#' @title readWISE
#' @description This function reads the preprocessed data from WISE_30sec
#'
#' @return magpie object in cellular resolution
#' @author Kristine Karstens
#'
#'
#' @examples
#' \dontrun{ readSource("WISE") }
#'
#' @importFrom raster rasterFromXYZ raster projectRaster crs<- res
#' @importFrom magclass as.magpie mbind
readWISE <- function() {
lpj_cells_carbon <- NULL
load("lpj_cells_carbon.rda")
out <- rasterFromXYZ(lpj_cells_carbon)$D30
lon <- seq(-179.75,179.75,by=0.5)
lat <- rev(seq(-89.75,89.75,by=0.5))
#Load celliso names for 1:59199 magpie cells
mapping <- toolGetMapping(type = "cell", name = "CountryToCellMapping.csv", where = "mappingfolder")
cellNames <- mapping$celliso
#Change longitude and latitude
r50 <- raster(res=0.5)
crs(out) <- "+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0"
toMag <- projectRaster(out,r50,over=TRUE)
toMag <- t(as.matrix(toMag))
#Create array for 59199 isocells, 1 year and 1 data dimension
mag <- array(NA,dim=c(59199,1,1),dimnames=list(cellNames,"y2000","soilc"))
#Fill array with data from raster object (note magpie_coord are loaded by default)
for (j in 1:59199) {
mag[j,,] <- toMag[which(magpie_coord[j, 1]==lon), which(magpie_coord[j,2]==lat)]
}
#Convert array to magpie object and rename set
x <- clean_magpie(as.magpie(mag))
getSets(x) <- c("cell","t","data")
return(x)
}
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