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#'@rdname CST_Analogs
#'@title Downscaling using Analogs based on large scale fields.
#'@author M. Carmen Alvarez-Castro, \email{carmen.alvarez-castro@cmcc.it}
#'@author Maria M. Chaves-Montero, \email{mariadm.chaves@cmcc.it }
#'@author Nuria Perez-Zanon \email{nuria.perez@bsc.es}
#'@description This function perform a downscaling using Analogs. To compute
#'the analogs, the function search for days with similar large scale conditions
#'to downscaled fields to a local scale. The large scale and the local scale
#'regions are defined by the user. The large scale is usually given by
#'atmospheric circulation as sea level pressure or geopotential height
#'(Yiou et al, 2013) but the function gives the possibility to use another
#'field. The local scale will be usually given by precipitation or temperature
#'fields, but might be another variable.The analogs function will find the best
#'analogs based in Minimum Euclidean distance in the large scale pattern
#'(i.e.SLP).
#'
#'The search of analogs must be done in the longest dataset posible. This is
#'important since it is necessary to have a good representation of the
#'possible states of the field in the past, and therefore, to get better
#'This function has not constrains of specific regions, variables to downscale,
#'or data to be used (seasonal forecast data, climate projections data,
#'reanalyses data). The regrid into a finner scale is done interpolating with
#'CST_Load. Then, this interpolation is corrected selecting the analogs in the
#'large and local scale in based of the observations. The function is an
#'adapted version of the method of Yiou et al 2013. For an advanced search of
#'Analogs (multiple Analogs, different criterias, further information from the
#'metrics and date of the selected Analogs) use the'Analog'
#'function within 'CSTools' package.
#'@references Yiou, P., T. Salameh, P. Drobinski, L. Menut, R. Vautard,
#' and M. Vrac, 2013 : Ensemble reconstruction of the atmospheric column
#' from surface pressure using analogues. Clim. Dyn., 41, 1419-1437.
#' \email{pascal.yiou@lsce.ipsl.fr}
#'@param expL an 's2dv_cube' object containing the experimental field on the
#'large scale for which the analog is aimed. This field is used to in all the
#'criterias. If parameter 'expVar' is not provided, the function will return
#'the expL analog. The element 'data' in the 's2dv_cube' object must have, at
#'least, latitudinal and longitudinal dimensions. The object is expect to be
#'already subset for the desired large scale region.
#'@param obsL an 's2dv_cube' object containing the observational field on the
#'large scale. The element 'data' in the 's2dv_cube' object must have the same
#'latitudinal and longitudinal dimensions as parameter 'expL' and a temporal
#'dimension with the maximum number of available observations.
#'@param time_obsL a character string indicating the date of the observations
#'in the date format (i.e. "yyyy-mm-dd")
#'@param time_expL a character string indicating the date of the experiment
#'in the same format than time_obsL (i.e. "yyyy-mm-dd")
#'@param expVar an 's2dv_cube' object containing the experimental field on the
#'local scale, usually a different variable to the parameter 'expL'. If it is
#'not NULL (by default, NULL), the returned field by this function will be the
#'analog of parameter 'expVar'.
#'@param lonVar a vector containing the longitude of parameter 'expVar'.
#'@param latVar a vector containing the latitude of parameter 'expVar'.
#'@param obsVar an 's2dv_cube' containing the field of the same variable as the
#'passed in parameter 'expVar' for the same region.
#'@param region a vector of length four indicating the minimum longitude, the
#'maximum longitude, the minimum latitude and the maximum latitude.
#'@param dimension the dimension where the downscaling will be performed (i.e.
#''member', 'sdate',etc))
#'@seealso code{\link{CST_Load}}, \code{\link[s2dverification]{Load}} and
#'\code{\link[s2dverification]{CDORemap}}
#'@return An 'array' object containing the dowscaled values of the best
#'analogs.
#'@example
#'expL <- rnorm(1:200)
#'dim(expL) <- c(member=10,lat = 4, lon = 5)
#'obsL <- c(rnorm(1:180),expL[1,,]*1.2)
#'dim(obsL) <- c(time = 10,lat = 4, lon = 5)
#'time_obsL <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
#'time_expL <- time_obsL[1]
#'dimension <- names(dim(expL))[1]
#'lon <- seq(-1,5,1.5)
#'lat <- seq(30,35,1.5)
#'region <- c(min(lon), max(lon), min(lat), max(lat))
#'downscaled_field <- CST_Analogs(expL = expL, obsL = obsL, time_obsL=time_obsL,
#' time_expL=time_expL,dimension=dimension,lonVar = lon,
#' latVar = lat, region = region)
CST_Analogs <- function(expL, obsL, time_obsL, time_expL, dimension,
expVar = NULL, obsVar = NULL, region = NULL,
lonVar=NULL, latVar=NULL){
if (any(is.na(expL))) {
warning("Parameter 'expL' contains NA values.")
}
if (any(is.na(obsL))) {
warning("Parameter 'obsL' contains NA values.")
}
if(is.null(time_expL)){
stop("parameter 'time_expL' cannot be NULL")
}
if(is.null(time_obsL)){
stop("parameter 'time_obsL' cannot be NULL")
}
stop("Dimension is NULL. It is necessary to choose a dimension to perform the
downscaling (i.e. 'member', 'ftime','stdate') ")
}else{
warning(paste0("the dimension selected to perform the downscaling is '",
dimension,"' "))
}
ApplyAnalog<-Apply(expL, target_dims = list(c('lat', 'lon')),
criteria = "Large_dist", lonVar = lonVar,time_expL=time_expL,
latVar = latVar, region = region)
result<-ApplyAnalog$output1[1,,,]
return(result)
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#'@rdname Analogs
#'@title Analogs based on large scale fields.
#'
#'@author M. Carmen Alvarez-Castro, \email{carmen.alvarez-castro@cmcc.it}
#'@author Maria M. Chaves-Montero, \email{mariadm.chaves@cmcc.it }
#'@author Nuria Perez-Zanon \email{nuria.perez@bsc.es}
#'
#'@description This function perform a downscaling using Analogs. To compute
#'the analogs, the function search for days with similar large scale conditions
#'to downscaled fields in the local scale. The large scale and the local scale
#'regions are defined by the user. The large scale is usually given by
#'atmospheric circulation as sea level pressure or geopotential height (Yiou
#'et al, 2013) but the function gives the possibility to use another field. The
#'local scale will be usually given by precipitation or temperature fields, but
#'might be another variable.
#'The analogs function will find the best analogs based in three criterias:
#' (1) Minimum Euclidean distance in the large scale pattern (i.e. SLP)
#' (2) Minimum Euclidean distance in the large scale pattern (i.e. SLP)
#' and minimum Euclidean distance in the local scale pattern (i.e. SLP).
#' (3) Minimum Euclidean distance in the large scale pattern (i.e. SLP),
#' minimum distance in the local scale pattern (i.e. SLP) and highest
#' correlation in the local variable to downscale (i.e Precipitation).
#'The search of analogs must be done in the longest dataset posible. This is
#'important since it is necessary to have a good representation of the
#'possible states of the field in the past, and therefore, to get better
#'analogs. Once the search of the analogs is complete, and in order to used the
#'three criterias the user can select a number of analogs , using parameter
#''nAnalogs' to restrict
#'the selection of the best analogs in a short number of posibilities, the best
#'ones. This function has not constrains of specific regions, variables to
#'downscale, or data to be used (seasonal forecast data, climate projections
#'data, reanalyses data). The regrid into a finner scale is done interpolating
#'with CST_Load. Then, this interpolation is corrected selecting the analogs in
#'the large and local scale in based of the observations. The function is an
#'adapted version of the method of Yiou et al 2013.
#'
#'@references Yiou, P., T. Salameh, P. Drobinski, L. Menut, R. Vautard,
#'and M. Vrac, 2013 : Ensemble reconstruction of the atmospheric column
#'from surface pressure using analogues. Clim. Dyn., 41, 1419-1437.
#'\email{pascal.yiou@lsce.ipsl.fr}
#'
#'@param expL an array of N named dimensions containing the experimental field
#'on the large scale for which the analog is aimed. This field is used to in
#'all the criterias. If parameter 'expVar' is not provided, the function will
#'return the expL analog. The element 'data' in the 's2dv_cube' object must
#'have, at least, latitudinal and longitudinal dimensions. The object is expect
#'to be already subset for the desired large scale region.
#'@param obsL an array of N named dimensions containing the observational field
#'on the large scale. The element 'data' in the 's2dv_cube' object must have
#'the same latitudinal and longitudinal dimensions as parameter 'expL' and a
#' temporal dimension with the maximum number of available observations.
#'@param time_obsL a character string indicating the date of the observations
#'in the format "dd/mm/yyyy". Reference time to search for analogs.
#'@param time_expL a character string indicating the date of the experiment
#'in the format "dd/mm/yyyy". Time to find the analogs.
#'@param excludeTime a character string indicating the date of the observations
#'in the format "dd/mm/yyyy" to be excluded during the search of analogs, in a
#'forecast might be NULL, if is not a forecast can not be NULL.
#'@param expVar an array of N named dimensions containing the experimental
#'field on the local scale, usually a different variable to the parameter
#''expL'. If it is not NULL (by default, NULL), the returned field by this
#'function will be the analog of parameter 'expVar'.
#'@param obsVar an array of N named dimensions containing the field of the
#'same variable as the passed in parameter 'expVar' for the same region.
#'@param AnalogsInfo TRUE to get a list with two elements: 1) the downscaled
#'field and 2) the AnalogsInfo which contains: a) the number of the best
#'analogs, b) the corresponding value of the metric used in the selected
#'criteria (distance values for Large_dist and Local_dist,correlation values
#'for Local_cor), c)dates of the analogs). The analogs are listed in decreasing
#'order, the first one is the best analog (i.e if the selected criteria is
#'Local_cor the best analog will be the one with highest correlation, while for
#'Large_dist criteria the best analog will be the day with minimum Euclidean
#'distance). Set to FALSE to get a single analog, the best analog, for instance
#'for downscaling.
#'@param criteria a character string indicating the criteria to be used for the
#'selection of analogs:
#'\itemize{
#'\item{Large_dist} minimum Euclidean distance in the large scale pattern;
#'\item{Local_dist} minimum Euclidean distance in the large scale pattern
#'and minimum Euclidean distance in the local scale pattern; and
#'\item{Local_cor} minimum Euclidean distance in the large scale pattern,
#'minimum Euclidean distance in the local scale pattern and highest
#'correlation in the local variable to downscale.}
#'@param lonVar a vector containing the longitude of parameter 'expVar'.
#'@param latVar a vector containing the latitude of parameter 'expVar'.
#'@param region a vector of length four indicating the minimum longitude,
#'the maximum longitude, the minimum latitude and the maximum latitude.
#'@param nAnalogs number of Analogs to be selected to apply the criterias
#''Local_dist' or 'Local_cor'. This is not the necessary the number of analogs
#'that the user can get, but the number of events with minimum distance in
#'which perform the search of the best Analog. The default value for the
#''Large_dist' criteria is 1, for 'Local_dist' and 'Local_cor' criterias must
#' be greater than 1 in order to match with the first criteria, if nAnalogs is
#' NULL for 'Local_dist' and 'Local_cor' the default value will be set at the
#' length of 'time_obsL'. If AnalogsInfo is FALSE the function returns just
#' the best analog.
#'
#'@import multiApply
#'@import s2dverification
#'@import abind
#'@import ClimProjDiags
#'
#'@return AnalogsFields, dowscaled values of the best analogs for the criteria
#'selected. If AnalogsInfo is set to TRUE the function also returns a
#'list with the dowsncaled field and the Analogs Information.
#'
#'@examples
#'# Example 1:Downscaling using criteria 'Large_dist' and a single variable:
#'expSLP <- rnorm(1:20)
#'dim(expSLP) <- c(lat = 4, lon = 5)
#'obsSLP <- c(rnorm(1:180), expSLP * 1.2)
#'dim(obsSLP) <- c(time = 10, lat = 4, lon = 5)
#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
#'downscale_field <- Analogs(expL = expSLP, obsL = obsSLP, time_obsL = time_obsSLP,
#' time_expL = "01-01-1994")
#'
#'# Example 2: Downscaling using criteria 'Large_dist' and 2 variables:
#'obs.pr <- c(rnorm(1:200) * 0.001)
#'dim(obs.pr) <- dim(obsSLP)
#'downscale_field <- Analogs(expL = expSLP, obsL = obsSLP, obsVar = obs.pr,
#' time_obsL = time_obsSLP, time_expL = "01-01-1994")
#'
#'# Example 3:List of best Analogs using criteria 'Large_dist' and a single
#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 100)
#'time_obsSLP <- paste(rep("01", 100), rep("01", 100), 1920 : 2019, sep = "-")
#'downscale_field<- Analogs(expL = expSLP, obsL = obsSLP, time_obsSLP,
#' nAnalogs = 5, time_expL = "01-01-2003",
#' AnalogsInfo = TRUE, excludeTime = "01-01-2003")
#'# Example 4:List of best Analogs using criteria 'Large_dist' and 2 variables:
#'obsSLP <- c(rnorm(1:180), expSLP * 2)
#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
#'downscale_field <- Analogs(expL = expSLP, obsL = obsSLP, obsVar = obs.pr,
#' time_obsL = time_obsSLP,nAnalogs=5,
#' time_expL = "01-10-2003", AnalogsInfo = TRUE)
#'
#'# Example 5: Downscaling using criteria 'Local_dist' and 2 variables:
#'# analogs of local scale using criteria 2
#'region=c(lonmin = -1 ,lonmax = 2, latmin = 30, latmax = 33)
#'Local_scale <- Analogs(expL = expSLP, obsL = obsSLP, time_obsL = time_obsSLP,
#' obsVar = obs.pr, criteria = "Local_dist", lonVar = seq(-1, 5, 1.5),
#' latVar = seq(30, 35, 1.5), region = region,
#' time_expL = "01-10-2000", nAnalogs = 10, AnalogsInfo = TRUE)
#'
#'# Example 6: list of best analogs using criteria 'Local_dist' and 2
#'Local_scale <- Analogs(expL = expSLP, obsL = obsSLP, time_obsL = time_obsSLP,
#' criteria = "Local_dist", lonVar = seq(-1, 5, 1.5),
#' latVar = seq(30, 35, 1.5), region = region,
#' time_expL = "01-10-2000", nAnalogs = 5, AnalogsInfo = TRUE)
#'
#'# Example 7: Downscaling using Local_dist criteria
#'Local_scale <- Analogs(expL = expSLP, obsL = obsSLP, time_obsL = time_obsSLP,
#' criteria = "Local_dist", lonVar = seq(-1, 5, 1.5),
#' latVar = seq(30, 35, 1.5), region = region, time_expL = "01-10-2000",
#' nAnalogs = 10, AnalogsInfo = FALSE)
#'
#'# Example 8: Downscaling using criteria 'Local_cor' and 2 variables:
#'exp.pr <- c(rnorm(1:20) * 0.001)
#'dim(exp.pr) <- dim(expSLP)
#'Local_scalecor <- Analogs(expL = expSLP, obsL = obsSLP, time_obsL = time_obsSLP,
#' obsVar = obs.pr, expVar = exp.pr,
#' criteria = "Local_cor", lonVar = seq(-1, 5, 1.5),
#' time_expL = "01-10-2000", latVar = seq(30, 35, 1.5),
#' nAnalogs = 8, region = region, AnalogsInfo = FALSE)
#'# same but without imposing nAnalogs,so nAnalogs will be set by default as 10
#'Local_scalecor <- Analogs(expL = expSLP, obsL = obsSLP, time_obsL = time_obsSLP,
#' obsVar = obs.pr, expVar = exp.pr,
#' criteria = "Local_cor", lonVar = seq(-1,5,1.5),
#' time_expL = "01-10-2000", latVar=seq(30, 35, 1.5),
#' region = region, AnalogsInfo = TRUE)
#'#'Example 9: List of best analogs in the three criterias Large_dist,
#'Large_scale <- Analogs(expL = expSLP, obsL = obsSLP, time_obsL = time_obsSLP,
#' criteria = "Large_dist", time_expL = "01-10-2000",
#'Local_scale <- Analogs(expL = expSLP, obsL = obsSLP, time_obsL = time_obsSLP,
#' time_expL = "01-10-2000", criteria = "Local_dist",
#' lonVar = seq(-1, 5, 1.5), latVar = seq(30, 35, 1.5),
#' nAnalogs = 7,region = region, AnalogsInfo = TRUE)
#'Local_scalecor <- Analogs(expL = expSLP, obsL = obsSLP, time_obsL = time_obsSLP,
#' obsVar = obsSLP, expVar = expSLP, time_expL = "01-10-2000",
#' criteria = "Local_cor", lonVar = seq(-1, 5, 1.5),
#' latVar = seq(30, 35, 1.5), nAnalogs = 7,region = region,
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Analogs <- function(expL, obsL, time_obsL,time_expL=NULL,expVar = NULL,
obsVar = NULL,
criteria = "Large_dist",excludeTime=NULL,
lonVar = NULL, latVar = NULL, region = NULL,
nAnalogs = NULL,AnalogsInfo=FALSE) {
if (!all(c('lon', 'lat') %in% names(dim(expL)))) {
stop("Parameter 'expL' must have the dimensions 'lat' and 'lon'.")
}
if (!all(c('lat', 'lon') %in% names(dim(obsL)))) {
stop("Parameter 'obsL' must have the dimension 'lat' and 'lon'.")
}
if (any(is.na(expL))) {
warning("Parameter 'exp' contains NA values.")
}
if (any(is.na(obsL))) {
warning("Parameter 'obs' contains NA values.")
}
if (!is.null(expVar) & is.null(obsVar)) {
expVar <- NULL
warning("Parameter 'expVar' is set to NULL as parameter 'obsVar',
large scale field will be returned.")
}
if (is.null(expVar) & is.null(obsVar)) {
warning("Parameter 'expVar' and 'obsVar' are NULLs, downscaling/listing
same variable as obsL and expL'.")
}
if(!is.null(obsVar) & is.null(expVar) & criteria=="Local_cor"){
stop("parameter 'expVar' cannot be NULL")
}
if(is.null(nAnalogs) & criteria!="Large_dist"){
nAnalogs=length(time_obsL)
warning("Parameter 'nAnalogs' is NULL and is set to the same length of
time_obsL by default")
}
if(is.null(nAnalogs) & criteria=="Large_dist"){
nAnalogs=1
}
if(is.null(time_expL)){
stop("parameter 'time_expL' cannot be NULL")
}
if(is.null(time_obsL)){
stop("parameter 'time_obsL' cannot be NULL")
}
if(is.null(expL)){
stop("parameter 'expL' cannot be NULL")
}
if(!is.null(obsL)){
obsL=replace_time_dimnames(obsL)
if(time_expL %in% time_obsL){
if(is.null(excludeTime)){
excludeTime=time_expL
warning("Parameter 'excludeTime' is NULL, time_obsL contains
time_expL, so, by default, the date of
time_expL will be excluded in the search of analogs")
}else{
`%!in%` = Negate(`%in%`)
if(time_expL %!in% excludeTime){
excludeTime=c(excludeTime,time_expL)
warning("Parameter 'excludeTime' is not NULL, time_obsL contains
time_expL, so, by default, the date of
time_expL will be excluded in the search of analogs")
}
}
time_ref<- time_obsL[-c(which(time_obsL %in% excludeTime))]
posdim<- which(names(dim(obsL)) == 'time')
posref<- which(time_obsL %in% time_ref)
obsT<- Subset(obsL,along = posdim,indices = posref)
if(!is.null(obsVar)){obsTVar<- Subset(obsVar,along = posdim,
indices = posref)}
time_obsL <- time_ref
obsL <- obsT
if(!is.null(obsVar)){obsVar <- obsTVar}
}else{
if(is.null(excludeTime)){
if(!is.null(obsVar)){
warning("Parameter 'excludeTime' is NULL, time_obsL does not contain
time_expL, obsVar not NULL")
}else{
warning("Parameter 'excludeTime' is NULL, time_obsL does not contain
time_expL")
}
}else{
time_ref<- time_obsL[-c(which(time_obsL %in% excludeTime))]
posdim<- which(names(dim(obsL)) == 'time')
posref<- which(time_obsL %in% time_ref)
obsT<- Subset(obsL,along = posdim,indices = posref)
if(!is.null(obsVar)){obsTVar<- Subset(obsVar,along = posdim,
indices = posref)}
time_obsL <- time_ref
obsL <- obsT
if(!is.null(obsVar)){obsVar <- obsTVar}
if(!is.null(obsVar)){
warning("Parameter 'excludeTime' has a value and time_obsL does not
contain time_expL, obsVar not NULL")
}else{
warning("Parameter 'excludeTime' has a value and time_obsL does not
contain time_expL")
}
}
}
}else{stop("parameter 'obsL' cannot be NULL")}
if(is.null(obsVar)){
warning("obsVar is NULL")
}
if (any(names(dim(obsL)) %in% 'ftime')) {
if (any(names(dim(obsL)) %in% 'time')) {
stop("Multiple temporal dimensions ('ftime' and 'time') found",
"in parameter 'obsL'.")
} else {
time_pos_obsL <- which(names(dim(obsL)) == 'ftime')
names(dim(obsL))[time_pos_obsL] <- 'time'
if (any(names(dim(expL)) %in% 'ftime')) {
time_pos_expL <- which(names(dim(expL)) == 'ftime')
names(dim(expL))[time_pos_expL] <- 'time'
}
}
}
if(!is.null(obsVar)){
if (any(names(dim(obsVar)) %in% 'ftime')) {
if (any(names(dim(obsVar)) %in% 'time')) {
stop("Multiple temporal dimensions ('ftime' and 'time') found",
"in parameter 'obsVar'.")
} else {
time_pos_obsVar <- which(names(dim(obsVar)) == 'ftime')
names(dim(obsVar))[time_pos_obsVar] <- 'time'
if (any(names(dim(expVar)) %in% 'ftime')) {
time_pos_expVar <- which(names(dim(expVar)) == 'ftime')
names(dim(expVar))[time_pos_expVar] <- 'time'
}
}
}
}
if ((any(names(dim(obsL)) %in% 'sdate')) &&
(any(names(dim(obsL)) %in% 'time'))){
dims_obsL <- dim(obsL)
pos_sdate <- which(names(dim(obsL)) == 'sdate')
pos_time <- which(names(dim(obsL)) == 'time')
pos <- 1 : length(dim(obsL))
pos <- c(pos_time, pos_sdate, pos[-c(pos_sdate,pos_time)])
obsL <- aperm(obsL, pos)
dim(obsL) <- c(time = prod(dims_obsL[c(pos_time, pos_sdate)]),
dims_obsL[-c(pos_time, pos_sdate)])
}else{
if(any(names(dim(obsL)) %in% 'sdate')){
dims_obsL <- dim(obsL)
pos_sdate <- which(names(dim(obsL)) == 'sdate')
pos <- 1 : length(dim(obsL))
pos <- c( pos_sdate, pos[-c(pos_sdate)])
obsL <- aperm(obsL, pos)
dim(obsL) <- c(time = prod(dims_obsL[c(pos_sdate)]),
dims_obsL[-c( pos_sdate)])
}else{
if (any(names(dim(obsL)) %in% 'time')){
dims_obsL <- dim(obsL)
pos_time <- which(names(dim(obsL)) == 'time')
if(length(time_obsL)!=dim(obsL)[pos_time]){
stop(" 'time_obsL' and 'obsL' must have same length in the temporal
dimension.")
}
pos <- 1 : length(dim(obsL))
pos <- c(pos_time, pos[-c(pos_time)])
obsL <- aperm(obsL, pos)
dim(obsL) <- c(time = prod(dims_obsL[pos_time]),
dims_obsL[-c(pos_time)])
}else{stop("Parameter 'obsL' must have a temporal dimension.")}
}
}
if(!is.null(obsVar)){
if (any(names(dim(obsVar)) %in% 'sdate')) {
if (any(names(dim(obsVar)) %in% 'time')) {
dims_obsVar <- dim(obsVar)
pos_sdate <- which(names(dim(obsVar)) == 'sdate')
pos_time <- which(names(dim(obsVar)) == 'time')
pos <- 1 : length(dim(obsVar))
pos <- c(pos_time, pos_sdate, pos[-c(pos_sdate,pos_time)])
obsVar <- aperm(obsVar, pos)
dim(obsVar) <- c(time = prod(dims_obsVar[c(pos_time, pos_sdate)]),
dims_obsVar[-c(pos_time, pos_sdate)])
} else {
dims_obsVar <- dim(obsVar)
pos_sdate <- which(names(dim(obsVar)) == 'sdate')
pos <- 1 : length(dim(obsVar))
pos <- c(pos_sdate, pos[-c(pos_sdate)])
obsVar <- aperm(obsVar, pos)
dim(obsVar) <- c(time = prod(dims_obsVar[c(pos_sdate)]),
dims_obsVar[-c(pos_sdate)])
}
} else {
if (any(names(dim(obsVar)) %in% 'time')) {
dims_obsVar <- dim(obsVar)
pos_time <- which(names(dim(obsVar)) == 'time')
if(length(time_obsL)!=dim(obsVar)[pos_time]){
stop(" 'time_obsL' and 'obsVar' must have same length in the temporal
dimension.")}
pos <- 1 : length(dim(obsVar))
pos <- c(pos_time, pos[-c(pos_time)])
obsVar <- aperm(obsVar, pos)
dim(obsVar) <- c(time = prod(dims_obsVar[c(pos_time)]),
dims_obsVar[-c(pos_time)])
}else{
stop("Parameter 'obsVar' must have a temporal dimension.")
}
}
}
if (is.null(region) & criteria!="Large_dist") {
if (!is.null(lonVar) & !is.null(latVar)) {
region <- c(min(lonVar), max(lonVar), min(latVar), max(latVar))
}else{
stop("Parameters 'lonVar' and 'latVar' must be given in criteria
'Local_dist' and 'Local_cor'")
}
}
if ((length(time_expL)==1) && (nAnalogs>=1)){
warning("computing one day and 1 or more Analogs")
Analog_result <- FindAnalog(expL = expL, obsL = obsL, time_obsL=time_obsL,
expVar = expVar, obsVar = obsVar,
criteria = criteria,
AnalogsInfo = AnalogsInfo,
nAnalogs=nAnalogs,lonVar = lonVar,
latVar = latVar, region = region)
if(AnalogsInfo==TRUE){
AnalogsInfo=list(analog=Analog_result$Analog,
metric=Analog_result$metric,
dates=Analog_result$dates)
return(list(AnalogsFields=Analog_result$AnalogsFields,
AnalogsInfo=AnalogsInfo
)
)
}else{
return(AnalogsFields=Analog_result$AnalogsFields)
}
}
if ((length(time_expL)>1) && (nAnalogs>=1)){
warning("Computing more than 1 analog in more than one day")
Analog_result_timestep<-Apply(expL,
target_dims = list(c('lat', 'lon')),
margins=c('time'),
fun = FindAnalog,
AnalogsInfo = AnalogsInfo,
nAnalogs=nAnalogs,
obsL = obsL, time_obsL=time_obsL,
expVar = expVar, obsVar = obsVar,
criteria = criteria, lonVar = lonVar,
latVar = latVar, region = region
)
if(AnalogsInfo==TRUE){
names(dim(Analog_result_timestep$AnalogsFields))[1]<-'analog'
names(dim(Analog_result_timestep$Analog))[1]<-'analog'
names(dim(Analog_result_timestep$metric))[1]<-'analog'
names(dim(Analog_result_timestep$dates))[1]<-'analog'
Analog_result_timestep$dates<- as.POSIXct(Analog_result_timestep$dates,
origin = '1970-01-01')
#Analog_result_timestep$dates<- as.Date(Analog_result_timestep$dates)
AnalogsInfo=list(analog=Analog_result_timestep$Analog,
metric=Analog_result_timestep$metric,
dates=Analog_result_timestep$dates)
return(list(AnalogsFields=Analog_result_timestep$AnalogsFields,
AnalogsInfo=AnalogsInfo)
)
}else{
return(AnalogsFields=Analog_result_timestep$AnalogsFields)
}
}
}
FindAnalog <- function(expL,obsL,time_obsL,expVar,obsVar,criteria,lonVar,
latVar,region,
nAnalogs=nAnalogs,AnalogsInfo = AnalogsInfo) {
position <- Select(expL = expL, obsL = obsL, expVar = expVar,
obsVar = obsVar, criteria = criteria, lonVar = lonVar,
latVar = latVar, region = region)$position
metrics<- Select(expL = expL, obsL = obsL, expVar = expVar,
obsVar = obsVar, criteria = criteria, lonVar = lonVar,
latVar = latVar, region = region)$metric.original
best <- Apply(list(position), target_dims = c('time', 'pos'),
fun = BestAnalog, criteria = criteria,
AnalogsInfo = AnalogsInfo, nAnalogs = nAnalogs)$output1
Analogs_dates <- time_obsL[best]
dim(Analogs_dates) <- dim(best)
if (all(!is.null(region), !is.null(lonVar), !is.null(latVar))) {
if (is.null(obsVar)) {
obsVar <- SelBox(obsL, lon = lonVar, lat = latVar, region = region)$data
expVar <- SelBox(expL, lon = lonVar, lat = latVar, region=region)$data
Analogs_fields <- Subset(obsVar,
along = which(names(dim(obsVar)) == 'time'),
indices = best)
warning("obsVar is NULL,
obsVar will be computed from obsL (same variable)")
} else {
obslocal <- SelBox(obsVar, lon = lonVar, lat = latVar,
region = region)$data
Analogs_fields <- Subset(obslocal,
along = which(names(dim(obslocal)) == 'time'),
indices = best)
}
} else {
warning("One or more of the parameter 'region', 'lonVar' and 'latVar'",
" are NULL and the large scale field will be returned.")
if (is.null(obsVar)) {
Analogs_fields <- Subset(obsL, along = which(names(dim(obsL)) == 'time'),
indices = best)
} else {
Analogs_fields <- Subset(obsVar,
along = which(names(dim(obsVar)) == 'time'),
indices = best)
}
}
lon_dim <- which(names(dim(Analogs_fields)) == 'lon')
lat_dim <- which(names(dim(Analogs_fields)) == 'lat')
Analogs_metrics <- Subset(metrics,
along = which(names(dim(metrics)) == 'time'),
indices = best)
return(list(AnalogsFields=Analogs_fields,
Analog=as.numeric(1:nrow(Analogs_metrics)),
metric=Analogs_metrics,
dates=Analogs_dates)
)
}
BestAnalog <- function(position, nAnalogs = nAnalogs, AnalogsInfo = FALSE,
criteria = 'Large_dist') {
pos_dim <- which(names(dim(position)) == 'pos')
if (dim(position)[pos_dim] == 1) {
pos1 <- Subset(position, along = pos_dim, indices = 1)
if (criteria != 'Large_dist') {
warning("Dimension 'pos' in parameter 'position' has length 1,",
" criteria 'Large_dist' will be used.")
criteria <- 'Large_dist'
}
} else if (dim(position)[pos_dim] == 2) {
pos1 <- Subset(position, along = pos_dim, indices = 1)
pos2 <- Subset(position, along = pos_dim, indices = 2)
if (criteria == 'Local_cor') {
warning("Dimension 'pos' in parameter 'position' has length 2,",
" criteria 'Local_dist' will be used.")
criteria <- 'Local_dist'
}
} else if (dim(position)[pos_dim] == 3) {
pos1 <- Subset(position, along = pos_dim, indices = 1)
pos2 <- Subset(position, along = pos_dim, indices = 2)
pos3 <- Subset(position, along = pos_dim, indices = 3)
if (criteria != 'Local_cor') {
warning("Parameter 'criteria' is set to", criteria, ".")
}
} else {
stop("Parameter 'position' has dimension 'pos' of different ",
"length than expected (from 1 to 3).")
}
if (criteria == 'Large_dist') {
if (AnalogsInfo == FALSE) {
pos <- pos1[1]
} else {
pos <- pos1[1 : nAnalogs]
}
} else if (criteria== 'Local_dist') {
pos1 <- pos1[1 : nAnalogs]
pos2 <- pos2[1 : nAnalogs]
best <- match(pos1, pos2)
if(length(best)==1){
warning("Just 1 best analog matching Large_dist and ",
"Local_dist criteria")
}
if(length(best)<1 & is.na(best[1])==TRUE){
stop("no best analogs matching Large_dist and Local_dist criterias,
please increase nAnalogs")
}
pos <- pos2[as.logical(best)]
pos <- pos[which(!is.na(pos))]
if (AnalogsInfo == FALSE) {
pos <- pos[1]
}else {
pos <- pos}
} else if (criteria == 'Local_cor') {
pos1 <- pos1[1 : nAnalogs]
pos2 <- pos2[1 : nAnalogs]
best <- match(pos1, pos2)
if(length(best)==1){
warning("Just 1 best analog matching Large_dist and ",
"Local_dist criteria")
}
if(length(best)<1 & is.na(best[1])==TRUE){
stop("no best analogs matching Large_dist and Local_dist criterias,
please increase nAnalogs")
}
pos <- pos1[as.logical(best)]
pos <- pos[which(!is.na(pos))]
pos3 <- pos3[1 : nAnalogs]
best <- match(pos, pos3)
if(length(best)==1){
warning("Just 1 best analog matching Large_dist, Local_dist and ",
"Local_cor criteria")
}
if(length(best)<1 & is.na(best[1])==TRUE){
stop("no best analogs matching Large_dist, Local_dist and Local_cor
criterias, please increase nAnalogs")
}
pos <- pos[order(best, decreasing = F)]
pos <- pos[which(!is.na(pos))]
if (AnalogsInfo == FALSE) {
pos <- pos[1]
} else{
pos <- pos
}
return(pos)
}
}
Select <- function(expL, obsL, expVar = NULL, obsVar = NULL,
criteria = "Large_dist",
lonVar = NULL, latVar = NULL, region = NULL) {
names(dim(expL)) <- replace_repeat_dimnames(names(dim(expL)),
names(dim(obsL)))
metric1 <- Apply(list(obsL), target_dims = list(c('lat', 'lon')),
fun = .select, expL, metric = "dist")$output1
metric1.original=metric1
if (length(dim(metric1)) > 1) {
dim_time_obs <- which(names(dim(metric1)) == 'time' |
names(dim(metric1)) == 'ftime')
dim(metric1) <- c(dim(metric1), metric=1)
margins <- c(1 : (length(dim(metric1))))[-dim_time_obs]
pos1 <- apply(metric1, margins, order)
names(dim(pos1))[1] <- 'time'
metric1.original=metric1
metric1 <- apply(metric1, margins, sort)
names(dim(metric1))[1] <- 'time'
names(dim(metric1.original))=names(dim(metric1))
} else {
pos1 <- order(metric1)
dim(pos1) <- c(time = length(pos1))
metric1 <- sort(metric1)
dim(metric1) <- c(time = length(metric1))
dim(metric1.original)=dim(metric1)
dim_time_obs=1
}
if (criteria == "Large_dist") {
dim(metric1) <- c(dim(metric1), metric = 1)
dim(pos1) <- c(dim(pos1), pos = 1)
dim(metric1.original)=dim(metric1)
return(list(metric = metric1, metric.original=metric1.original,
position = pos1))
}
if (criteria == "Local_dist" | criteria == "Local_cor") {
obs <- SelBox(obsL, lon = lonVar, lat = latVar, region = region)$data
exp <- SelBox(expL, lon = lonVar, lat = latVar, region = region)$data
metric2 <- Apply(list(obs), target_dims = list(c('lat', 'lon')),
fun = .select, exp, metric = "dist")$output1
metric2.original=metric2
dim(metric2) <- c(dim(metric2), metric=1)
margins <- c(1 : (length(dim(metric2))))[-dim_time_obs]
pos2 <- apply(metric2, margins, order)
dim(pos2) <- dim(pos1)
names(dim(pos2))[1] <- 'time'
metric2 <- apply(metric2, margins, sort)
names(dim(metric2))[1] <- 'time'
if (criteria == "Local_dist") {
metric <- abind(metric1, metric2, along = length(dim(metric1))+1)
metric.original <- abind(metric1.original,metric2.original,
along=length(dim(metric1))+1)
position <- abind(pos1, pos2, along = length(dim(pos1))+1)
names(dim(metric)) <- c(names(dim(pos1)), 'metric')
names(dim(position)) <- c(names(dim(pos1)), 'pos')
names(dim(metric.original)) = names(dim(metric))
return(list(metric = metric, metric.original=metric.original,
position = position))
}
}
if (criteria == "Local_cor") {
obs <- SelBox(obsVar, lon = lonVar, lat = latVar, region = region)$data
exp <- SelBox(expVar, lon = lonVar, lat = latVar, region = region)$data
metric3 <- Apply(list(obs), target_dims = list(c('lat', 'lon')),
fun = .select, exp, metric = "cor")$output1
metric3.original=metric3
dim(metric3) <- c(dim(metric3), metric=1)
margins <- c(1 : (length(dim(metric3))))[-dim_time_obs]
pos3 <- apply(abs(metric3), margins, order, decreasing = TRUE)
names(dim(pos3))[1] <- 'time'
metricsort <- metric3[pos3]
dim(metricsort)=dim(metric3)
names(dim(metricsort))[1] <- 'time'
metric <- abind(metric1, metric2, metricsort,
along = length(dim(metric1)) + 1)
metric.original <- abind(metric1.original, metric2.original,
metric3.original,
along = length(dim(metric1)) + 1)
position <- abind(pos1, pos2, pos3, along = length(dim(pos1)) + 1)
names(dim(metric)) <- c(names(dim(metric1)), 'metric')
names(dim(position)) <- c(names(dim(pos1)), 'pos')
names(dim(metric.original)) = names(dim(metric))
return(list(metric = metric, metric.original=metric.original,
position = position))
}
else {
stop("Parameter 'criteria' must to be one of the: 'Large_dist', ",
"'Local_dist','Local_cor'.")
}
}
.select <- function(exp, obs, metric = "dist") {
if (metric == "dist") {
result <- Apply(list(obs), target_dims = list(c('lat', 'lon')),
fun = function(x) {sqrt(sum((x - exp) ^ 2, na.rm = TRUE))})$output1
} else if (metric == "cor") {
result <- Apply(list(obs), target_dims = list(c('lat', 'lon')),
fun = function(x) {cor(as.vector(x),
as.vector(exp),
method="spearman")})$output1
}
result
}
.time_ref<- function(time_obsL,time_expL,excludeTime){
sameTime=which(time_obsL %in% time_expL)
result<- c(time_obsL[1:(sameTime-excludeTime-1)],
time_obsL[(sameTime+excludeTime+1):length(time_obsL)])
result
}
replace_repeat_dimnames <- function(names_exp, names_obs, lat_name = 'lat',
lon_name = 'lon') {
if (!is.character(names_exp)) {
stop("Parameter 'names_exp' must be a vector of characters.")
}
if (!is.character(names_obs)) {
stop("Parameter 'names_obs' must be a vector of characters.")
}
latlon_dim_exp <- which(names_exp == lat_name | names_exp == lon_name)
latlon_dim_obs <- which(names_obs == lat_name | names_obs == lon_name)
if (any(unlist(lapply(names_exp[-latlon_dim_exp],
function(x){x == names_obs[-latlon_dim_obs]})))) {
original_pos <- lapply(names_exp,
function(x) which(x == names_obs[-latlon_dim_obs]))
original_pos <- lapply(original_pos, length) > 0
names_exp[original_pos] <- paste0(names_exp[original_pos], "_exp")
}
return(names_exp)
}
replace_time_dimnames <- function(dataL, time_name = 'time',
stdate_name='stdate', ftime_name='ftime') {
names_obs=names(dim(dataL))
if (!is.character(names_obs)) {
stop("Parameter 'names_obs' must be a vector of characters.")
}
time_dim_obs <- which(names_obs == time_name |
names_obs == stdate_name | names_obs == ftime_name)
if(length(time_dim_obs) >1){
stop ("more than 1 time dimension, please give just 1")
}
if(length(time_dim_obs) == 0){
warning ("name of time dimension is not 'ftime' or 'time' or 'stdate'
or time dimension is null")
}
if(length(time_dim_obs)!=0){ names_obs[time_dim_obs]= time_name}
names(dim(dataL))=names_obs
return(dataL)
}