Mercurial > repos > vmarcon > normalization
changeset 0:79f00bc83ecc draft default tip
planemo upload commit a2411926bebc2ca3bb31215899a9f18a67e59556
| author | vmarcon |
|---|---|
| date | Thu, 18 Jan 2018 06:20:30 -0500 |
| parents | |
| children | |
| files | LICENSE normalization.R normalization.xml normalization_galaxy.R test-data/decathlon.tsv test-data/log_file test-data/output_file |
| diffstat | 7 files changed, 1315 insertions(+), 0 deletions(-) [+] |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/LICENSE Thu Jan 18 06:20:30 2018 -0500 @@ -0,0 +1,674 @@ + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/> + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. By contrast, +the GNU General Public License is intended to guarantee your freedom to +share and change all versions of a program--to make sure it remains free +software for all its users. We, the Free Software Foundation, use the +GNU General Public License for most of our software; it applies also to +any other work released this way by its authors. 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If not, see <http://www.gnu.org/licenses/>. + +Also add information on how to contact you by electronic and paper mail. + + If the program does terminal interaction, make it output a short +notice like this when it starts in an interactive mode: + + {project} Copyright (C) {year} {fullname} + This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. + This is free software, and you are welcome to redistribute it + under certain conditions; type `show c' for details. + +The hypothetical commands `show w' and `show c' should show the appropriate +parts of the General Public License. Of course, your program's commands +might be different; for a GUI interface, you would use an "about box". + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU GPL, see +<http://www.gnu.org/licenses/>. + + The GNU General Public License does not permit incorporating your program +into proprietary programs. If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +<http://www.gnu.org/philosophy/why-not-lgpl.html>.
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/normalization.R Thu Jan 18 06:20:30 2018 -0500 @@ -0,0 +1,282 @@ +# R Script implementing different kind of normalisation +# Input : a file containing a table with numeric values +# except for the first column containing sample names +# and the first line containing variable names +# separator expected is <TAB> +# +# Normalization method : +# log, DESeq2, Rlog, Standard_score, Pareto, TSS, TSS+CLR, Pareto +# +# Ouptut : input table with values normalized according +# to the normalization procedure chosen +#----------------------------------------------------------------- +# Authors : luc.jouneau(at)inra.fr +# valentin.marcon(at)inra.fr +# Version : 0.9 +# Date : 30/08/2017 +#----------------------------------------------------------------- + +normalization=function( +########################################################## +# Function input +########################################################## +#Possible values : "log", "DESeq2", "Rlog", "Standard_score", "Pareto", "TSS", "TSS_CLR" +transformation_method="Standard_score", +na_encoding="NA", +#Path to file containg table of values (separator="tab") +input_file="", +#Path to file produced after transformation +output_file="out/table_out.txt", +#Path to file containing messages for user if something bad happens +log_file="log/normalization_report.html", +#Boolean flag (0/1) indicating if variables are in line or in columns +variable_in_line="1") { + +########################################################## +# Read and verify data +########################################################## +#1°) Checks valids for all modules +if (variable_in_line=="1") { + column_use="individual" + line_use="variable" +} else { + line_use="individual" + column_use="variable" +} +log_error=function(message="") { + cat("<HTML><HEAD><TITLE>Normalization report</TITLE></HEAD><BODY>\n",file=log_file,append=F,sep="") + cat("⚠ An error occurred while trying to read your table.\n<BR>",file=log_file,append=T,sep="") + cat("Please check that:\n<BR>",file=log_file,append=T,sep="") + cat("<UL>\n",file=log_file,append=T,sep="") + cat(" <LI> the table you want to process contains the same number of columns for each line</LI>\n",file=log_file,append=T,sep="") + cat(" <LI> the first line of your table is a header line (specifying the name of each ",column_use,")</LI>\n",file=log_file,append=T,sep="") + cat(" <LI> the first column of your table specifies the name of each ",line_use,"</LI>\n",file=log_file,append=T,sep="") + cat(" <LI> both individual and variable names should be unique</LI>\n",file=log_file,append=T,sep="") + cat(" <LI> each value is separated from the other by a <B>TAB</B> character</LI>\n",file=log_file,append=T,sep="") + cat(" <LI> except for first line and first column, table should contain a numeric value</LI>\n",file=log_file,append=T,sep="") + cat(" <LI> this value may contain character '.' as decimal separator or '",na_encoding,"' for missing values</LI>\n",file=log_file,append=T,sep="") + cat("</UL>\n",file=log_file,append=T,sep="") + cat("-------<BR>\nError messages recieved :<BR><FONT color=red>\n",conditionMessage(message),"</FONT>\n",file=log_file,append=T,sep="") + cat("</BODY></HTML>\n",file=log_file,append=T,sep="") + q(save="no",status=1) +} + +tab_in=tryCatch( + { + tab_in=read.table(file=input_file,sep="\t",header=T,quote="\"",na.strings=na_encoding,check.names=FALSE) + }, + error=function(cond) { + log_error(message=cond) + return(NA) + }, + warning=function(cond) { + log_error(message=cond) + return(NA) + }, + finally={ + #Do nothing special + } +) + +if (ncol(tab_in)<2) { + log_error(simpleCondition("The table you want to normalize contains less than two columns.")) +} + +rn=as.character(tab_in[,1]) +if (length(rn)!=length(unique(rn))) { + duplicated_rownames=table(rn) + duplicated_rownames=duplicated_rownames[duplicated_rownames>1] + duplicated_rownames=names(duplicated_rownames) + if (length(duplicated_rownames)>3) { + duplicated_rownames=c(duplicated_rownames[1:3],"...") + } + duplicated_rownames=paste(duplicated_rownames,collapse=", ") + log_error(simpleCondition( + paste("The table you want to normalize have duplicated values in the first column (", + line_use," names) - duplicated ",line_use," names : ",duplicated_rownames,sep="") + )) +} +tab=tab_in[,-1] +rownames(tab)=rn + +#Check all columns are numeric +tab=as.matrix(tab) +cell.with.na=c() +for (i in 1:ncol(tab)) { + na.v1=is.na(tab[,i]) + na.v2=is.na(as.numeric(tab[,i])) + if (sum(na.v1)!=sum(na.v2)) { + sel=which(na.v1!=na.v2) + sel=sel[1] + value=tab[sel,i] + log_error(simpleCondition( + paste("Column '",colnames(tab)[i],"' of your table contains non numeric values. Please check its content (on line #",sel," : value='",value,"').",sep="") + )) + } + if (length(cell.with.na)==0 & sum(na.v1)!=0) { + cell.with.na=c(i,which(na.v1)[1]) + } +} + +#2°) Checks only valid for normalization module +if (transformation_method %in% c("DESeq2","Rlog")) { + #Check there is no missing values + if (length(cell.with.na)!=0) { + log_error(simpleCondition( + paste("Column '",colnames(tab)[cell.with.na[1]],"' of your table contains missing values (see line #",cell.with.na[2],").\n", + transformation_method," normalization does not accept missing values. ",sep="") + )) + } +} +if (transformation_method %in% c("DESeq2","Rlog","TSS","TSS_CLR")) { + #Check values are integer + for (i in 1:ncol(tab)) { + if (!is.integer(tab[,i])) { + sel=which(!is.integer(tab[,i])) + sel=sel[1] + value=tab[sel,i] + log_error(simpleCondition( + paste("Column '",colnames(tab)[i],"' of your table contains non integer values.\n", + transformation_method," normalization only accepts integer values. ", + "Please check its content (on line #",sel," : value=",value,").",sep="") + )) + } + } +} + +if (transformation_method %in% c("log","TSS","TSS_CLR","DESeq2","Rlog")) { + #Check values are positive + for (i in 1:ncol(tab)) { + if (sum(tab[,i]>=0 | is.na(tab[,i]))!=nrow(tab)) { + sel=which(tab[,i]<0) + sel=sel[1] + value=tab[sel,i] + log_error(simpleCondition( + paste("Column '",colnames(tab)[i],"' of your table contains negative values.\n", + transformation_method," normalization only accepts positive or null values. ", + "Please check its content (on line #",sel," : value=",value,").",sep="") + )) + } + } +} + +########################################################## +# End of data checks +########################################################## + +### Transpose if variable are in line ### +if (variable_in_line=="1") { + #Transpose matrix + tab=t(tab) +} + +########################################################## +### Value transformation +########################################################## + +#Avoid null values when there is a log transformation +na.replaced=c() +log.transformed=FALSE +if (transformation_method %in% c("log","TSS_CLR")) { + log.transformed=TRUE + for (idx_col in 1:ncol(tab)) { + sel=tab[,idx_col]==0 + na.replaced=cbind(na.replaced,sel) + tab[sel,idx_col]=1e-2 + } +} + +### log ### +if (transformation_method=="log") { + tab=log2(tab) +} + +### DESeq2 or Rlog ### +if (transformation_method %in% c("DESeq2","Rlog")) { + library(DESeq2) + n <- ncol(tab) + dds <- DESeqDataSetFromMatrix(tab, + colData = data.frame(condition = c("a", rep("b", n - 1))), + design = formula(~ condition)) + colnames(dds) <- colnames(tab) + dds <- estimateSizeFactors(dds) + tab <- switch(transformation_method, + DESeq2 = counts(dds, normalized = TRUE), + Rlog = assay(rlogTransformation(dds)) + ) +} + +### Standard_score ### +if (transformation_method=="Standard_score") { + tab=scale(tab) +} + +### Pareto ### +if (transformation_method=="Pareto") { + tab.centered <- apply(tab, 2, function(x) x - mean(x,na.rm=TRUE)) + tab.sc <- apply(tab.centered, 2, function(x) x/sqrt(sd(x,na.rm=TRUE))) + tab=tab.sc +} + +### TSS ### +if (transformation_method=="TSS") { + tab= t(apply(tab, 1, function(x) x/sum(x,na.rm=TRUE))) +} + +### TSS + CLR avec function de mixOmics ### +if (transformation_method=="TSS_CLR") { + #From http://stackoverflow.com/questions/2602583/geometric-mean-is-there-a-built-in + geometric.mean = function(x, na.rm=TRUE){ + exp(sum(log(x[x > 0]), na.rm=na.rm) / length(x)) + } + tab = t(apply(tab+1e-2,1,function(x) log(x/geometric.mean(x,na.rm=TRUE)))) +} + + +#If there is a log transformation put 0 where there was NA +if (log.transformed) { + for (idx_col in 1:ncol(tab)) { + tab[na.replaced[,idx_col],idx_col]=0 + } +} + +#If there are missing values, replace it with NA_enconding +for (idx_col in 1:ncol(tab)) { + sel=is.na(tab[,idx_col]) + tab[sel,idx_col]=na_encoding +} + +########################################################## +# Prepare and write output table +########################################################## +if (variable_in_line=="1") { + #Transpose matrix again + tab=t(tab) +} + +tab_out=cbind(rownames(tab),tab) +colnames(tab_out)[1]=colnames(tab_in)[1] + +write.table(file=output_file,tab_out,sep="\t",row.names=F,quote=F) + +########################################################## +# Treatment successfull +########################################################## +cat("<HTML><HEAD><TITLE>Normalization report</TITLE></HEAD><BODY>\n",file=log_file,append=F,sep="") +cat(paste("➔ You choose to apply the transformation method :",transformation_method,"<BR>"),file=log_file,append=F,sep="") +cat("✓ Your normalization process is successfull !<BR>",file=log_file,append=T,sep="") +cat("</BODY></HTML>\n",file=log_file,append=T,sep="") + +q(save="no",status=0) + +} # end of function + +########################################################## +# Test +########################################################## +#Used for debug : LJO 6/3/2017 +#normalization() +#setwd("H:/INRA/cati/groupe stats/Galaxy/normalisation") +#normalization(transformation_method="Standard_score",na_encoding="NA",input_file="datasets/valid - decathlon.txt",output_file="out/table_out.txt",log_file="log/normalization.html",variable_in_line="0") +#normalization(transformation_method="Pareto",na_encoding="NA",input_file="datasets/valid - decathlon.txt",output_file="out/table_out.txt",log_file="log/normalization.html",variable_in_line="1") +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/normalization.xml Thu Jan 18 06:20:30 2018 -0500 @@ -0,0 +1,229 @@ +<!--# Copyright (C) 2017 INRA +# This program 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 3 of the License, or +# (at your option) any later version. +# +# This program 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 this program. If not, see http://www.gnu.org/licenses/. +#--> + +<tool id="normalization" name="Normalization" version="1.0.0"> + <description>Normalize your data with some well known methods</description> + <requirements> + <requirement type="package">R</requirement> + <requirement type="package">bioconductor-deseq2</requirement> + <requirement type="package">r-batch</requirement> + </requirements> + <stdio> + <!-- Anything other than zero is an error --> + <exit_code range="1:" level="fatal"/> + <exit_code range=":-1" level="fatal"/> + </stdio> + <command interpreter="Rscript"><![CDATA[ + normalization_galaxy.R + input_file '${input_file}' + transformation_method '${transformation_method}' + na_encoding '${na_encoding}' + output_file '${output_file}' + log_file '${log_file}' + variable_in_line '${variable_in_line}' + ]]></command> + <inputs> + <param format="tabular,csv" name="input_file" type="data" label="Input file"/> + <param name="transformation_method" type="select" label="Data transformation method" help="See the complete help below for more details"> + <option value="log">Log (binary logarithm)</option> + <option value="DESeq2">DESeq2 for NGS counts</option> + <option value="Rlog">RLog (as implemented in DESeq2)</option> + <option value="Standard_score">Standard score (mean=0;sd=1) </option> + <option value="Pareto">Pareto (mean=0;sd moderate)</option> + <option value="TSS">Total sum scaling (TSS)</option> + <option value="TSS_CLR">Total sum scaling + log ratio (TSS+CLR)</option> + <validator type="empty_field" message="Please choose, at least, one data transformation method." /> + </param> + <param name="na_encoding" size="30" type="text" value="NA" label="Label used for Missing values"/> + <param name="variable_in_line" type="select" multiple="false" display="radio" label="Variable in line or column?"> + <option value="1">Line</option> + <option value="0">Column</option> + </param> + </inputs> + <outputs> + <data name="log_file" format="html" label="Normalization_log"/> + <data name="output_file" format_source="input_file" label="Transfo-${transformation_method.value}_${input_file.name}"/> + </outputs> + <tests> + <test> + <param name="input_file" value="decathlon.tsv"/> + <param name="transformation_method" value="log"/> + <param name="na_encoding" value="NA"/> + <param name="variable_in_line" value="0"/> + <output name="log_file" file="log_file"/> + <output name="output_file" file="output_file"/> + </test> + </tests> + <help><![CDATA[ + +========= +Normalize +========= + +----------- +Description +----------- + + - This tool is part of a set of statistical tools made by members of the BIOS4BIOL group ("Normalization", "Summary statistics", "Hierarchical clustering" and "PCAFactoMineR"). + - Please use this Normalization module before using other modules of the suite. + +What it does: + - It normalize your data with some well known methods + +------ + +----------- +Input files +----------- + ++---------------------------+------------+ +| Parameter : num + label | Format | ++===========================+============+ +| 1 : input file | tabular | ++---------------------------+------------+ + + +---------- +Parameters +---------- + +Data transformation method + | Possible values: "log", "DESeq2", "Rlog", "Standard_score", "TSS", "TSS_CLR" + | + +Label used for Missing values: + | Missing value coding character + | + +Variable in line or column: + | Indicate if variables are in lin or in columns + | + + +------------ +Output files +------------ + + +Transfo-<method>_<input file name> + | input file normalized according to the choosen method + | + +Normalization_log + | + +------- +Advices +------- + +Nature of data may change + | Depending on the subjects of the experimentation and/or the technology used to measure a signal on these subjects. + | By instance, when dealing with RNA-Seq data, expression intensity values are expressed as counts, while with microarray technology, it is expressed as fluorescence intensity. + | + +Before to conduct any analysis on a table of data, it is important to: + | Identify the nature of data you are dealing with + | Check if this nature of data is adapted to the type of analysis you want to do + +If your nature of data is not adapted to the analysis you plan to do, you should first transform your data in a scale of values which fits better requirement of your analysis. +This transformation process is named “normalization”. + + +--------------------- +Normalization Methods +--------------------- + +In this Galaxy module, we propose several normalization methods, and we provide some guidelines to help user choose the accurate normalization method: + +Log normalization + | -Objective: Binary logarithm provide homogeneity of variance even if the range of values is pretty large + | -Accepted: values Any positive or null real numbers + | (null values, will stay null after transformation) + | -Range of values: Input: [0;100.000] / Output: [0;17] + | -Adapted for: PCA, HC, SS* + | + +DESeq2 normalization + | -Objective: Obtain comparable counts between samples, whatever the difference of their libraries sequencing depth + | -Accepted values: NGS counts (positive integers ; no missing values) + | (null values, will stay null after transformation) + | -Range of values: Input: [0;100.000] / Output: [0; 100.000] + | -Adapted for: Differential analysis + | + +RLog normalization + | -Objective: Similar to a combination of {DESeq2 + Log} transformation + | -Accepted values: NGS counts (positive integers ; no missing values) + | -Range of values: Input: [0;100.000] / Output: [0; 20] + | -Adapted for: PCA, HC, SS + | + +Standard score normalization + | -Objective: Transform values such as {mean=0 and standard deviation=1} for all variables. + | -Accepted values: No specific constraint + | -Range of values: No specific constraint + | -Adapted for: PCA, HC, SS + | + +Pareto normalization + | -Objective: Transform values such as + | {mean=0 and variance equal to its standard deviation instead of unit variance} for all variables. + | -Accepted values: No specific constraint + | -Range of values: No specific constraint + | -Adapted for: metabolite intensity values before PCA, HC, SS + | + +Total sum scaling normalization (TSS) + | -Objective: Normalizes count data by dividing variable read count by the total number of read counts in each individual sample + | -Accepted values: 16S rRNA amplicon sequencing + | -Range of values: Input: no specific constraint / Output: [0;1[ + | -Adapted for: PCA, HC, SS + | + +Total sum scaling+Log ratio normalization (TSS+CLR) + | -Objective: Transform values such as {mean=0 and standard deviation=1} for all variables. + | -Accepted values: 16S rRNA amplicon sequencing + | -Range of values: Input: no specific constraint / Output: [0;1[ + | -Adapted for: PCA, HC, SS + +(*)PCA: Principal Component Analysis / HC: Hierarchical Clustering / SS: Summary Statistics + +------ + +**Authors**: Luc Jouneau (luc.jouneau@inra.fr), Sarah Maman (sarah.maman@inra.fr) and Valentin Marcon (valentin.marcon@inra.fr) + +Contact : support.sigenae@inra.fr + +E-learning available : Not yet. + +.. class:: infomark + +------------- +Please cite : +------------- + +- (Depending on the help provided you can cite us in acknowledgements, references or both.) + +Acknowledgements + | We wish to thank SIGENAE group and the statistical CATI BIOS4Biol group : Luc Jouneau, Sarah Maman + | Re-packaging was provided by Valentin Marcon (INRA, Migale platform http://migale.jouy.inra.fr), as part of the IFB project 'Galaxy For Life Science' (http://www.france-bioinformatique.fr/fr) + | + +References + | SIGENAE [http://www.sigenae.org/] + | + + ]]></help> +</tool>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/normalization_galaxy.R Thu Jan 18 06:20:30 2018 -0500 @@ -0,0 +1,45 @@ +#!/usr/local/bioinfo/bin/Rscript --vanilla --slave --no-site-file + +# R Script making the bridge between Galaxy and the call of the normalization method +#----------------------------------------------------------------- +# Authors : luc.jouneau(at)inra.fr +# valentin.marcon(at)inra.fr +# Version : 0.9 +# Date : 30/08/2017 +#--------------------------------------------------------------- + +##------------------------------ +## Options +##------------------------------ +strAsFacL <- options()$stringsAsFactors +options(stringsAsFactors = FALSE) + +##------------------------------ +## Libraries laoding +##------------------------------ +# For parseCommandArgs function +library(batch) + +# R script call +source_local <- function(fname) +{ + argv <- commandArgs(trailingOnly = FALSE) + base_dir <- dirname(substring(argv[grep("--file=", argv)], 8)) + source(paste(base_dir, fname, sep="/")) +} + +#Import the different functions used for Normalization +source_local("normalization.R") + +##------------------------------ +## Lecture parametres +##------------------------------ +argLs <- parseCommandArgs(evaluate=FALSE) + +normalization(input_file=argLs[["input_file"]], + transformation_method=argLs[["transformation_method"]], + na_encoding=argLs[["na_encoding"]], + output_file=argLs[["output_file"]], + log_file=argLs[["log_file"]], + variable_in_line=argLs[["variable_in_line"]]) +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/decathlon.tsv Thu Jan 18 06:20:30 2018 -0500 @@ -0,0 +1,42 @@ +"name" "100m" "Long.jump" "Shot.put" "High.jump" "400m" "110m.hurdle" "Discus" "Pole.vault" "Javeline" "1500m" +"SEBRLE" 11.04 7.58 14.83 2.07 49.81 14.69 43.75 5.02 63.19 291.7 +"CLAY" 10.76 7.4 14.26 1.86 49.37 14.05 50.72 4.92 60.15 301.5 +"KARPOV" 11.02 7.3 14.77 2.04 48.37 14.09 48.95 4.92 50.31 300.2 +"BERNARD" 11.02 7.23 14.25 1.92 48.93 14.99 40.87 5.32 62.77 280.1 +"YURKOV" 11.34 7.09 15.19 2.1 50.42 15.31 46.26 4.72 63.44 276.4 +"WARNERS" 11.11 7.6 14.31 1.98 48.68 14.23 41.1 4.92 51.77 278.1 +"ZSIVOCZKY" 11.13 7.3 13.48 2.01 48.62 14.17 45.67 4.42 55.37 268 +"McMULLEN" 10.83 7.31 13.76 2.13 49.91 14.38 44.41 4.42 56.37 285.1 +"MARTINEAU" 11.64 6.81 14.57 1.95 50.14 14.93 47.6 4.92 52.33 262.1 +"HERNU" 11.37 7.56 14.41 1.86 51.1 15.06 44.99 4.82 57.19 285.1 +"BARRAS" 11.33 6.97 14.09 1.95 49.48 14.48 42.1 4.72 55.4 282 +"NOOL" 11.33 7.27 12.68 1.98 49.2 15.29 37.92 4.62 57.44 266.6 +"BOURGUIGNON" 11.36 6.8 13.46 1.86 51.16 15.67 40.49 5.02 54.68 291.7 +"Sebrle" 10.85 7.84 16.36 2.12 48.36 14.05 48.72 5 70.52 280.01 +"Clay" 10.44 7.96 15.23 2.06 49.19 14.13 50.11 4.9 69.71 282 +"Karpov" 10.5 7.81 15.93 2.09 46.81 13.97 51.65 4.6 55.54 278.11 +"Macey" 10.89 7.47 15.73 2.15 48.97 14.56 48.34 4.4 58.46 265.42 +"Warners" 10.62 7.74 14.48 1.97 47.97 14.01 43.73 4.9 55.39 278.05 +"Zsivoczky" 10.91 7.14 15.31 2.12 49.4 14.95 45.62 4.7 63.45 269.54 +"Hernu" 10.97 7.19 14.65 2.03 48.73 14.25 44.72 4.8 57.76 264.35 +"Nool" 10.8 7.53 14.26 1.88 48.81 14.8 42.05 5.4 61.33 276.33 +"Bernard" 10.69 7.48 14.8 2.12 49.13 14.17 44.75 4.4 55.27 276.31 +"Schwarzl" 10.98 7.49 14.01 1.94 49.76 14.25 42.43 5.1 56.32 273.56 +"Pogorelov" 10.95 7.31 15.1 2.06 50.79 14.21 44.6 5 53.45 287.63 +"Schoenbeck" 10.9 7.3 14.77 1.88 50.3 14.34 44.41 5 60.89 278.82 +"Barras" 11.14 6.99 14.91 1.94 49.41 14.37 44.83 4.6 64.55 267.09 +"Smith" 10.85 6.81 15.24 1.91 49.27 14.01 49.02 4.2 61.52 272.74 +"Averyanov" 10.55 7.34 14.44 1.94 49.72 14.39 39.88 4.8 54.51 271.02 +"Ojaniemi" 10.68 7.5 14.97 1.94 49.12 15.01 40.35 4.6 59.26 275.71 +"Smirnov" 10.89 7.07 13.88 1.94 49.11 14.77 42.47 4.7 60.88 263.31 +"Qi" 11.06 7.34 13.55 1.97 49.65 14.78 45.13 4.5 60.79 272.63 +"Drews" 10.87 7.38 13.07 1.88 48.51 14.01 40.11 5 51.53 274.21 +"Parkhomenko" 11.14 6.61 15.69 2.03 51.04 14.88 41.9 4.8 65.82 277.94 +"Terek" 10.92 6.94 15.15 1.94 49.56 15.12 45.62 5.3 50.62 290.36 +"Gomez" 11.08 7.26 14.57 1.85 48.61 14.41 40.95 4.4 60.71 269.7 +"Turi" 11.08 6.91 13.62 2.03 51.67 14.26 39.83 4.8 59.34 290.01 +"Lorenzo" 11.1 7.03 13.22 1.85 49.34 15.38 40.22 4.5 58.36 263.08 +"Karlivans" 11.33 7.26 13.3 1.97 50.54 14.98 43.34 4.5 52.92 278.67 +"Korkizoglou" 10.86 7.07 14.81 1.94 51.16 14.96 46.07 4.7 53.05 317 +"Uldal" 11.23 6.99 13.53 1.85 50.95 15.09 43.01 4.5 60 281.7 +"Casarsa" 11.36 6.68 14.92 1.94 53.2 15.39 48.66 4.4 58.62 296.12
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/log_file Thu Jan 18 06:20:30 2018 -0500 @@ -0,0 +1,1 @@ +➔ You choose to apply the transformation method : log <BR>✓ Your normalization process is successfull !<BR></BODY></HTML>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output_file Thu Jan 18 06:20:30 2018 -0500 @@ -0,0 +1,42 @@ +name 100m Long.jump Shot.put High.jump 400m 110m.hurdle Discus Pole.vault Javeline 1500m +SEBRLE 3.46466826700344 2.92219784839637 3.89044669267991 1.0496307677246 5.63836350589786 3.87676249078156 5.45121111183233 2.32768736417605 5.98162436069636 8.18834157601082 +CLAY 3.4276061727819 2.88752527074159 3.83390207666916 0.895302621333307 5.62556273996637 3.81249822533356 5.66448284036468 2.29865831556452 5.91049283228871 8.23601419190008 +KARPOV 3.46205231879643 2.86789646399265 3.88459792099006 1.02856915219677 5.59604063267114 3.81659970653491 5.61323695471633 2.29865831556452 5.65277328451078 8.22978016673333 +BERNARD 3.46205231879643 2.85399564717639 3.83289001416474 0.941106310946431 5.61264737765832 3.90592847817313 5.35297033824593 2.41142624572647 5.97200330380836 8.12979817318714 +YURKOV 3.5033487351675 2.82578562746479 3.92504996472736 1.0703893278914 5.65592421308382 3.93640237772506 5.53169336086147 2.23878685958712 5.98732086592925 8.1106138055009 +WARNERS 3.47378691161437 2.92599941855622 3.83895176695194 0.985500430304885 5.605257262939 3.83086375675176 5.36106648879432 2.29865831556452 5.6940444130862 8.11945993445933 +ZSIVOCZKY 3.47638168756724 2.86789646399265 3.75274859140713 1.0071955014042 5.603477988254 3.82476785314329 5.51317488460363 2.14404636961671 5.79103261675567 8.06608919045777 +McMULLEN 3.4369613378336 2.86987140617771 3.78240856492737 1.09085343045111 5.64125699872677 3.84599177066457 5.47281266619239 2.14404636961671 5.81685566236649 8.15532422905059 +MARTINEAU 3.54101915313356 2.76765479823735 3.86492897228979 0.963474123974886 5.64789009105921 3.90014226035982 5.57288966842058 2.29865831556452 5.70956635383999 8.03397354344106 +HERNU 3.50716034911752 2.91838623444635 3.84899843040002 0.895302621333307 5.67525138605026 3.9126498648972 5.49153246180432 2.26903314645524 5.83769100042856 8.15532422905059 +BARRAS 3.50207595604579 2.8011586560937 3.81659970653491 0.963474123974886 5.62877359520165 3.85598969730848 5.39574832817903 2.23878685958712 5.79181407116183 8.13955135239879 +NOOL 3.50207595604579 2.86195536414487 3.66448284036468 0.985500430304885 5.62058641045188 3.93451650158608 5.24488705912353 2.20789285164133 5.84398384404833 8.05853297020161 +BOURGUIGNON 3.50589092972996 2.76553474636298 3.75060650483559 0.895302621333307 5.67694435910691 3.96993327469786 5.33949373790173 2.32768736417605 5.77294133783134 8.18834157601082 +Sebrle 3.43962313755712 2.97085365434048 4.03210084316702 1.08406426478847 5.5957423394744 3.81249822533356 5.60644222813161 2.32192809488736 6.13996056954546 8.12933454084779 +Clay 3.38404980679516 2.99276843076892 3.92884403671257 1.04264433740849 5.6202931499486 3.82068956055921 5.64702663265485 2.29278174922785 6.12329372251013 8.13955135239879 +Karpov 3.39231742277876 2.96532254836725 3.99367436175058 1.06350294230616 5.54874485993723 3.80426011563474 5.6906964439777 2.20163386116965 5.79545527204412 8.11951181037098 +Macey 3.44493204894218 2.90110824301451 3.97544676564096 1.10433665981474 5.61382629093404 3.86393845042397 5.59514556799086 2.13750352374994 5.86937792403133 8.05213327492777 +Warners 3.40871186102943 2.95233356636969 3.85598969730848 0.978195629681652 5.58406053442676 3.80838505065609 5.45055144330648 2.29278174922785 5.7915536333886 8.11920052691734 +Zsivoczky 3.44757919654888 2.83592407425437 3.93640237772506 1.08406426478847 5.62643913669732 3.90207357931074 5.51159454153741 2.23266075679027 5.98754825895374 8.07435557599732 +Hernu 3.45549162062847 2.84599177066457 3.87282875953489 1.02147972741045 5.60673831741759 3.83289001416474 5.48284828306847 2.26303440583379 5.85199883711245 8.04630551649358 +Nool 3.43295940727611 2.9126498648972 3.83390207666916 0.910732661902913 5.60910484661896 3.88752527074159 5.39403389536778 2.43295940727611 5.93852104586597 8.1102483878344 +Bernard 3.41818994794577 2.90303827011291 3.88752527074159 1.08406426478847 5.61853233397629 3.82476785314329 5.48381577726426 2.13750352374994 5.78842470743944 8.11014396578449 +Schwarzl 3.45680614923047 2.90496571868403 3.80838505065609 0.956056652412403 5.63691458035588 3.83289001416474 5.40701277351601 2.35049724708413 5.81557542886257 8.09571348425126 +Pogorelov 3.45285896471381 2.86987140617771 3.91647664443772 1.04264433740849 5.66647256884207 3.82883464946806 5.47897180503294 2.32192809488736 5.74011804283313 8.16807034745054 +Schoenbeck 3.44625622988956 2.86789646399265 3.88459792099006 0.910732661902913 5.65248649491816 3.84197311892718 5.47281266619239 2.32192809488736 5.92813340782985 8.12319024045716 +Barras 3.47767732756531 2.80529245560071 3.89820835250872 0.956056652412403 5.62673115067279 3.84498815668261 5.48639259427596 2.20163386116965 6.01234519041983 8.06118215144418 +Smith 3.43962313755712 2.76765479823735 3.9297909977186 0.933572638261024 5.62263756653746 3.80838505065609 5.61529857909211 2.0703893278914 5.9429835981871 8.09138249094364 +Averyanov 3.39917109381982 2.87578006306849 3.85199883711245 0.956056652412403 5.63575439127776 3.84699468696557 5.31759350462347 2.26303440583379 5.76844901518055 8.08225550938395 +Ojaniemi 3.41683974191283 2.90689059560852 3.90400231628369 0.956056652412403 5.61823865559545 3.90785207184596 5.33449676839042 2.20163386116965 5.88898672118656 8.10700778461203 +Smirnov 3.44493204894218 2.82171021503467 3.79493566280354 0.956056652412403 5.61794491742059 3.88459792099006 5.40837220357268 2.23266075679027 5.92789645372882 8.04061850294165 +Qi 3.46727948045998 2.87578006306849 3.76022094646651 0.978195629681652 5.63372181264101 3.88557436437143 5.49601487343776 2.16992500144231 5.92576211367192 8.09080051372945 +Drews 3.44228003525258 2.88362081628567 3.70818723602071 0.910732661902913 5.60021027442009 3.80838505065609 5.32589006103978 2.32192809488736 5.6873406873783 8.09913737463126 +Parkhomenko 3.47767732756531 2.72465027173297 3.97177344719337 1.02147972741045 5.67355642399014 3.89530262133331 5.38887833881199 2.26303440583379 6.04045412134962 8.11862966608686 +Terek 3.44890095114513 2.79493566280354 3.92124588858559 0.956056652412403 5.63110428236588 3.91838623444635 5.51159454153741 2.40599235967584 5.66163560233596 8.18169891109611 +Gomez 3.46988597627446 2.85996954822103 3.86492897228979 0.887525270741587 5.60318122901386 3.84899843040002 5.35579154675365 2.13750352374994 5.92386226807603 8.07521171134824 +Turi 3.46988597627446 2.78868571061353 3.76765479823735 1.02147972741045 5.69125497865014 3.83390207666916 5.31578357458946 2.26303440583379 5.89093302170554 8.17995883726209 +Lorenzo 3.47248777146274 2.81352468929781 3.72465027173297 0.887525270741587 5.6246858105254 3.9429835981871 5.32984117653063 2.16992500144231 5.86690797814317 8.0393577651597 +Karlivans 3.50207595604579 2.85996954822103 3.73335434061383 0.978195629681652 5.65935375917005 3.90496571868403 5.43762724831895 2.16992500144231 5.72574115650395 8.12241388837423 +Korkizoglou 3.44095219802964 2.82171021503467 3.88849973551412 0.956056652412403 5.67694435910691 3.90303827011291 5.52575569282949 2.23266075679027 5.7292808460274 8.30833903013941 +Uldal 3.48928602262588 2.80529245560071 3.75808993420181 0.887525270741587 5.67101024127845 3.91552090075196 5.42660022616993 2.16992500144231 5.90689059560852 8.13801575348757 +Casarsa 3.50589092972996 2.73984810269933 3.89917563048051 0.956056652412403 5.73335434061383 3.94392132655349 5.6046644151637 2.13750352374994 5.87332106291482 8.21003812347289