#' Makes a logical subset of a CFSv2 GeoGrid
#'
#' Makes a logical subset of a CFSv2 GeoGrid using the parameters specified by the user,
#' applying the java methods makeSubset and readDataSlice. Subroutine of \code{loadSeasonalForecast.CFS}
#'
#' @param grid An input java GeoGrid
#' @param latLon A list of geolocation parameters, as returned by getLatLonDomainForecast
#' @param runTimePars A list of run time definition parameters, as returned by \code{getRunTimeDomain.ECOMS}
#' @param foreTimePars A list of forecast time definition parameters, as returned by \code{getForecastTimeDomain.CFS}
#' @return A list with the n-dimensional array of data and the modified foreTimePars with adjusted dates depending on the
#' temporal aggregation performed.
#' @details Dimensions of length one are dropped and the \dQuote{dimnames} attribute is consequently modified.
#' In the current version the Z dimension is ignored (and dropped), as it is not planned to include multi-level variables
#' in the ECOMS-UDG by the moment. Because of the lagged-runtime configuration of CFSv2 for member definition,
#' the dimension \sQuote{ensemble} doen not exist. In turn, this is created and included as a dimension in the returned array
#' from the run time parameters passed by runTimePars and foreTimePars.
#'
#' This function performs the temporal aggregations.
#'
#' @references \url{http://www.unidata.ucar.edu/software/thredds/v4.3/netcdf-java/v4.3/javadocAll/ucar/nc2/dt/grid/GeoGrid.html}
#' @author J Bedia and A. Cofi\~no
#'
makeSubset.CFS <- function(grid, latLon, runTimePars, foreTimePars) {
message("[", Sys.time(), "] Retrieving data subset ..." )
gcs <- grid$getCoordinateSystem()
dimNames <- rev(names(scanVarDimensions(grid))) # reversed!
z <- .jnew("ucar/ma2/Range", 0L, 0L)
ens <- .jnull()
aux.list <- rep(list(bquote()), length(runTimePars$runTimeRanges))
for (i in 1:length(runTimePars$runTimeRanges)) {
aux.list1 <- rep(list(bquote()), length(runTimePars$runTimeRanges[[i]]))
for (j in 1:length(runTimePars$runTimeRanges[[i]])) {
rt <- runTimePars$runTimeRanges[[i]][[j]]
ft <- foreTimePars$ForeTimeRangesList[[i]][[j]]
aux.list2 <- rep(list(bquote()), length(latLon$llRanges))
for (k in 1:length(latLon$llRanges)) {
subSet <- grid$makeSubset(rt, ens, ft, z, latLon$llRanges[[k]]$get(0L), latLon$llRanges[[k]]$get(1L))
shapeArray <- rev(subSet$getShape())
dimNamesRef <- dimNames
if (latLon$pointXYindex[1] >= 0) {
rm.dim <- grep("^lon", dimNamesRef)
shapeArray <- shapeArray[-rm.dim]
dimNamesRef <- dimNamesRef[-rm.dim]
}
if (latLon$pointXYindex[2] >= 0) {
rm.dim <- grep("^lat", dimNamesRef)
shapeArray <- shapeArray[-rm.dim]
dimNamesRef <- dimNamesRef[-rm.dim]
}
aux.list2[[k]] <- array(subSet$readDataSlice(-1L, -1L, -1L, -1L, latLon$pointXYindex[2], latLon$pointXYindex[1])$copyTo1DJavaArray(), dim = shapeArray)
}
aux.list1[[j]] <- do.call("abind", c(aux.list2, along = 1))
aux.list2 <- NULL
# Daily aggregator
if (foreTimePars$aggr.d != "none") {
aux.string <- paste((foreTimePars$forecastDates[[i]][[j]])$mon, (foreTimePars$forecastDates[[i]][[j]])$mday, sep = "-")
aux.factor <- factor(aux.string, levels = unique(aux.string), ordered = TRUE)
mar <- grep("^time", dimNamesRef, invert = TRUE)
aux.list1[[j]] <- apply(aux.list1[[j]], mar, function(x) {
tapply(x, INDEX = aux.factor, FUN = foreTimePars$aggr.d, na.rm = TRUE)
})
dimNamesRef <- c("time", dimNamesRef[mar])
# Convert dates to daily:
nhours <- length(aux.factor) / nlevels(aux.factor)
foreTimePars$forecastDates[[i]][[j]] <- foreTimePars$forecastDates[[i]][[j]][seq(1, by = nhours, length.out = nlevels(aux.factor))]
}
# Monthly aggregator
if (foreTimePars$aggr.m != "none") {
mes <- (foreTimePars$forecastDates[[i]][[j]])$mon
mes <- factor(mes, levels = unique(mes), ordered = TRUE)
day <- (foreTimePars$forecastDates[[i]][[j]])$mday
mar <- grep("^time", dimNamesRef, invert = TRUE)
aux.list1[[j]] <- apply(aux.list1[[j]], MARGIN = mar, FUN = function(x) {
tapply(x, INDEX = mes, FUN = foreTimePars$aggr.m)
})
dimNamesRef <- if (length(unique(mes)) > 1) {
c("time", dimNamesRef[mar])
} else {
dimNamesRef[mar]
}
foreTimePars$forecastDates[[i]][[j]] <- foreTimePars$forecastDates[[i]][[j]][which(day == 1)]
}
}
if (foreTimePars$aggr.m != "none") {
if (length(unique(mes)) > 1) {
aux.list[[i]] <- do.call("abind", c(aux.list1, along = grep("^time", dimNamesRef)))
} else {
aux.list[[i]] <- do.call("abind", c(aux.list1, along = -1L))
dimNamesRef <- c("time", dimNamesRef)
}
} else {
aux.list[[i]] <- do.call("abind", c(aux.list1, along = grep("^time", dimNamesRef)))
}
aux.list1 <- NULL
}
mdArray <- do.call("abind", aux.list)
aux.list <- NULL
if (any(dim(mdArray) == 1)) {
dimNames <- dimNamesRef[-which(dim(mdArray) == 1)]
mdArray <- drop(mdArray)
} else {
dimNames <- dimNamesRef
}
if ("runtime" %in% dimNames) {
dimNames <- gsub("runtime", "member", dimNames)
}
dimNames <- gsub("^time.*", "time", dimNames)
mdArray <- unname(mdArray)
attr(mdArray, "dimensions") <- dimNames
# Date adjustment
if (!is.null(foreTimePars$forecastDates[[1]][[1]])) { ## STATIC are null
foreTimePars$forecastDates <- adjustDates.forecast(foreTimePars)
}
return(list("mdArray" = mdArray, "foreTimePars" = foreTimePars))
}
# End
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