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read xlsx to vartable json

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Hollos Roland 2020-02-12 19:21:14 +01:00
parent 9f6eb0e7a9
commit 03d7af6252
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RBBGCMuso/R/outputMapping.R
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@ -2,20 +2,44 @@
#' #'
#' This function updates the Biome-BGCMuSo output code-variable matrix. Within Biome-BGCMuSo the state variables and fluxes are marked by integer numbers. In order to provide meaningful variable names (e.g. 3009 means Gross Primary Production in Biome-BGCMuSo v5) a conversion table is needed which is handled by this function. #' This function updates the Biome-BGCMuSo output code-variable matrix. Within Biome-BGCMuSo the state variables and fluxes are marked by integer numbers. In order to provide meaningful variable names (e.g. 3009 means Gross Primary Production in Biome-BGCMuSo v5) a conversion table is needed which is handled by this function.
#' @author Roland HOLLOS #' @author Roland HOLLOS
#' @param output_map_init The output code-variable bindings are described in output_map_init.c file that is part of the Biome-BGCMuSo source code. Using this function the user can read the output_map_init.c file and pass it to RBBGCMuso for further work. #' @param excelName Name of the excelfile which contains the parameters
#' @return The output code-variable matrix, and also the function changes the global variable #' @return The output code-variable matrix, and also the function changes the global variable
#' @export #' @export
#' @usage updateMusoMapping(output_map_init="output_map_init.c")
updateMusoMapping<-function(output_map_init="output_map_init.c"){ updateMusoMapping<-function(excelName, dest="./", version=getOption("RMuso_version")){
outputRaw<-grep("\\[",readLines(output_map_init,-1),value=TRUE) expandRangeRows <- function (ind) {
rangeString <- gsub(".*?(\\d*\\-\\d*).*","\\1",toExpand[2])
interval <- as.numeric(strsplit(rangeString,split="-")[[1]])
result <- do.call(rbind,lapply(interval[1]:interval[2],function(x){
toExpand[2] <- x
toExpand[1] <- gsub("\\[.*?\\]",sprintf("_%s",(x-interval[1])),toExpand[1])
toExpand
}))
result <- as.data.frame(result,stringsAsFactors = FALSE)
result[,2] <- as.numeric(result[,2])
colnames(result) <- c("names","codes","units","descriptions")
result[,c(2,1,3,4)]
}
excelDF <- read.xlsx(excelName)
excelDF <- excelDF[!is.na(excelDF[,2]),]
excelDF[,1] <- trimws(excelDF[,1])
excelDF[,2] <- trimws(excelDF[,2])
excelDF[,3] <- trimws(excelDF[,3])
excelDF[,4] <- trimws(excelDF[,4])
rangeRows <- grep("-",excelDF[,2])
nonRangeMatrix <- excelDF[setdiff(1:nrow(excelDF),rangeRows),]
nonRangeMatrix[,2] <- as.numeric(nonRangeMatrix[,2])
nonRangeMatrix[,1] <- trimws(nonRangeMatrix[,1])
names(nonRangeMatrix) <- c("names","codes","units","descriptions")
outMatrix <- rbind.data.frame(do.call(rbind.data.frame,lapply(rangeRows,expandRangeRows)),
nonRangeMatrix[,c(2,1,3,4)]
)
outMatrix <- outMatrix[order(outMatrix[,1]),]
rownames(outMatrix)<- NULL
write_json(outMatrix, file.path(dest,sprintf("varTable%s.json",version)), pretty=TRUE)
codes <- as.vector(lapply(outputRaw, function (x) as.numeric(unlist(strsplit(unlist(strsplit(x,"\\["))[2],"\\]"))[1])))
names <- unlist(lapply(outputRaw, function (x) unlist(strsplit(unlist(strsplit(x,"\\&"))[2],";"))[1]))
mMapping <-cbind(codes,names)
save(mMapping, file="mMap.RData")
return(mMapping)
} }
#' musoMapping #' musoMapping