrame ) <- c( "ms", "type", "scale" )

dataFrame$type <- as.character( dataFrame$type )
dataFrame$type <- factor( dataFrame$type, levels=unique( dataFrame$type ) )

dataFrame <- na.omit( dataFrame )   # Omit any data that doesn't exist

print( "Data Frame Results:" )
print( dataFrame )

# **********************************************************
# STEP 3: Generate graphs.
# **********************************************************

print( "Generating fundamental graph data." )

theme_set( theme_grey( base_size = 20 ) )   # set the default text size of the graph.

mainPlot <- ggplot( data = dataFrame, aes( x = scale, y = ms, fill = type ) )
xScaleConfig <- scale_x_continuous( breaks=c( 1, 3, 5, 7, 9) )
xLabel <- xlab( "Scale" )
yLabel <- ylab( "Max Number of Intents/Flow Rules" )
fillLabel <- labs( fill="Type" )
theme <- theme( plot.title=element_text( hjust = 0.5, size = 28, face='bold' ) )

fundamentalGraphData <- mainPlot + xScaleConfig + xLabel + yLabel + fillLabel + theme


print( "Generating bar graph bars." )
width <- 1.3
barGraphFormat <- geom_bar( stat="identity", position=position_dodge( ), width = width )
title <- ggtitle( chartTitle )
result <- fundamentalGraphData + barGraphFormat + title


print( paste( "Saving bar chart to", outputFile ) )
ggsave( outputFile, width = 10, height = 6, dpi = 200 )

print( paste( "Successfully wrote bar chart out to", outputFile ) )