#' Load user-defined subsets from System4 datasets
#'
#' Load user-defined subsets from System4 datasets considering the ensemble dimensions and
#' other particular characteristics of System4 datasets. This is a subroutine of
#' \code{\link{loadSeasonalForecast}}.
#'
#' @param dataset character string indicating the dataset requested.
#' @param var character string indicating the variable to download.
#' @param grid A java GeoGrid.
#' @param dic A single-row data.frame, as returned by \code{dictionaryLookup.ECOMS}.
#' @param members Numeric vector indicating the members to be retrieved.
#' @param latLon A list of geolocation parameters as returned by \code{getLatLonDomainForecast}
#' @param runTimePars A list of parameters defining de initializations to be taken and other.
#' auxiliary parameters, as returned by \code{getRunTimeDomain.ECOMS}.
#' @param time Verification time. See \code{\link{loadSeasonalForecast}}
#' @param derInterface A list of components indicating the interface for derived variables,
#' when relevant. See details.
#' @return A list of components, as returned by \code{\link{loadSeasonalForecast}}.
#' @details In the case of derived variables, these are computed on-the-fly applying the
#' appropriate method. The idea is computing the derived variable loading the minimum
#' object sizes at a time in order to optimize the available memory. Thus, different
#' variants of the \code{\link{makeSubset.S4}} method (referred to as \sQuote{interfaces} in the dictionary
#' and henceforth within the code) have been implemented, and called alternatively depending on the
#' variable to be derived. They operate in a time-slice basis, removing from the memory the input variables
#' at each time step within the for loop once the derived variable is calculated,
#' freeing as much space as possible. The different functions for deriving variables are named with the
#' \sQuote{derive} prefix.
#' @references \url{http://meteo.unican.es/ecoms-udg/ListOfVariables}
#' @author J. Bedia
#' @importFrom loadeR C4R.vocabulary
#' @importFrom loadeR dictionaryTransformForecast
loadSeasonalForecast.S4 <- function(dataset, gds, var, grid, dic, members, latLon, runTimePars, time, level, aggr.d, aggr.m, derInterface) {
memberRangeList <- getMemberDomain.S4(grid, dataset, members)
foreTimePars <- getForecastTimeDomain.S4(grid, dataset, dic, runTimePars, time, aggr.d, aggr.m)
verticalPars <- getVerticalLevelPars.ECOMS(grid, dataset, level)
cube <- switch(derInterface$deriveInterface,
none = makeSubset.S4(grid, latLon, runTimePars, memberRangeList, foreTimePars, verticalPars),
deriveSurfacePressure = deriveSurfacePressure.S4(gds, grid, latLon, runTimePars, memberRangeList, foreTimePars),
deriveSurfaceRelativeHumidity = deriveSurfaceRelativeHumidity.S4(gds, grid, latLon, runTimePars, memberRangeList, foreTimePars),
deriveSurfaceSpecificHumidity = deriveSurfaceSpecificHumidity.S4(gds, grid, latLon, runTimePars, memberRangeList, foreTimePars),
deriveSurfaceWindSpeed = deriveSurfaceWindSpeed.S4(gds, grid, latLon, runTimePars, memberRangeList, foreTimePars))
foreTimePars <- NULL
if (!is.null(dic)) {
isStandard <- TRUE
cube$mdArray <- dictionaryTransformForecast(dic, cube$mdArray)
var <- derInterface$origVar
} else {
isStandard <- FALSE
}
if (isTRUE(latLon$revLat)) {
cube$mdArray <- revArrayLatDim(cube$mdArray)
}
# formatting initialization dates
if (!is.null(runTimePars$runDates)) { ## Otherwise static variable
runTimePars$runDates <- format(as.POSIXct(runTimePars$runDates, tz = "GMT"),
format = "%Y-%m-%d %H:%M:%S", usetz = TRUE)
}
# Static fields
if (dic$time_step == "static") {
runTimePars$runDates <- NA
names(memberRangeList) <- NA
fakedate <- as.POSIXct("2000-01-01 00:00:00", tz = "GMT")
cube$foreTimePars$forecastDates <- list("start" = fakedate, "end" = fakedate)
}
Variable <- list("varName" = var, "level" = level)
attr(Variable, "use_dictionary") <- isStandard
attr(Variable, "description") <- grid$getDescription()
if (isStandard) {
vocabulary <- C4R.vocabulary()
attr(Variable, "units") <- as.character(vocabulary[grep(paste0("^", var, "$"), vocabulary$identifier), 3])
attr(Variable, "longname") <- as.character(vocabulary[grep(paste0("^", var, "$"), vocabulary$identifier), 2])
} else {
attr(Variable, "units") <- "undefined"
attr(Variable, "longname") <- "undefined"
}
attr(Variable, "daily_agg_cellfun") <- cube$foreTimePars$aggr.d
attr(Variable, "monthly_agg_cellfun") <- cube$foreTimePars$aggr.m
attr(Variable, "verification_time") <- time
## Monthly datasets
if (grepl("m", dic$time_step)) {
attr(Variable, "monthly_agg_cellfun") <- dic$aggr_fun
}
rtList <- rep(list(runTimePars$runDates), length(memberRangeList))
names(rtList) <- names(memberRangeList)
return(list("Variable" = Variable,
"Data" = cube$mdArray,
"xyCoords" = latLon$xyCoords,
"Dates" = cube$foreTimePars$forecastDates,
"InitializationDates" = rtList,
"Members" = names(memberRangeList)))
}
# End
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