blob: 93b30601d1dc0d995f525ae84f0ec1343764c233 [file] [log] [blame]
# 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
# This is the R script that generates the SCPF front page graphs.
# **********************************************************
# STEP 1: Data management.
# **********************************************************
database_host = 1
database_port = 2
database_u_id = 3
database_pw = 4
graph_title = 5
branch_name = 6
num_dates = 7
sql_commands = 8
y_axis = 9
old_flow = 10
save_directory = 11
print( "**********************************************************" )
print( "STEP 1: Data management." )
print( "**********************************************************" )
# ----------------
# Import Libraries
# ----------------
print( "Importing libraries." )
library( ggplot2 )
library( reshape2 )
library( RPostgreSQL )
# -------------------
# Check CLI Arguments
# -------------------
print( "Verifying CLI args." )
# Command line arguments are read. Args include the database credentials, test name, branch name, and the directory to output files.
print( "Reading commmand-line args." )
args <- commandArgs( trailingOnly=TRUE )
# Check if sufficient args are provided.
if ( is.na( args[ save_directory ] ) ){
print( paste( "Usage: Rscript testresultgraph.R",
"<database-host>",
"<database-port>",
"<database-user-id>",
"<database-password>",
"<graph-title>", # part of the output filename as well
"<branch-name>", # part of the output filename
"<#-dates>", # part of the output filename
"<SQL-command>",
"<y-axis-title>", # y-axis may be different among other SCPF graphs (ie: batch size, latency, etc. )
"<using-old-flow>",
"<directory-to-save-graph>",
sep = " " ) )
q() # basically exit(), but in R
}
# -------------------------------
# Create Title and Graph Filename
# -------------------------------
print( "Creating title of graph" )
# Title of graph based on command line args.
title <- args[ graph_title ]
title <- paste( title, if( args[ old_flow ] == "y" ) "\nWith Old Flow" else "" )
print( "Creating graph filename." )
# Filenames for the output graph include the testname, branch, and the graph type.
outputFile <- paste( args[ save_directory ],
"SCPF_Front_Page_",
gsub( " ", "_", args[ graph_title ] ),
"_",
args[ branch_name ],
"_",
args[ num_dates ],
"-dates",
if( args[ old_flow ] == "y" ) "_OldFlow" else "",
"_graph.jpg",
sep="" )
# ------------------
# SQL Initialization
# ------------------
print( "Initializing SQL" )
con <- dbConnect( dbDriver( "PostgreSQL" ),
dbname = "onostest",
host = args[ database_host ],
port = strtoi( args[ database_port ] ),
user = args[ database_u_id ],
password = args[ database_pw ] )
print( "Sending SQL command:" )
print( args[ sql_commands ] )
fileData <- dbGetQuery( con, args[ sql_commands ] )
# **********************************************************
# STEP 2: Organize data.
# **********************************************************
print( "**********************************************************" )
print( "STEP 2: Organize Data." )
print( "**********************************************************" )
# Create lists c() and organize data into their corresponding list.
print( "Combine data retrieved from databases into a list." )
if ( ncol( fileData ) > 1 ){
for ( i in 2:ncol( fileData ) ){
fileData[ i ] <- fileData[ i - 1 ] + fileData[ i ]
}
}
# --------------------
# Construct Data Frame
# --------------------
print( "Constructing data frame from combined data." )
dataFrame <- melt( fileData )
dataFrame$date <- fileData$date
colnames( dataFrame ) <- c( "Legend",
"Values" )
# Format data frame so that the data is in the same order as it appeared in the file.
dataFrame$Legend <- as.character( dataFrame$Legend )
dataFrame$Legend <- factor( dataFrame$Legend, levels=unique( dataFrame$Legend ) )
# Adding a temporary iterative list to the dataFrame so that there are no gaps in-between date numbers.
dataFrame$iterative <- rev( seq( 1, nrow( fileData ), by = 1 ) )
dataFrame <- na.omit( dataFrame ) # Omit any data that doesn't exist
print( "Data Frame Results:" )
print( dataFrame )
# **********************************************************
# STEP 3: Generate graphs.
# **********************************************************
print( "**********************************************************" )
print( "STEP 3: Generate Graph." )
print( "**********************************************************" )
# -------------------
# Main Plot Generated
# -------------------
print( "Creating main plot." )
# 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 iterative, but it will become date # later)
# - y: y-axis values (usually tests)
# - color: the category of the colored lines (usually legend of test)
mainPlot <- ggplot( data = dataFrame, aes( x = iterative,
y = Values,
color = Legend ) )
# -------------------
# Main Plot Formatted
# -------------------
print( "Formatting main plot." )
limitExpansion <- expand_limits( y = 0 )
maxYDisplay <- max( dataFrame$Values ) * 1.05
yBreaks <- ceiling( max( dataFrame$Values ) / 10 )
yScaleConfig <- scale_y_continuous( breaks = seq( 0, maxYDisplay, by = yBreaks ) )
# ------------------------------
# Fundamental Variables Assigned
# ------------------------------
print( "Generating fundamental graph data." )
theme_set( theme_grey( base_size = 22 ) ) # set the default text size of the graph.
xLabel <- xlab( "Build" )
yLabel <- ylab( args[ y_axis ] )
imageWidth <- 15
imageHeight <- 10
imageDPI <- 200
# Set other graph configurations here.
theme <- theme( axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
plot.title = element_text( size = 32, face='bold', hjust = 0.5 ),
legend.position = "bottom",
legend.text = element_text( size=22 ),
legend.title = element_blank(),
legend.key.size = unit( 1.5, 'lines' ),
legend.direction = 'horizontal' )
# Colors used for the lines.
# Note: graphs that have X lines will use the first X colors in this list.
colors <- scale_color_manual( values=c( "#111111", # black
"#008CFF", # blue
"#FF3700", # red
"#00E043", # green
"#EEB600", # yellow
"#E500FF") ) # purple (not used)
wrapLegend <- guides( color = guide_legend( nrow = 2, byrow = TRUE ) )
title <- ggtitle( title )
fundamentalGraphData <- mainPlot +
limitExpansion +
yScaleConfig +
xLabel +
yLabel +
theme +
colors +
wrapLegend +
title
# ----------------------------
# Generating Line Graph Format
# ----------------------------
print( "Generating line graph." )
lineGraphFormat <- geom_line( size = 0.75 )
pointFormat <- geom_point( size = 1.75 )
result <- fundamentalGraphData +
lineGraphFormat +
pointFormat
# -----------------------
# Exporting Graph to File
# -----------------------
print( paste( "Saving result graph to", outputFile ) )
ggsave( outputFile,
width = imageWidth,
height = imageHeight,
dpi = imageDPI )
print( paste( "[SUCCESS] Successfully wrote result graph out to", outputFile ) )