| Next changeset 1:c1c85170db1b (2018-12-30) |
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Commit message:
Uploaded |
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added:
blupcal.R blupcal.sh blupcal.xml blupcal_wrapper.R test-data/blup.output test-data/pheno_input.tab.txt |
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| diff -r 000000000000 -r 45d215f2be74 blupcal.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/blupcal.R Sat Dec 29 18:44:05 2018 -0500 |
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| b'@@ -0,0 +1,326 @@\n+# Calculation of BLUE/BLUP\r\n+# Umesh Rosyara, CIMMYT\r\n+blupcal<- function(data, \r\n+ Replication = "Rep", \r\n+ Genotype = "Entry", \r\n+ y = "y", \r\n+ design = "rcbd",\r\n+ Block = NULL,\r\n+ summarizeby = NULL, \r\n+ groupvar1 = NULL, \r\n+ groupvar2 = NULL) {\r\n+\r\n+ suppressMessages(library(arm))\r\n+ # so that it would not throw messages at the stderr channel of Galaxy\r\n+ library(lme4, quietly = TRUE)\r\n+\r\n+ # Basic summary of the variables eused \r\n+ print(paste("Replication variable = ", Replication))\r\n+ print(paste("block variable = ", Block)) \r\n+ print(paste("Genotype variable = ", Genotype))\r\n+ print(paste("summarizeby (included in the model) = ", summarizeby))\r\n+ print(paste("groupvariable 1 (included in the model) = ", groupvar1))\r\n+ print(paste("groupvariable 2 (included in the model) = ", groupvar2))\r\n+ print(paste("design = ", design))\r\n+ print(paste("y = ", y))\r\n+\r\n+ # Summary of data\r\n+ print("*************************************")\r\n+ print("*************************************")\r\n+ print(summary(data))\r\n+ print("*************************************")\r\n+ print("*************************************")\r\n+\r\n+ data_list <- list()\r\n+ if (length(summarizeby) != 0) {\r\n+ data$summarizeby <- as.factor(data[,summarizeby])\r\n+ data_list = split(data, f = data$summarizeby)\r\n+ } else {\r\n+ data$summarizeby <- "none"\r\n+ data_list[[1]] <- data\r\n+ }\r\n+\r\n+ all_results <- list()\r\n+\r\n+ for (i in 1: length(data_list)) {\r\n+ data1 <- data_list[[i]]\r\n+\r\n+ data1$Rep <- as.factor(data1[, Replication])\r\n+ cat("\\ndata1$Rep:\\n")\r\n+ print(data1$Rep)\r\n+\r\n+ data1$Entry <- as.factor(data1[, Genotype])\r\n+ cat("\\ndata1$Entry:\\n")\r\n+ print(data1$Entry)\r\n+\r\n+ if (design == "lattice") {\r\n+ data1$Subblock <- as.factor(data1[, Block])\r\n+ }\r\n+\r\n+ data1$y <- as.numeric(data1[, y])\r\n+ cat("\\ndata1$y:\\n")\r\n+ print(data1$y)\r\n+\r\n+ if (length(groupvar1) != 0) {\r\n+ data1$groupvar1 <- as.factor(data1[, groupvar1])\r\n+ cat("\\ndata1$groupvar1:\\n") \r\n+ print(data1$groupvar1) \r\n+ }\r\n+\r\n+ if (length(groupvar2) != 0) {\r\n+ data1$groupvar2 <- as.factor(data1[, groupvar2])\r\n+ cat("\\ndata1$groupvar2:\\n") \r\n+ print(data1$groupvar2) \r\n+ }\r\n+\r\n+ if (design == "rcbd") {\r\n+ if (length(groupvar1) != 0) {\r\n+ if (length(groupvar2) != 0) {\r\n+ fm1 <- lmer(y ~ 1 + \r\n+ (1|Entry) + \r\n+ (1|groupvar1) + \r\n+ (1|groupvar2) + \r\n+ (1|Entry:groupvar1) + \r\n+ (1|Entry:groupvar2) + \r\n+ (1|Entry:groupvar1:groupvar2) + \r\n+ (1|Rep), \r\n+ data1)\r\n+ fm2 <- lmer(y ~ (-1) + \r\n+ Entry + \r\n+ (1|groupvar1) + \r\n+ (1|groupvar2) + \r\n+ (1|Entry:groupvar1) + \r\n+ (1|Entry:groupvar2) + \r\n+ (1|Entry:groupvar1:groupvar2) + \r\n+ (1|Rep), \r\n+ data1)\r\n+ }\r\n+ if (length(groupvar2) == 0) {\r\n+ fm1 <- lmer(y ~ 1 + \r\n+ (1|Entry) + \r\n+ (1|groupvar1) + \r\n+ (1|Entry:groupvar1) + \r\n+ (1|Rep), \r\n+ data1)\r\n+ fm2 <- lmer(y ~ (-1) + \r\n+ Entry + \r\n+ (1|groupvar1) + \r\n+ (1|Entry:groupvar1) + \r\n+ (1|Rep), \r\n+ data1) \r\n+ }\r\n+ }\r\n+ if (length(groupvar1) == 0) {\r\n+ if (length(groupvar2) != 0) {\r\n+ fm1 <- lmer(y ~ 1 + \r\n+ '..b' # PEV reliability\r\n+ pevReliability <- 1 - (PEV / Vg)\r\n+ names(pevReliability) <- "PEV reliability"\r\n+\r\n+ blupdf = data.frame(genotype = rownames(ranef(fm1)$Entry), \r\n+ blup = blup,\r\n+ BLUP_PEV = PEV, \r\n+ BLUP_pevReliability = pevReliability)\r\n+\r\n+ ######## \r\n+ # BLUE \r\n+ ######## \r\n+ blue <- fixef(fm2)\r\n+\r\n+ bluedf <- data.frame(genotype = substr(names(blue), 6, nchar(names(blue))), \r\n+ blue = blue)\r\n+\r\n+ ######### \r\n+ # MEANS \r\n+ ######### \r\n+ means <- with(data1, tapply(y, Entry, mean, na.rm = TRUE))\r\n+\r\n+ meandf <- data.frame(genotype = names(means), \r\n+ means = means)\r\n+ \r\n+ resultdf1 <- merge(meandf, bluedf, by = "genotype")\r\n+ resultdf <- merge(resultdf1, blupdf, by = "genotype")\r\n+\r\n+ if (length(summarizeby) != 0) {\r\n+ results <- data.frame(row.names = NULL, \r\n+ genotype = resultdf$genotype, \r\n+ blue = resultdf$blue, \r\n+ blup = resultdf$blup,\r\n+ BLUP_PEV = resultdf$BLUP_PEV, \r\n+ pevReliability = resultdf$BLUP_pevReliability, \r\n+ means = resultdf$means, \r\n+ group = levels(data$summarizeby)[i])\r\n+ names(results) <- c(Genotype, \r\n+ paste(y, "_blue", sep = ""), \r\n+ paste(y, "_blup", sep = ""), \r\n+ paste(y, "_PEV", sep = ""), \r\n+ paste(y, "_pevReliability", sep = ""), \r\n+ paste(y, "_means", sep = ""), \r\n+ summarizeby)\r\n+ } else {\r\n+ results <- data.frame(row.names = NULL, \r\n+ genotype = resultdf$genotype, \r\n+ blue = resultdf$blue, \r\n+ blup = resultdf$blup, \r\n+ BLUP_PEV = resultdf$BLUP_PEV, \r\n+ pevReliability = resultdf$BLUP_pevReliability, \r\n+ means = resultdf$means)\r\n+ names(results) <- c(Genotype, \r\n+ paste(y, "_blue", sep = ""), \r\n+ paste(y, "_blup", sep = ""), \r\n+ paste(y, "_PEV", sep = ""), \r\n+ paste(y, "_pevReliability", sep = ""), \r\n+ paste(y, "_means", sep = ""))\r\n+ }\r\n+\r\n+ all_results[[i]] <- results \r\n+ }\r\n+ outdf <- do.call("rbind", all_results)\r\n+ class(outdf) <- c("blupcal", class(outdf))\r\n+ return(outdf)\r\n+}\r\n+\r\n+# plot function of blupcal module \r\n+plot2.blupcal <- function(outdf) {\r\n+ hist_with_box <- function(data, main = main, hist.col, box.col) {\r\n+ histpar <- hist(data, plot = FALSE)\r\n+ hist(data, col = hist.col, main = main, ylim = c(0, max(histpar$density) + max(histpar$density) * 0.3), prob = TRUE)\r\n+ m = mean(data, na.rm = TRUE)\r\n+ std = sd(data, na.rm = TRUE)\r\n+ curve(dnorm(x, mean = m, sd = std), col = "yellow", lwd = 2, add = TRUE, yaxt = "n")\r\n+ boxout <- boxplot(data, plot = FALSE)\r\n+ points(boxout$out, y = rep(max(histpar$density) * 0.3, length(boxout$out)), col = "red", pch = 1)\r\n+ texts <- paste("mean = ", round(mean(data, na.rm = TRUE), 2), " sd = ", round(sd(data, na.rm = TRUE), 2), " n = ", length(data))\r\n+ text(min(histpar$breaks), max(histpar$density) + max(histpar$density) * 0.2, labels = texts, pos = 4)\r\n+ }\r\n+ par(mfrow = c(3,1), mar = c(3.1, 3.1, 1.1, 2.1))\r\n+ hist_with_box(outdf[,2], main = names(outdf)[2], hist.col = "green4", box.col = "green1")\r\n+ hist_with_box(outdf[,3], main = names(outdf)[3], hist.col = "blue4", box.col = "blue1")\r\n+ hist_with_box(outdf[,4], main = names(outdf)[4], hist.col = "gray20", box.col = "gray60")\r\n+}\r\n' |
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| diff -r 000000000000 -r 45d215f2be74 blupcal.sh --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/blupcal.sh Sat Dec 29 18:44:05 2018 -0500 |
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| @@ -0,0 +1,56 @@ +#!/bin/bash +tool_directory=$1 +galaxy_tabular_file=$2 +galaxy_replication_vector_column_index=$3 +galaxy_genotype_vector_column_index=$4 +galaxy_first_y_vector_column_index=$5 +galaxy_last_y_vector_column_index=$6 +galaxy_design=$7 +galaxy_summarize_by=$8 +galaxy_summarize_by_vector_column_index=$9 +galaxy_group_variable_1=${10} +galaxy_group_variable_1_vector_column_index=${11} +galaxy_group_variable_2=${12} +galaxy_group_variable_2_vector_column_index=${13} +galaxy_output_file_path=${14} +block_vector_column_index=${15} + +directory=`dirname $0` +mkdir tmpdir$$ + + +counter=$galaxy_first_y_vector_column_index + +previous="tmpdir$$/output" +echo '' >$previous +while [ $counter -le $galaxy_last_y_vector_column_index ] +do +Rscript --vanilla $directory/blupcal_wrapper.R --tool_directory $tool_directory --tabular_file $galaxy_tabular_file --replication_vector_column_index $galaxy_replication_vector_column_index --genotype_vector_column_index $galaxy_genotype_vector_column_index --y_vector_column_index $counter --design $galaxy_design --summarize_by $galaxy_summarize_by --summarize_by_vector_column_index $galaxy_summarize_by_vector_column_index --group_variable_1 $galaxy_group_variable_1 --group_variable_1_vector_column_index $galaxy_group_variable_1_vector_column_index --group_variable_2 $galaxy_group_variable_2 --group_variable_2_vector_column_index $galaxy_group_variable_2_vector_column_index --output_file_path tmpdir$$/output.$counter --block_vector_column_index $block_vector_column_index +#cat tmpdir$$/output.$counter >>$galaxy_output_file_path +if [ "$counter" -gt "$galaxy_first_y_vector_column_index" ];then +if [[ "$galaxy_summarize_by" == "true" ]];then +cut -f 3-7 tmpdir$$/output.$counter >>tmpdir$$/output.$counter.cut +fi +if [[ "$galaxy_summarize_by" == "false" ]];then +cut -f 2-6 tmpdir$$/output.$counter >>tmpdir$$/output.$counter.cut +fi +mv tmpdir$$/output.$counter.cut tmpdir$$/output.$counter +fi +if [ "$counter" -gt "$galaxy_first_y_vector_column_index" ];then +paste $previous tmpdir$$/output.$counter >>tmpdir$$/output.$counter.$counter +fi +if [ "$counter" -eq "$galaxy_first_y_vector_column_index" ];then +cp tmpdir$$/output.$counter tmpdir$$/output.$counter.$counter +fi +previous="tmpdir$$/output.$counter.$counter" +cp -rf tmpdir$$/output.$counter.$counter $galaxy_output_file_path +#paste $galaxy_output_file_path tmpdir$$/output.$counter >>$galaxy_output_file_path +#cut -f 3-7 tmpdir$$/output.$counter >tmpdir$$/output.$counter.cut +((counter++)) +done + + +echo $galaxy_summarize_by + + + |
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| diff -r 000000000000 -r 45d215f2be74 blupcal.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/blupcal.xml Sat Dec 29 18:44:05 2018 -0500 |
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| @@ -0,0 +1,185 @@ +<tool id="blup_and_blue_multiple2" name="BLUP/BLUE" version="1.11.0"> + <description>calculator (multiple traits)</description> + <requirements> + <requirement type="package" version="1.1_18_1">r-lme4</requirement> + <requirement type="package" version="1.20.2">r-getopt</requirement> + </requirements> + <command interpreter="bash"> +./blupcal.sh $__tool_directory__ $galaxy_tabular_file $galaxy_replication_vector_column_index $galaxy_genotype_vector_column_index $galaxy_first_y_vector_column_index $galaxy_last_y_vector_column_index $galaxy_design_conditional.galaxy_design + $galaxy_summarize_by + $galaxy_summarize_by_vector_column_index + $galaxy_group_variable_1 + $galaxy_group_variable_1_vector_column_index + $galaxy_group_variable_2 + $galaxy_group_variable_2_vector_column_index + $galaxy_output_file_path +#if $galaxy_design_conditional.galaxy_design == "lattice": + $galaxy_design_conditional.galaxy_block_vector_column_index +#else: + NULL +#end if +</command> + <inputs> + <param name="galaxy_tabular_file" + type="data" + format="tab,tabular" + label="Encoded Data" + help="(required) Must be the TAB as both column delimiter and file type" /> + <conditional name="galaxy_design_conditional"> + <param name="galaxy_design" + type="select" + label="Design" + help="(required) Must choose one of the two design options available: Lattice or RCBD"> + <option value="lattice">Lattice</option> + <option value="rcbd" + selected="true">RCBD</option> + </param> + <when value="lattice"> + <param name="galaxy_block_vector_column_index" + type="data_column" + data_ref="galaxy_tabular_file" + use_header_names="true" + label="Block within Replication" + help="(required) Must be the block-vector column name" + value="4" /> + </when> + <when value="rcbd"> + </when> + </conditional> + <param name="galaxy_replication_vector_column_index" + type="data_column" + data_ref="galaxy_tabular_file" + use_header_names="true" + label="Replication Column" + help="(required) Must be the replication-vector column name" + value="1" /> + <param name="galaxy_genotype_vector_column_index" + type="data_column" + data_ref="galaxy_tabular_file" + use_header_names="true" + label="Genotype Column" + help="(required) Must be the genotype-vector column name" + value="2" /> + <param name="galaxy_first_y_vector_column_index" + type="data_column" + data_ref="galaxy_tabular_file" + use_header_names="true" + label="First Y Column" + help="(required) Must be the first y-vector column name" + value="3" /> + <param name="galaxy_last_y_vector_column_index" + type="data_column" + data_ref="galaxy_tabular_file" + use_header_names="true" + label="Last Y Column" + help="(required) Must be the last y-vector column name" + value="3" /> + <param name="galaxy_summarize_by" + type="boolean" + checked="false" + truevalue="true" + falsevalue="false" + label="Summarize By" + help="(optional) It can be summarized by a column chosen" /> + <param name="galaxy_summarize_by_vector_column_index" + type="data_column" + data_ref="galaxy_tabular_file" + use_header_names="true" + label="Summarize By Column" + help="Must be the summarized-vector column name" + value="5" /> + <param name="galaxy_group_variable_1" + type="boolean" + checked="false" + truevalue="true" + falsevalue="false" + label="Variable 1, Factor" + help="(optional) It can choose any column as the group variable 1" /> + <param name="galaxy_group_variable_1_vector_column_index" + type="data_column" + data_ref="galaxy_tabular_file" + use_header_names="true" + label="Variable 1, Factor Column" + help="Must be the group-variable-1-vector column name" + value="6" /> + <param name="galaxy_group_variable_2" + type="boolean" + checked="false" + truevalue="true" + falsevalue="false" + label="Variable 2, Factor" + help="(optional) It can choose any column as the group variable 2" /> + <param name="galaxy_group_variable_2_vector_column_index" + type="data_column" + data_ref="galaxy_tabular_file" + use_header_names="true" + label="Variable 2, Factor Column" + help="Must be the group-variable-2-vector column name" + value="7" /> + </inputs> + <outputs> + <data name="galaxy_output_file_path" + format="tab" + label="BLUP output data" /> + </outputs> + <tests> + <test> +<param name="galaxy_tabular_file" value="pheno_input.tab.txt" /> +<param name="galaxy_replication_vector_column_index" value="1" /> +<param name="galaxy_genotype_vector_column_index" value="18" /> +<param name="galaxy_first_y_vector_column_index" value="13" /> +<param name="galaxy_last_y_vector_column_index" value="14" /> +<param name="galaxy_summarize_by" value="true" /> +<param name="galaxy_summarize_by_vector_column_index" value="3" /> +<output name="galaxy_output_file_path" file="blup.output" compare="sim_size" > +<assert_contents> + <has_n_columns n="12" /> +</assert_contents> +</output> + </test> + </tests> + <help> +**What it does** + +This function calculates BLUP, BLUE and means for Y variable based on the model specified by the user. + +The required variables for RCBD are Genotype and Replication and for lattice required variables are Genotype, Replication (main block) and Block within Replication (sub block or block). User can add additional two X variables (eg. Location, Season etc) in the model and are treated as factor ( even supplied as numerical variable). +If you want to calculate BLUP / BLUE by a grouping variable (eg. Management), then this will calculate BLUP / BLUE for each level of the variable. + +**Model** + +for RCBD design + +without additional factors (variable 1 and variable 2) + +*Y = Genotype + Replication + error* RCBD + +*Y = Genotype + Replication + Block within Replication + error* Lattice + + +With variable 1 + +*Y = Variable 1 + Genotype + Variable 1 : Genotype + Replication + error* RCBD + +*Y = Variable 1 + Genotype + Replication + Variable 1 : Genotype + Block within Replication + error* Lattice + + +With variable 1 and variable 2 + +*Y = Variable 2 + Variable 1 + Genotype + Variable 1 : Genotype + Variable 1: Variable 2 + Variable 1:Variable 2: Genotype + Replication + error* RCBD +*Y = Variable 2 + Variable 1 + Genotype + Variable 1 : Genotype + Variable 1: Variable 2 + Variable 1:Variable 2: Genotype + Replication + Block within Replication + error* RCBD + +**Model details** + +- Genotype is treated as Fixed for BLUE calculation and random for BLUP calculation. +- Replication, Block within Replication are treated as random both BLUP and BLUE calculations. +- Variable 1, Genotype : Variable 1 are treated as random both BLUP and BLUE calculations. +- Variable 2, Genotype : Variable 2, Variable 1 : Variable 2, Genotype : Variable 1 : Variable 2 are treated as random effects for both BLUP and BLUE calculations. + + +**Citation** +Douglas Bates, Martin Maechler, Ben Bolker, Steve Walker (2015). Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software, 67(1), 1-48. + +**Author(s)** + </help> +</tool> |
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| diff -r 000000000000 -r 45d215f2be74 blupcal_wrapper.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/blupcal_wrapper.R Sat Dec 29 18:44:05 2018 -0500 |
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| @@ -0,0 +1,186 @@ +#!/usr/bin/Rscript + +options(show.error.messages = F, + error = function() { + cat(geterrmessage(), file = stderr()) + q("no", 1, F) } ) +loc <- Sys.setlocale("LC_MESSAGES", "en_US.UTF-8") +library("getopt") + +options(stringAsfactors = FALSE, useFancyQuotes = FALSE) +options(warn = -1) +args <- commandArgs(trailingOnly = TRUE) +option_specification <- matrix(c( + "tool_directory", "a", 1, "character" +,"tabular_file", "b", 1, "character" +,"replication_vector_column_index", "c", 1, "integer" +,"genotype_vector_column_index", "d", 1, "integer" +,"y_vector_column_index", "e", 1, "integer" +,"design", "f", 1, "character" +,"block_vector_column_index", "g", 1, "integer" +,"summarize_by", "h", 1, "character" +,"summarize_by_vector_column_index", "i", 1, "integer" +,"group_variable_1", "j", 1, "character" +,"group_variable_1_vector_column_index", "k", 1, "integer" +,"group_variable_2", "l", 1, "character" +,"group_variable_2_vector_column_index", "m", 1, "integer" +,"output_file_path", "n", 1, "character" +,"png_plots_file_path", "o", 1, "character" +,"png_histograms_file_path", "p", 1, "character" +,"pdf_plots_file_path", "q", 1, "character" +,"pdf_histograms_file_path", "r", 1, "character" +), byrow = TRUE, ncol = 4) +options <- getopt(option_specification) + +cat("\nTool Directory: ", options$tool_directory) +cat("\nTabular File: ", options$tabular_file) +cat("\nReplication Vector Column Index: ", options$replication_vector_column_index) +cat("\nGenotype Vector Column Index: ", options$genotype_vector_column_index) +cat("\nY Vector Column Index: ", options$y_vector_column_index) +cat("\nDesign: ", options$design) +cat("\nBlock Vector Column Index: ", options$block_vector_column_index) +cat("\nSummarize By: ", options$summarize_by) +cat("\nSummarize By Vector Column Index: ", options$summarize_by_vector_column_index) +cat("\nGroup Variable #1: ", options$group_variable_1) +cat("\nGroup Variable #1 Vector Column Index: ", options$group_variable_1_vector_column_index) +cat("\nGroup Variable #2: ", options$group_variable_2) +cat("\nGroup Variable #2 Vector Column Index: ", options$group_variable_2_vector_column_index) +cat("\nOutput file path: ", options$output_file_path) +cat("\nPNG plots file path: ", options$png_plots_file_path) +cat("\nPNG histograms file path: ", options$png_histograms_file_path) +cat("\nPDF plots file path: ", options$pdf_plots_file_path) +cat("\nPDF histograms file path: ", options$pdf_histograms_file_path) +cat("\n\n") + +tabular_data <- read.table(file = options$tabular_file, + header = TRUE, + sep = "\t", + stringsAsFactors = FALSE, + strip.white = TRUE, + na.strings = ".") +#tabular_data + +column_names <- colnames(tabular_data) +cat("Column names:\n") +column_names +cat("\n\n") + +# Replication +replication_vector_header <- column_names[options$replication_vector_column_index] +cat("\nreplication vector header: ", replication_vector_header) + +# Genotype +genotype_vector_header <- column_names[options$genotype_vector_column_index] +cat("\ngenotype vector header: ", genotype_vector_header) + +# Y +y_vector_header <- column_names[options$y_vector_column_index] +cat("\ny vector header: ", y_vector_header) + +# Design +design <- options$design +cat("\ndesign: ", design) + +# Block +if (design == "rcbd") { + block_vector_header <- NULL +} else { + block_vector_header <- column_names[options$block_vector_column_index] +} +cat("\nblock vector header: ", block_vector_header) + +# Summarize By +if (options$summarize_by == "false") { + summarize_by_vector_header <- NULL +} else { + summarize_by_vector_header <- column_names[options$summarize_by_vector_column_index] +} +cat("\nsummarize by vector header: ", summarize_by_vector_header) + +# Group Variable #1 +if (options$group_variable_1 == "false") { + group_variable_1_vector_header <- NULL +} else { + group_variable_1_vector_header <- column_names[options$group_variable_1_vector_column_index] +} +cat("\ngroup variable 1 vector header: ", group_variable_1_vector_header) + +# Group Variable #2 +if (options$group_variable_2 == "false") { + group_variable_2_vector_header <- NULL +} else { + group_variable_2_vector_header <- column_names[options$group_variable_2_vector_column_index] +} +cat("\ngroup variable 2 vector header: ", group_variable_2_vector_header) + +cat("\n\n") + +source(paste(options$tool_directory, "/blupcal.R", sep = "")) + +fit <- blupcal(data = tabular_data, + Replication = replication_vector_header, + Genotype = genotype_vector_header, + y = y_vector_header, + design = design, + Block = block_vector_header, + summarizeby = summarize_by_vector_header, + groupvar1 = group_variable_1_vector_header, + groupvar2 = group_variable_2_vector_header) + +cat("\n\n") + +output_file_path <- options$output_file_path + +cat("\nfit: ") +cat("\n") +fit +cat("\n") + +#fit$gid <- as.numeric(levels(fit$gid))[fit$gid] +if (options$summarize_by == "false") { + fit_view <- fit +} else { + fit_view <- fit[c(1, 7, 2, 3, 4, 5, 6)] +} + +formatted_table <- lapply(fit_view, function(.col) { + if (is.numeric(.col)) { + return(as.numeric(sprintf("%.3f", .col))) + } else { + return(.col) + } + }) + +write.table(formatted_table, + file = output_file_path, + sep = "\t", + row.names = FALSE, + quote = FALSE) + +png_plots_file_path <-options$png_plots_file_path +if (!(is.null(png_plots_file_path))) { + png(png_plots_file_path) + plot(fit) + dev.off() +} + +png_histograms_file_path <-options$png_histograms_file_path +if (!(is.null(png_histograms_file_path))) { + png(png_histograms_file_path) + plot2.blupcal(fit) + dev.off() +} + +pdf_plots_file_path <-options$pdf_plots_file_path +if (!(is.null(pdf_plots_file_path))) { + pdf(pdf_plots_file_path) + plot(fit) + dev.off() +} + +pdf_histograms_file_path <-options$pdf_histograms_file_path +if (!(is.null(pdf_histograms_file_path))) { + pdf(pdf_histograms_file_path) + plot2.blupcal(fit) + dev.off() +} |
| b |
| diff -r 000000000000 -r 45d215f2be74 test-data/blup.output --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/blup.output Sat Dec 29 18:44:05 2018 -0500 |
| b |
| b'@@ -0,0 +1,381 @@\n+Sample.Name.in.GBS.Data\tSeason\tGY_blue\tGY_blup\tGY_PEV\tGY_pevReliability\tGY_means\tAD_blue\tAD_blup\tAD_PEV\tAD_pevReliability\tAD_means\n+MARS-WEMA_270039\tMain\t6.715\t6.936\t0.072\t0.298\t6.715\t63\t62.664\t0.216\t0.465\t63\n+MARS-WEMA_270040\tMain\t5.959\t6.709\t0.072\t0.298\t5.959\t62.625\t62.489\t0.216\t0.465\t62.625\n+MARS-WEMA_270041\tMain\t6.925\t6.999\t0.072\t0.298\t6.925\t62.75\t62.547\t0.216\t0.465\t62.75\n+MARS-WEMA_270042\tMain\t7.576\t7.194\t0.072\t0.298\t7.576\t63.5\t62.898\t0.216\t0.465\t63.5\n+MARS-WEMA_270043\tMain\t8.101\t7.352\t0.072\t0.298\t8.101\t61.25\t61.847\t0.216\t0.465\t61.25\n+MARS-WEMA_270044\tMain\t7.335\t7.122\t0.072\t0.298\t7.335\t61.25\t61.847\t0.216\t0.465\t61.25\n+MARS-WEMA_270045\tMain\t8.015\t7.326\t0.072\t0.298\t8.015\t62.875\t62.606\t0.216\t0.465\t62.875\n+MARS-WEMA_270046\tMain\t7.234\t7.091\t0.072\t0.298\t7.234\t60.625\t61.555\t0.216\t0.465\t60.625\n+MARS-WEMA_270047\tMain\t6.87\t6.982\t0.072\t0.298\t6.87\t62.75\t62.547\t0.216\t0.465\t62.75\n+MARS-WEMA_270048\tMain\t7.43\t7.15\t0.072\t0.298\t7.43\t63\t62.664\t0.216\t0.465\t63\n+MARS-WEMA_270049\tMain\t7.178\t7.075\t0.072\t0.298\t7.178\t62\t62.197\t0.216\t0.465\t62\n+MARS-WEMA_270050\tMain\t6.555\t6.888\t0.072\t0.298\t6.555\t62.375\t62.372\t0.216\t0.465\t62.375\n+MARS-WEMA_270051\tMain\t6.491\t6.869\t0.072\t0.298\t6.491\t62\t62.197\t0.216\t0.465\t62\n+MARS-WEMA_270052\tMain\t6.811\t6.965\t0.072\t0.298\t6.811\t62.5\t62.431\t0.216\t0.465\t62.5\n+MARS-WEMA_270053\tMain\t7.82\t7.267\t0.072\t0.298\t7.82\t62.625\t62.489\t0.216\t0.465\t62.625\n+MARS-WEMA_270054\tMain\t6.384\t6.837\t0.072\t0.298\t6.384\t63\t62.664\t0.216\t0.465\t63\n+MARS-WEMA_270055\tMain\t6.818\t6.967\t0.072\t0.298\t6.817\t62.875\t62.606\t0.216\t0.465\t62.875\n+MARS-WEMA_270056\tMain\t7.374\t7.133\t0.072\t0.298\t7.374\t62.625\t62.489\t0.216\t0.465\t62.625\n+MARS-WEMA_270057\tMain\t7.186\t7.077\t0.072\t0.298\t7.186\t62.25\t62.314\t0.216\t0.465\t62.25\n+MARS-WEMA_270058\tMain\t7.781\t7.256\t0.072\t0.298\t7.781\t63.375\t62.839\t0.216\t0.465\t63.375\n+MARS-WEMA_270059\tMain\t7.153\t7.067\t0.072\t0.298\t7.152\t61.875\t62.139\t0.216\t0.465\t61.875\n+MARS-WEMA_270060\tMain\t7.476\t7.164\t0.072\t0.298\t7.476\t60.125\t61.321\t0.216\t0.465\t60.125\n+MARS-WEMA_270061\tMain\t6.475\t6.864\t0.072\t0.298\t6.475\t61.625\t62.022\t0.216\t0.465\t61.625\n+MARS-WEMA_270062\tMain\t6.335\t6.822\t0.072\t0.298\t6.335\t61.875\t62.139\t0.216\t0.465\t61.875\n+MARS-WEMA_270063\tMain\t6.911\t6.995\t0.072\t0.298\t6.911\t62.625\t62.489\t0.216\t0.465\t62.625\n+MARS-WEMA_270064\tMain\t6.066\t6.741\t0.072\t0.298\t6.066\t63\t62.664\t0.216\t0.465\t63\n+MARS-WEMA_270065\tMain\t7.945\t7.305\t0.072\t0.298\t7.945\t63.25\t62.781\t0.216\t0.465\t63.25\n+MARS-WEMA_270066\tMain\t7.45\t7.156\t0.072\t0.298\t7.45\t62.5\t62.431\t0.216\t0.465\t62.5\n+MARS-WEMA_270067\tMain\t7.884\t7.286\t0.072\t0.298\t7.884\t61.5\t61.963\t0.216\t0.465\t61.5\n+MARS-WEMA_270068\tMain\t7.71\t7.234\t0.072\t0.298\t7.71\t62.875\t62.606\t0.216\t0.465\t62.875\n+MARS-WEMA_270069\tMain\t7.196\t7.08\t0.072\t0.298\t7.196\t62.375\t62.372\t0.216\t0.465\t62.375\n+MARS-WEMA_270070\tMain\t7.03\t7.03\t0.072\t0.298\t7.03\t62.25\t62.314\t0.216\t0.465\t62.25\n+MARS-WEMA_270071\tMain\t6.438\t6.853\t0.072\t0.298\t6.438\t62.125\t62.255\t0.216\t0.465\t62.125\n+MARS-WEMA_270072\tMain\t6.26\t6.799\t0.072\t0.298\t6.26\t61.625\t62.022\t0.216\t0.465\t61.625\n+MARS-WEMA_270073\tMain\t7.239\t7.093\t0.072\t0.298\t7.239\t62.625\t62.489\t0.216\t0.465\t62.625\n+MARS-WEMA_270074\tMain\t7.158\t7.069\t0.072\t0.298\t7.157\t62.5\t62.431\t0.216\t0.465\t62.5\n+MARS-WEMA_270075\tMain\t7.761\t7.25\t0.072\t0.298\t7.761\t62.375\t62.372\t0.216\t0.465\t62.375\n+MARS-WEMA_270076\tMain\t5.774\t6.654\t0.072\t0.298\t5.774\t63.625\t62.956\t0.216\t0.465\t63.625\n+MARS-WEMA_270077\tMain\t7.505\t7.173\t0.072\t0.298\t7.505\t61.625\t62.022\t0.216\t0.465\t61.625\n+MARS-WEMA_270078\tMain\t6.258\t6.799\t0.072\t0.298\t6.258\t62.375\t62.372\t0.216\t0.465\t62.375\n+MARS-WEMA_270079\tMain\t7.466\t7.161\t0.072\t0.298\t7.466\t62.125\t62.255\t0.216\t0.465\t62.125\n+MARS-WEMA_270080\tMain\t7.578\t7.194\t0.072\t0.298\t7.578\t61.375\t61.905\t0.216\t0.465\t61.375\n+MARS-WEMA_270081\tMain\t7.333\t7.121\t0.072\t0.298\t7.332\t62.875\t62.606\t0.216\t0.465\t62.875\n+MARS-WEMA_270082\tMain\t7.784\t7.256\t0.072\t0.298\t7.784\t62.375\t62.372\t0.216\t0.465\t62.375\n+MARS-WEMA_270083\tMain\t7.028\t7.03\t0.072\t0.298\t7.027\t63.25\t62.781\t0.216\t0.465\t63.25\n+MARS-WEMA_270084\tMain\t6.131\t6.761\t0.072\t0.298\t6.131\t62.75\t62.547\t0.216\t0.465\t62.7'..b'333\t84.064\t0.353\t0.327\t83.333\n+MARS-WEMA_292957\tOff\t2.635\t2.689\t0.004\t0.045\t2.635\t83.667\t84.174\t0.353\t0.327\t83.667\n+MARS-WEMA_292958\tOff\t2.713\t2.693\t0.004\t0.045\t2.713\t83.667\t84.174\t0.353\t0.327\t83.667\n+MARS-WEMA_292959\tOff\t2.903\t2.701\t0.004\t0.045\t2.903\t83.833\t84.229\t0.353\t0.327\t83.833\n+MARS-WEMA_292960\tOff\t2.292\t2.674\t0.004\t0.045\t2.292\t84.833\t84.558\t0.353\t0.327\t84.833\n+MARS-WEMA_292961\tOff\t3.343\t2.721\t0.004\t0.045\t3.343\t85.333\t84.723\t0.353\t0.327\t85.333\n+MARS-WEMA_292962\tOff\t2.76\t2.695\t0.004\t0.045\t2.76\t85.167\t84.668\t0.353\t0.327\t85.167\n+MARS-WEMA_292963\tOff\t2.703\t2.692\t0.004\t0.045\t2.703\t82.5\t83.79\t0.353\t0.327\t82.5\n+MARS-WEMA_292964\tOff\t2.637\t2.689\t0.004\t0.045\t2.637\t84.667\t84.503\t0.353\t0.327\t84.667\n+MARS-WEMA_292965\tOff\t3.087\t2.709\t0.004\t0.045\t3.087\t84.167\t84.338\t0.353\t0.327\t84.167\n+MARS-WEMA_292966\tOff\t2.59\t2.687\t0.004\t0.045\t2.59\t84.333\t84.393\t0.353\t0.327\t84.333\n+MARS-WEMA_292967\tOff\t2.297\t2.674\t0.004\t0.045\t2.297\t84.833\t84.558\t0.353\t0.327\t84.833\n+MARS-WEMA_292968\tOff\t3.832\t2.743\t0.004\t0.045\t3.832\t83\t83.954\t0.353\t0.327\t83\n+MARS-WEMA_292969\tOff\t2.933\t2.703\t0.004\t0.045\t2.933\t81.5\t83.461\t0.353\t0.327\t81.5\n+MARS-WEMA_292970\tOff\t2.627\t2.689\t0.004\t0.045\t2.627\t83.667\t84.174\t0.353\t0.327\t83.667\n+MARS-WEMA_292971\tOff\t3.008\t2.706\t0.004\t0.045\t3.008\t85.5\t84.777\t0.353\t0.327\t85.5\n+MARS-WEMA_292972\tOff\t2.693\t2.692\t0.004\t0.045\t2.693\t84.667\t84.503\t0.353\t0.327\t84.667\n+MARS-WEMA_292973\tOff\t3.002\t2.706\t0.004\t0.045\t3.002\t85.333\t84.723\t0.353\t0.327\t85.333\n+MARS-WEMA_292974\tOff\t2.732\t2.693\t0.004\t0.045\t2.732\t83.167\t84.009\t0.353\t0.327\t83.167\n+MARS-WEMA_292975\tOff\t2.675\t2.691\t0.004\t0.045\t2.675\t86\t84.942\t0.353\t0.327\t86\n+MARS-WEMA_292976\tOff\t3.032\t2.707\t0.004\t0.045\t3.032\t83.667\t84.174\t0.353\t0.327\t83.667\n+MARS-WEMA_292977\tOff\t2.32\t2.675\t0.004\t0.045\t2.32\t85.833\t84.887\t0.353\t0.327\t85.833\n+MARS-WEMA_292978\tOff\t2.537\t2.685\t0.004\t0.045\t2.537\t83.5\t84.119\t0.353\t0.327\t83.5\n+MARS-WEMA_292979\tOff\t2.645\t2.69\t0.004\t0.045\t2.645\t83.5\t84.119\t0.353\t0.327\t83.5\n+MARS-WEMA_292980\tOff\t2.933\t2.703\t0.004\t0.045\t2.933\t84\t84.284\t0.353\t0.327\t84\n+MARS-WEMA_292981\tOff\t2.362\t2.677\t0.004\t0.045\t2.362\t85.333\t84.723\t0.353\t0.327\t85.333\n+MARS-WEMA_292982\tOff\t2.383\t2.678\t0.004\t0.045\t2.383\t87.333\t85.381\t0.353\t0.327\t87.333\n+MARS-WEMA_292983\tOff\t3.1\t2.71\t0.004\t0.045\t3.1\t85.667\t84.832\t0.353\t0.327\t85.667\n+MARS-WEMA_292984\tOff\t2.853\t2.699\t0.004\t0.045\t2.853\t84.167\t84.338\t0.353\t0.327\t84.167\n+MARS-WEMA_292985\tOff\t3.177\t2.714\t0.004\t0.045\t3.177\t84.333\t84.393\t0.353\t0.327\t84.333\n+MARS-WEMA_292986\tOff\t2.58\t2.687\t0.004\t0.045\t2.58\t85.167\t84.668\t0.353\t0.327\t85.167\n+MARS-WEMA_292987\tOff\t3.503\t2.728\t0.004\t0.045\t3.503\t83.5\t84.119\t0.353\t0.327\t83.5\n+MARS-WEMA_292988\tOff\t2.193\t2.669\t0.004\t0.045\t2.193\t86.5\t85.107\t0.353\t0.327\t86.5\n+MARS-WEMA_292989\tOff\t2.91\t2.702\t0.004\t0.045\t2.91\t86.167\t84.997\t0.353\t0.327\t86.167\n+MARS-WEMA_292990\tOff\t2.973\t2.704\t0.004\t0.045\t2.973\t82.833\t83.9\t0.353\t0.327\t82.833\n+MARS-WEMA_292991\tOff\t2.485\t2.682\t0.004\t0.045\t2.485\t84.333\t84.393\t0.353\t0.327\t84.333\n+MARS-WEMA_292992\tOff\t2.908\t2.701\t0.004\t0.045\t2.908\t81.833\t83.57\t0.353\t0.327\t81.833\n+MARS-WEMA_292993\tOff\t2.497\t2.683\t0.004\t0.045\t2.497\t88.667\t85.82\t0.353\t0.327\t88.667\n+MARS-WEMA_292994\tOff\t2.643\t2.689\t0.004\t0.045\t2.643\t83.333\t84.064\t0.353\t0.327\t83.333\n+MARS-WEMA_292995\tOff\t2.698\t2.692\t0.004\t0.045\t2.698\t83.5\t84.119\t0.353\t0.327\t83.5\n+MARS-WEMA_292996\tOff\t2.612\t2.688\t0.004\t0.045\t2.612\t83.667\t84.174\t0.353\t0.327\t83.667\n+MARS-WEMA_292997\tOff\t3.033\t2.707\t0.004\t0.045\t3.033\t84.5\t84.448\t0.353\t0.327\t84.5\n+MARS-WEMA_292998\tOff\t2.612\t2.688\t0.004\t0.045\t2.612\t84\t84.284\t0.353\t0.327\t84\n+MARS-WEMA_292999\tOff\t2.782\t2.696\t0.004\t0.045\t2.782\t85\t84.613\t0.353\t0.327\t85\n+MARS-WEMA_293000\tOff\t3.058\t2.708\t0.004\t0.045\t3.058\t84.833\t84.558\t0.353\t0.327\t84.833\n+MARS-WEMA_293001\tOff\t2.783\t2.696\t0.004\t0.045\t2.783\t84\t84.284\t0.353\t0.327\t84\n+MARS-WEMA_293002\tOff\t3.257\t2.717\t0.004\t0.045\t3.257\t84.333\t84.393\t0.353\t0.327\t84.333\n+MARS-WEMA_293003\tOff\t2.127\t2.666\t0.004\t0.045\t2.127\t83.5\t84.119\t0.353\t0.327\t83.5\n+MARS-WEMA_293004\tOff\t2.63\t2.689\t0.004\t0.045\t2.63\t82.667\t83.845\t0.353\t0.327\t82.667\n' |
| b |
| diff -r 000000000000 -r 45d215f2be74 test-data/pheno_input.tab.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/pheno_input.tab.txt Sat Dec 29 18:44:05 2018 -0500 |
| b |
| b'@@ -0,0 +1,2661 @@\n+NameOfExperiment\tYear\tSeason\tNameOfLocation\tCountry\tCollaborator\tManagement\tLOC\tRep\tBlock\tPlot\tEntry\tGY\tAD\tPH\tEPP\tgid\tSample Name in GBS Data\tPedigree in GBS data\n+WET10B-EVALTC-09-1\t2010\tMain\tKakamega\tKenya\tS. Ajanga\tOptimal\t1\t1\t8\t93\t1\t8\t79\t217.5\t0.94\t49307\tMARS-WEMA_270039\tCZL04003/CZL00009-1\n+WET10B-EVALTC-09-1\t2010\tMain\tKakamega\tKenya\tS. Ajanga\tOptimal\t1\t1\t12\t150\t2\t8.35\t72\t203.5\t0.82\t49308\tMARS-WEMA_270040\tCZL04003/CZL00009-2\n+WET10B-EVALTC-09-1\t2010\tMain\tKakamega\tKenya\tS. Ajanga\tOptimal\t1\t1\t11\t141\t3\t7.68\t72\t192.5\t0.89\t49309\tMARS-WEMA_270041\tCZL04003/CZL00009-3\n+WET10B-EVALTC-09-1\t2010\tMain\tKakamega\tKenya\tS. Ajanga\tOptimal\t1\t1\t5\t64\t4\t9.86\t72\t215.5\t0.79\t49310\tMARS-WEMA_270042\tCZL04003/CZL00009-4\n+WET10B-EVALTC-09-1\t2010\tMain\tKakamega\tKenya\tS. Ajanga\tOptimal\t1\t1\t5\t57\t5\t10.7\t71\t213.5\t1.11\t49311\tMARS-WEMA_270043\tCZL04003/CZL00009-5\n+WET10B-EVALTC-09-1\t2010\tMain\tKakamega\tKenya\tS. Ajanga\tOptimal\t1\t1\t5\t55\t6\t11\t72\t221.5\t0.89\t49312\tMARS-WEMA_270044\tCZL04003/CZL00009-6\n+WET10B-EVALTC-09-1\t2010\tMain\tKakamega\tKenya\tS. Ajanga\tOptimal\t1\t1\t15\t191\t7\t10.3\t71\t245\t1\t49313\tMARS-WEMA_270045\tCZL04003/CZL00009-7\n+WET10B-EVALTC-09-1\t2010\tMain\tKakamega\tKenya\tS. Ajanga\tOptimal\t1\t1\t14\t176\t8\t8.45\t71\t221\t0.95\t49314\tMARS-WEMA_270046\tCZL04003/CZL00009-8\n+WET10B-EVALTC-09-1\t2010\tMain\tKakamega\tKenya\tS. Ajanga\tOptimal\t1\t1\t2\t23\t9\t6.18\t72\t235\t1.13\t49315\tMARS-WEMA_270047\tCZL04003/CZL00009-9\n+WET10B-EVALTC-09-1\t2010\tMain\tKakamega\tKenya\tS. Ajanga\tOptimal\t1\t1\t3\t38\t10\t8.02\t71\t265\t0.94\t49316\tMARS-WEMA_270048\tCZL04003/CZL00009-10\n+WET10B-EVALTC-09-1\t2010\tMain\tKakamega\tKenya\tS. Ajanga\tOptimal\t1\t1\t8\t103\t11\t10.57\t72\t220.5\t0.89\t49317\tMARS-WEMA_270049\tCZL04003/CZL00009-11\n+WET10B-EVALTC-09-1\t2010\tMain\tKakamega\tKenya\tS. Ajanga\tOptimal\t1\t1\t14\t175\t12\t9.53\t72\t228\t0.95\t49318\tMARS-WEMA_270050\tCZL04003/CZL00009-12\n+WET10B-EVALTC-09-1\t2010\tMain\tKakamega\tKenya\tS. Ajanga\tOptimal\t1\t1\t2\t22\t13\t2.04\t72\t205\t1\t49319\tMARS-WEMA_270051\tCZL04003/CZL00009-13\n+WET10B-EVALTC-09-1\t2010\tMain\tKakamega\tKenya\tS. Ajanga\tOptimal\t1\t1\t5\t65\t14\t8.06\t72\t223.5\t0.8\t49320\tMARS-WEMA_270052\tCZL04003/CZL00009-14\n+WET10B-EVALTC-09-1\t2010\tMain\tKakamega\tKenya\tS. Ajanga\tOptimal\t1\t1\t14\t177\t15\t10.05\t71\t219.5\t1.19\t49321\tMARS-WEMA_270053\tCZL04003/CZL00009-15\n+WET10B-EVALTC-09-1\t2010\tMain\tKakamega\tKenya\tS. Ajanga\tOptimal\t1\t1\t12\t149\t16\t4.4\t74\t206.1\t1.14\t49322\tMARS-WEMA_270054\tCZL04003/CZL00009-16\n+WET10B-EVALTC-09-1\t2010\tMain\tKakamega\tKenya\tS. Ajanga\tOptimal\t1\t1\t14\t182\t17\t8.38\t72\t235\t0.68\t49323\tMARS-WEMA_270055\tCZL04003/CZL00009-17\n+WET10B-EVALTC-09-1\t2010\tMain\tKakamega\tKenya\tS. Ajanga\tOptimal\t1\t1\t2\t18\t18\t10.7\t71\t241.6\t1.07\t49324\tMARS-WEMA_270056\tCZL04003/CZL00009-18\n+WET10B-EVALTC-09-1\t2010\tMain\tKakamega\tKenya\tS. Ajanga\tOptimal\t1\t1\t6\t69\t19\t9.5\t72\t211.5\t0.94\t49326\tMARS-WEMA_270057\tCZL04003/CZL00009-20\n+WET10B-EVALTC-09-1\t2010\tMain\tKakamega\tKenya\tS. Ajanga\tOptimal\t1\t1\t8\t102\t20\t11.2\t76\t242.5\t0.95\t49327\tMARS-WEMA_270058\tCZL04003/CZL00009-21\n+WET10B-EVALTC-09-1\t2010\tMain\tKakamega\tKenya\tS. Ajanga\tOptimal\t1\t1\t14\t172\t21\t10.21\t72\t207.5\t1.23\t49328\tMARS-WEMA_270059\tCZL04003/CZL00009-22\n+WET10B-EVALTC-09-1\t2010\tMain\tKakamega\tKenya\tS. Ajanga\tOptimal\t1\t1\t15\t186\t22\t10.56\t71\t207\t1.05\t49329\tMARS-WEMA_270060\tCZL04003/CZL00009-23\n+WET10B-EVALTC-09-1\t2010\tMain\tKakamega\tKenya\tS. Ajanga\tOptimal\t1\t1\t7\t89\t23\t7.27\t72\t199\t1\t49330\tMARS-WEMA_270061\tCZL04003/CZL00009-24\n+WET10B-EVALTC-09-1\t2010\tMain\tKakamega\tKenya\tS. Ajanga\tOptimal\t1\t1\t13\t168\t24\t8.55\t73\t229.5\t1\t49331\tMARS-WEMA_270062\tCZL04003/CZL00009-25\n+WET10B-EVALTC-09-1\t2010\tMain\tKakamega\tKenya\tS. Ajanga\tOptimal\t1\t1\t10\t119\t25\t11.56\t72\t227.5\t1.06\t49332\tMARS-WEMA_270063\tCZL04003/CZL00009-26\n+WET10B-EVALTC-09-1\t2010\tMain\tKakamega\tKenya\tS. Ajanga\tOptimal\t1\t1\t10\t125\t26\t7.68\t71\t228.5\t0.79\t49333\tMARS-WEMA_270064\tCZL04003/CZL00009-27\n+WET10B-EVALTC-09-1\t2010\tMain\tKakamega\tKenya\tS. Ajanga\tOptimal\t1\t1\t14\t178\t27\t10.84\t71\t225\t1.38\t49334\tMARS-WEMA_270065\tCZL04003/CZL00009-28\n+WET10B-EVALTC-09-1\t2010\tMain\tKakamega\tKenya\tS. Ajanga\tOptimal\t1\t1\t13\t169\t28\t11.97\t72\t231.5\t1.33\t49335\tMARS-WEMA'..b'\t2\t25\t325\t163\t4.47\t87\t108.8\t0.82\t49499\tMARS-WEMA_292977\tCZL04003/CZL00009-193\n+WET11A-EVALTC-09-8\t2010\tOff\tIsinya\tKenya\tM. Gunga\tManaged Drought\t8\t2\t24\t302\t164\t5.6\t88\t114\t0.72\t49500\tMARS-WEMA_292978\tCZL04003/CZL00009-194\n+WET11A-EVALTC-09-8\t2010\tOff\tIsinya\tKenya\tM. Gunga\tManaged Drought\t8\t2\t28\t354\t165\t5.52\t89\t122.8\t0.89\t49501\tMARS-WEMA_292979\tCZL04003/CZL00009-195\n+WET11A-EVALTC-09-8\t2010\tOff\tIsinya\tKenya\tM. Gunga\tManaged Drought\t8\t2\t26\t326\t166\t5.53\t89\t124.4\t0.94\t49502\tMARS-WEMA_292980\tCZL04003/CZL00009-196\n+WET11A-EVALTC-09-8\t2010\tOff\tIsinya\tKenya\tM. Gunga\tManaged Drought\t8\t2\t23\t294\t167\t4.11\t94\t123.4\t0.67\t49504\tMARS-WEMA_292981\tCZL04003/CZL00009-198\n+WET11A-EVALTC-09-8\t2010\tOff\tIsinya\tKenya\tM. Gunga\tManaged Drought\t8\t2\t28\t364\t168\t5.21\t91\t123.4\t1\t49505\tMARS-WEMA_292982\tCZL04003/CZL00009-199\n+WET11A-EVALTC-09-8\t2010\tOff\tIsinya\tKenya\tM. Gunga\tManaged Drought\t8\t2\t28\t352\t169\t6.77\t91\t112.4\t0.94\t49507\tMARS-WEMA_292983\tCZL04003/CZL00009-201\n+WET11A-EVALTC-09-8\t2010\tOff\tIsinya\tKenya\tM. Gunga\tManaged Drought\t8\t2\t16\t205\t170\t4.4\t89\t118.8\t0.94\t49509\tMARS-WEMA_292984\tCZL04003/CZL00009-203\n+WET11A-EVALTC-09-8\t2010\tOff\tIsinya\tKenya\tM. Gunga\tManaged Drought\t8\t2\t26\t335\t171\t5.56\t89\t127.4\t0.94\t49511\tMARS-WEMA_292985\tCZL04003/CZL00009-205\n+WET11A-EVALTC-09-8\t2010\tOff\tIsinya\tKenya\tM. Gunga\tManaged Drought\t8\t2\t30\t379\t172\t4.92\t91\t124.6\t0.79\t49512\tMARS-WEMA_292986\tCZL04003/CZL00009-206\n+WET11A-EVALTC-09-8\t2010\tOff\tIsinya\tKenya\tM. Gunga\tManaged Drought\t8\t2\t24\t309\t173\t6.04\t89\t126.4\t0.94\t49514\tMARS-WEMA_292987\tCZL04003/CZL00009-208\n+WET11A-EVALTC-09-8\t2010\tOff\tIsinya\tKenya\tM. Gunga\tManaged Drought\t8\t2\t22\t282\t174\t4.37\t94\t112.4\t0.95\t49515\tMARS-WEMA_292988\tCZL04003/CZL00009-209\n+WET11A-EVALTC-09-8\t2010\tOff\tIsinya\tKenya\tM. Gunga\tManaged Drought\t8\t2\t24\t301\t175\t5.57\t89\t121.8\t0.79\t49516\tMARS-WEMA_292989\tCZL04003/CZL00009-210\n+WET11A-EVALTC-09-8\t2010\tOff\tIsinya\tKenya\tM. Gunga\tManaged Drought\t8\t2\t30\t384\t176\t5.61\t90\t123.8\t0.85\t49518\tMARS-WEMA_292990\tCZL04003/CZL00009-212\n+WET11A-EVALTC-09-8\t2010\tOff\tIsinya\tKenya\tM. Gunga\tManaged Drought\t8\t2\t21\t265\t177\t5.5\t91\t122.2\t0.88\t49525\tMARS-WEMA_292991\tCZL04003/CZL00009-219\n+WET11A-EVALTC-09-8\t2010\tOff\tIsinya\tKenya\tM. Gunga\tManaged Drought\t8\t2\t30\t380\t178\t4.86\t91\t120.6\t0.84\t49526\tMARS-WEMA_292992\tCZL04003/CZL00009-220\n+WET11A-EVALTC-09-8\t2010\tOff\tIsinya\tKenya\tM. Gunga\tManaged Drought\t8\t2\t22\t278\t179\t4.62\t100\t100.2\t0.71\t49527\tMARS-WEMA_292993\tCZL04003/CZL00009-221\n+WET11A-EVALTC-09-8\t2010\tOff\tIsinya\tKenya\tM. Gunga\tManaged Drought\t8\t2\t24\t310\t180\t5.89\t86\t125\t0.83\t49530\tMARS-WEMA_292994\tCZL04003/CZL00009-224\n+WET11A-EVALTC-09-8\t2010\tOff\tIsinya\tKenya\tM. Gunga\tManaged Drought\t8\t2\t28\t363\t181\t5.42\t90\t125.4\t0.78\t49535\tMARS-WEMA_292995\tCZL04003/CZL00009-230\n+WET11A-EVALTC-09-8\t2010\tOff\tIsinya\tKenya\tM. Gunga\tManaged Drought\t8\t2\t16\t199\t182\t3.59\t91\t103\t0.9\t49537\tMARS-WEMA_292996\tCZL04003/CZL00009-232\n+WET11A-EVALTC-09-8\t2010\tOff\tIsinya\tKenya\tM. Gunga\tManaged Drought\t8\t2\t17\t219\t183\t4.35\t90\t122.2\t0.84\t49538\tMARS-WEMA_292997\tCZL04003/CZL00009-233\n+WET11A-EVALTC-09-8\t2010\tOff\tIsinya\tKenya\tM. Gunga\tManaged Drought\t8\t2\t28\t353\t184\t5.4\t89\t118.6\t0.94\t49539\tMARS-WEMA_292998\tCZL04003/CZL00009-234\n+WET11A-EVALTC-09-8\t2010\tOff\tIsinya\tKenya\tM. Gunga\tManaged Drought\t8\t2\t28\t358\t185\t4.46\t96\t124.8\t0.87\t49540\tMARS-WEMA_292999\tCZL04003/CZL00009-235\n+WET11A-EVALTC-09-8\t2010\tOff\tIsinya\tKenya\tM. Gunga\tManaged Drought\t8\t2\t29\t371\t186\t5.81\t90\t121.8\t1\t49541\tMARS-WEMA_293000\tCZL04003/CZL00009-236\n+WET11A-EVALTC-09-8\t2010\tOff\tIsinya\tKenya\tM. Gunga\tManaged Drought\t8\t2\t24\t300\t187\t4.77\t88\t100.2\t0.83\t49542\tMARS-WEMA_293001\tCZL04003/CZL00009-237\n+WET11A-EVALTC-09-8\t2010\tOff\tIsinya\tKenya\tM. Gunga\tManaged Drought\t8\t2\t26\t332\t188\t5.72\t88\t125\t0.88\t49543\tMARS-WEMA_293002\tCZL04003/CZL00009-238\n+WET11A-EVALTC-09-8\t2010\tOff\tIsinya\tKenya\tM. Gunga\tManaged Drought\t8\t2\t26\t328\t189\t4.06\t89\t112.4\t0.87\t49544\tMARS-WEMA_293003\tCZL04003/CZL00009-239\n+WET11A-EVALTC-09-8\t2010\tOff\tIsinya\tKenya\tM. Gunga\tManaged Drought\t8\t2\t19\t247\t190\t5\t91\t125.4\t0.84\t49545\tMARS-WEMA_293004\tCZL04003/CZL00009-240\n' |