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#'Compute anomalies in cross-validation mode
#'
#'Compute the anomalies from the arrays of the experimental and observational
#'data output by subtracting the climatologies computed with a leave-one-out
#'cross validation technique and a per-pair method (Garcia-Serrano and
#'Doblas-Reyes, CD, 2012).
#'Per-pair climatology means that only the start dates covered by the
#'whole experiments/observational datasets will be used. In other words, the
#'startdates which do not all have values along 'dat_dim' dimension of both
#'the 'exp' and 'obs' are excluded when computing the climatologies.
#'
#'@param exp A named numeric array of experimental data, with at least
#' dimensions 'time_dim' and 'dat_dim'.
#'@param obs A named numeric array of observational data, same dimensions as
#' parameter 'exp' except along 'dat_dim'.
#'@param time_dim A character string indicating the name of the time dimension.
#' The default value is 'sdate'.
#'@param dat_dim A character vector indicating the name of the dataset and
#' member dimensions. When calculating the climatology, if data at one
#' startdate (i.e., 'time_dim') is not complete along 'dat_dim', this startdate
#' along 'dat_dim' will be discarded. The default value is
#' "c('dataset', 'member')".
#'@param memb_dim A character string indicating the name of the member
#' dimension. Only used when parameter 'memb' is FALSE. It must be one element
#' in 'dat_dim'. The default value is 'member'.
#'@param memb A logical value indicating whether to subtract the climatology
#' based on the individual members (TRUE) or the ensemble mean over all
#' members (FALSE) when calculating the anomalies. The default value is TRUE.
#'@param ncores An integer indicating the number of cores to use for parallel
#' computation. The default value is NULL.
#'
#'@return
#'A list of 2:
#' A numeric array with the same dimensions as 'exp'. The dimension order may
#' change.
#'}
#' A numeric array with the same dimensions as 'obs'.The dimension order may
#' change.
#'}
#'
#'@examples
#'# Load sample data as in Load() example:
#'example(Load)
#'anomalies <- Ano_CrossValid(sampleData$mod, sampleData$obs)
#'\dontrun{
#'PlotAno(anomalies$exp, anomalies$obs, startDates,
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#' toptitle = paste('anomalies'), ytitle = c('K', 'K', 'K'),
#' legends = 'ERSST', biglab = FALSE, fileout = 'tos_ano_crossvalid.eps')
#'}
#'@import multiApply
#'@importFrom ClimProjDiags Subset
#'@export
Ano_CrossValid <- function(exp, obs, time_dim = 'sdate', dat_dim = c('dataset', 'member'),
memb_dim = 'member', memb = TRUE, ncores = NULL) {
# Check inputs
## exp and obs (1)
if (is.null(exp) | is.null(obs)) {
stop("Parameter 'exp' and 'obs' cannot be NULL.")
}
if (!is.numeric(exp) | !is.numeric(obs)) {
stop("Parameter 'exp' and 'obs' must be a numeric array.")
}
if (is.null(dim(exp)) | is.null(dim(obs))) {
stop(paste0("Parameter 'exp' and 'obs' must have at least dimensions ",
"time_dim and dat_dim."))
}
if(any(is.null(names(dim(exp))))| any(nchar(names(dim(exp))) == 0) |
any(is.null(names(dim(obs))))| any(nchar(names(dim(obs))) == 0)) {
stop("Parameter 'exp' and 'obs' must have dimension names.")
}
if(!all(names(dim(exp)) %in% names(dim(obs))) |
!all(names(dim(obs)) %in% names(dim(exp)))) {
stop("Parameter 'exp' and 'obs' must have same dimension name.")
}
## time_dim
if (!is.character(time_dim) | length(time_dim) > 1) {
stop("Parameter 'time_dim' must be a character string.")
}
if (!time_dim %in% names(dim(exp)) | !time_dim %in% names(dim(obs))) {
stop("Parameter 'time_dim' is not found in 'exp' or 'obs' dimension.")
}
## dat_dim
if (!is.character(dat_dim)) {
stop("Parameter 'dat_dim' must be a character vector.")
}
if (!all(dat_dim %in% names(dim(exp))) | !all(dat_dim %in% names(dim(obs)))) {
stop("Parameter 'dat_dim' is not found in 'exp' or 'obs' dimension.")
}
## memb
if (!is.logical(memb) | length(memb) > 1) {
stop("Parameter 'memb' must be one logical value.")
}
## memb_dim
if (!memb) {
if (!is.character(memb_dim) | length(memb_dim) > 1) {
stop("Parameter 'memb_dim' must be a character string.")
}
if (!memb_dim %in% names(dim(exp)) | !memb_dim %in% names(dim(obs))) {
stop("Parameter 'memb_dim' is not found in 'exp' or 'obs' dimension.")
}
if (!memb_dim %in% dat_dim) {
stop("Parameter 'memb_dim' must be one element in parameter 'dat_dim'.")
}
}
## ncores
if (!is.null(ncores)) {
if (!is.numeric(ncores) | ncores %% 1 != 0 | ncores <= 0 |
length(ncores) > 1) {
stop("Parameter 'ncores' must be a positive integer.")
}
}
## exp and obs (2)
name_exp <- sort(names(dim(exp)))
name_obs <- sort(names(dim(obs)))
for (i in 1:length(dat_dim)) {
name_exp <- name_exp[-which(name_exp == dat_dim[i])]
name_obs <- name_obs[-which(name_obs == dat_dim[i])]
}
if(!all(dim(exp)[name_exp] == dim(obs)[name_obs])) {
stop(paste0("Parameter 'exp' and 'obs' must have the same length of ",
"all dimensions except 'dat_dim'."))
}
###############################
# Sort dimension
name_exp <- names(dim(exp))
name_obs <- names(dim(obs))
order_obs <- match(name_exp, name_obs)
if (any(order_obs != sort(order_obs))) {
obs <- Reorder(obs, order_obs)
}
#-----------------------------------
# Per-paired method: If any sdate along dat_dim is NA, turn all sdate points along dat_dim into NA.
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pos <- rep(0, length(dat_dim)) # dat_dim: [dataset, member]
for (i in 1:length(dat_dim)) {
pos[i] <- which(names(dim(obs)) == dat_dim[i])
}
outrows_exp <- MeanDims(exp, pos, na.rm = FALSE) +
MeanDims(obs, pos, na.rm = FALSE)
outrows_obs <- outrows_exp
for (i in 1:length(pos)) {
outrows_exp <- InsertDim(outrows_exp, pos[i], dim(exp)[pos[i]])
outrows_obs <- InsertDim(outrows_obs, pos[i], dim(obs)[pos[i]])
}
exp_for_clim <- exp
obs_for_clim <- obs
exp_for_clim[which(is.na(outrows_exp))] <- NA
obs_for_clim[which(is.na(outrows_obs))] <- NA
#-----------------------------------
res <- Apply(list(exp, obs, exp_for_clim, obs_for_clim),
target_dims = c(time_dim, dat_dim),
fun = .Ano_CrossValid,
memb_dim = memb_dim, memb = memb,
ncores = ncores)
return(res)
}
.Ano_CrossValid <- function(exp, obs, exp_for_clim, obs_for_clim,
memb_dim = 'member', memb = TRUE, ncores = NULL) {
# exp: [sdate, dat_dim, memb_dim]
# obs: [sdate, dat_dim, memb_dim]
ano_exp_list <- vector('list', length = dim(exp)[1]) #length: [sdate]
ano_obs_list <- vector('list', length = dim(obs)[1])
for (tt in 1:dim(exp)[1]) { #[sdate]
# calculate clim
exp_sub <- ClimProjDiags::Subset(exp_for_clim, 1, c(1:dim(exp)[1])[-tt])
obs_sub <- ClimProjDiags::Subset(obs_for_clim, 1, c(1:dim(obs)[1])[-tt])
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clim_exp <- apply(exp_sub, c(1:length(dim(exp)))[-1], mean, na.rm = TRUE) # average out time_dim -> [dat, memb]
clim_obs <- apply(obs_sub, c(1:length(dim(obs)))[-1], mean, na.rm = TRUE)
# ensemble mean
if (!memb) {
if (is.null(dim(clim_exp)) | length(dim(clim_exp)) == 1) { #dim: [member]
clim_exp <- mean(clim_exp, na.rm = TRUE) # a number
clim_obs <- mean(clim_obs, na.rm = TRUE)
} else {
pos <- which(names(dim(clim_exp)) == memb_dim)
pos <- c(1:length(dim(clim_exp)))[-pos]
dim_name <- names(dim(clim_exp))
dim_exp_ori <- dim(clim_exp)
dim_obs_ori <- dim(clim_obs)
clim_exp <- apply(clim_exp, pos, mean, na.rm = TRUE)
clim_obs <- apply(clim_obs, pos, mean, na.rm = TRUE)
if (is.null(names(dim(as.array(clim_exp))))) {
clim_exp <- as.array(clim_exp)
clim_obs <- as.array(clim_obs)
names(dim(clim_exp)) <- dim_name[pos]
names(dim(clim_obs)) <- dim_name[pos]
}
# Expand it back
clim_exp_tmp <- array(clim_exp, dim = c(dim_exp_ori[pos], dim_exp_ori[-pos]))
clim_obs_tmp <- array(clim_obs, dim = c(dim_obs_ori[pos], dim_obs_ori[-pos]))
# Reorder it back to dim(clim_exp)
tmp <- match(dim_exp_ori, dim(clim_exp_tmp))
if (any(tmp != sort(tmp))) {
clim_exp <- Reorder(clim_exp_tmp, tmp)
clim_obs <- Reorder(clim_obs_tmp, tmp)
} else {
clim_exp <- clim_exp_tmp
clim_obs <- clim_obs_tmp
}
}
}
# calculate ano
ano_exp_list[[tt]] <- ClimProjDiags::Subset(exp, 1, tt, drop = 'selected') - clim_exp
ano_obs_list[[tt]] <- ClimProjDiags::Subset(obs, 1, tt, drop = 'selected') - clim_obs
}
ano_exp <- array(unlist(ano_exp_list), dim = c(dim(exp)[-1], dim(exp)[1]))
ano_exp <- Reorder(ano_exp, c(length(dim(exp)), 1:(length(dim(exp)) - 1)))
ano_obs <- array(unlist(ano_obs_list), dim = c(dim(obs)[-1], dim(obs)[1]))
ano_obs <- Reorder(ano_obs, c(length(dim(obs)), 1:(length(dim(obs)) - 1)))
return(list(exp = ano_exp, obs = ano_obs))