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#'Interpolate arrays with longitude and latitude dimensions using CDO
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#'
#'This function takes as inputs a multidimensional array (optional), a vector
#'or matrix of longitudes, a vector or matrix of latitudes, a destination grid
#'specification, and the name of a method to be used to interpolate (one of
#'those available in the 'remap' utility in CDO). The interpolated array is
#'returned (if provided) together with the new longitudes and latitudes.\cr\cr
#'\code{CDORemap()} permutes by default the dimensions of the input array (if
#'needed), splits it in chunks (CDO can work with data arrays of up to 4
#'dimensions), generates a file with the data of each chunk, interpolates it
#'with CDO, reads it back into R and merges it into a result array. If no
#'input array is provided, the longitude and latitude vectors will be
#'transformed only. If the array is already on the desired destination grid,
#'no transformation is performed (this behvaiour works only for lonlat and
#'gaussian grids). \cr\cr
#'Any metadata attached to the input data array, longitudes or latitudes will
#'be preserved or accordingly modified.
#'
#'@param data_array Multidimensional numeric array to be interpolated. If
#' provided, it must have at least a longitude and a latitude dimensions,
#' identified by the array dimension names. The names for these dimensions
#' must be one of the recognized by s2dverification (can be checked with
#' \code{s2dverification:::.KnownLonNames()} and
#' \code{s2dverification:::.KnownLatNames()}).
#'@param lons Numeric vector or array of longitudes of the centers of the grid
#' cells. Its size must match the size of the longitude/latitude dimensions
#' of the input array.
#'@param lats Numeric vector or array of latitudes of the centers of the grid
#' cells. Its size must match the size of the longitude/latitude dimensions
#' of the input array.
#'@param grid Character string specifying either a name of a target grid
#' (recognized by CDO; e.g.: 'r256x128', 't106grid') or a path to another
#' NetCDF file which to read the target grid from (a single grid must be
#' defined in such file).
#'@param method Character string specifying an interpolation method
#' (recognized by CDO; e.g.: 'con', 'bil', 'bic', 'dis'). The following
#' long names are also supported: 'conservative', 'bilinear', 'bicubic' and
#' 'distance-weighted'.
#'@param avoid_writes The step of permutation is needed when the input array
#' has more than 3 dimensions and none of the longitude or latitude dimensions
#' in the right-most position (CDO would not accept it without permuting
#' previously). This step, executed by default when needed, can be avoided
#' for the price of writing more intermediate files (whis usually is
#' unconvenient) by setting the parameter \code{avoid_writes = TRUE}.
#'@param crop Whether to crop the data after interpolation with
#' 'cdo sellonlatbox' (TRUE) or to extend interpolated data to the whole
#' world as CDO does by default (FALSE). If \code{crop = TRUE} then the
#' longitude and latitude borders which to crop at are taken as the limits of
#' the cells at the borders ('lons' and 'lats' are perceived as cell centers),
#' i.e. the resulting array will contain data that covers the same area as
#' the input array. This is equivalent to specifying \code{crop = 'preserve'},
#' i.e. preserving area. If \code{crop = 'tight'} then the borders which to
#' crop at are taken as the minimum and maximum cell centers in 'lons' and
#' 'lats', i.e. the area covered by the resulting array may be smaller if
#' interpolating from a coarse grid to a fine grid. The parameter 'crop' also
#' accepts a numeric vector of custom borders which to crop at:
#' c(western border, eastern border, southern border, northern border).
#'@param force_remap Whether to force remapping, even if the input data array
#' is already on the target grid.
#'@param write_dir Path to the directory where to create the intermediate
#' files for CDO to work. By default, the R session temporary directory is
#' used (\code{tempdir()}).
#'
#'@return A list with the following components:
#' \item{'data_array'}{The interpolated data array (if an input array
#' is provided at all, NULL otherwise).}
#' \item{'lons'}{The longitudes of the data on the destination grid.}
#' \item{'lats'}{The latitudes of the data on the destination grid.}
#'@examples
#' \dontrun{
#'# Interpolating only vectors of longitudes and latitudes
#'lon <- seq(0, 360 - 360/50, length.out = 50)
#'lat <- seq(-90, 90, length.out = 25)
#'tas2 <- CDORemap(NULL, lon, lat, 't170grid', 'bil', TRUE)
#'
#'# Minimal array interpolation
#'tas <- array(1:50, dim = c(25, 50))
#'names(dim(tas)) <- c('lat', 'lon')
#'lon <- seq(0, 360 - 360/50, length.out = 50)
#'lat <- seq(-90, 90, length.out = 25)
#'tas2 <- CDORemap(tas, lon, lat, 't170grid', 'bil', TRUE)
#'
#'# Metadata can be attached to the inputs. It will be preserved and
#'# accordignly modified.
#'tas <- array(1:50, dim = c(25, 50))
#'names(dim(tas)) <- c('lat', 'lon')
#'lon <- seq(0, 360 - 360/50, length.out = 50)
#'metadata <- list(lon = list(units = 'degrees_east'))
#'attr(lon, 'variables') <- metadata
#'lat <- seq(-90, 90, length.out = 25)
#'metadata <- list(lat = list(units = 'degrees_north'))
#'attr(lat, 'variables') <- metadata
#'metadata <- list(tas = list(dim = list(lat = list(len = 25,
#' vals = lat),
#' lon = list(len = 50,
#' vals = lon)
#' )))
#'attr(tas, 'variables') <- metadata
#'tas2 <- CDORemap(tas, lon, lat, 't170grid', 'bil', TRUE)
#'
#'# Arrays of any number of dimensions in any order can be provided.
#'num_lats <- 25
#'num_lons <- 50
#'tas <- array(1:(10*num_lats*10*num_lons*10),
#' dim = c(10, num_lats, 10, num_lons, 10))
#'names(dim(tas)) <- c('a', 'lat', 'b', 'lon', 'c')
#'lon <- seq(0, 360 - 360/num_lons, length.out = num_lons)
#'metadata <- list(lon = list(units = 'degrees_east'))
#'attr(lon, 'variables') <- metadata
#'lat <- seq(-90, 90, length.out = num_lats)
#'metadata <- list(lat = list(units = 'degrees_north'))
#'attr(lat, 'variables') <- metadata
#'metadata <- list(tas = list(dim = list(a = list(),
#' lat = list(len = num_lats,
#' vals = lat),
#' b = list(),
#' lon = list(len = num_lons,
#' vals = lon),
#' c = list()
#' )))
#'attr(tas, 'variables') <- metadata
#'tas2 <- CDORemap(tas, lon, lat, 't17grid', 'bil', TRUE)
#'# The step of permutation can be avoided but more intermediate file writes
#'# will be performed.
#'tas2 <- CDORemap(tas, lon, lat, 't17grid', 'bil', FALSE)
#'
#'# If the provided array has the longitude or latitude dimension in the
#'# right-most position, the same number of file writes will be performed,
#'# even if avoid_wrties = FALSE.
#'num_lats <- 25
#'num_lons <- 50
#'tas <- array(1:(10*num_lats*10*num_lons*10),
#' dim = c(10, num_lats, 10, num_lons))
#'names(dim(tas)) <- c('a', 'lat', 'b', 'lon')
#'lon <- seq(0, 360 - 360/num_lons, length.out = num_lons)
#'metadata <- list(lon = list(units = 'degrees_east'))
#'attr(lon, 'variables') <- metadata
#'lat <- seq(-90, 90, length.out = num_lats)
#'metadata <- list(lat = list(units = 'degrees_north'))
#'attr(lat, 'variables') <- metadata
#'metadata <- list(tas = list(dim = list(a = list(),
#' lat = list(len = num_lats,
#' vals = lat),
#' b = list(),
#' lon = list(len = num_lons,
#' vals = lon)
#' )))
#'attr(tas, 'variables') <- metadata
#'tas2 <- CDORemap(tas, lon, lat, 't17grid', 'bil', TRUE)
#'tas2 <- CDORemap(tas, lon, lat, 't17grid', 'bil', FALSE)
#'
#'# An example of an interpolation from and onto a rectangular regular grid
#'num_lats <- 25
#'num_lons <- 50
#'tas <- array(1:(1*num_lats*num_lons), dim = c(num_lats, num_lons))
#'names(dim(tas)) <- c('y', 'x')
#'lon <- array(seq(0, 360 - 360/num_lons, length.out = num_lons),
#' dim = c(num_lons, num_lats))
#'metadata <- list(lon = list(units = 'degrees_east'))
#'names(dim(lon)) <- c('x', 'y')
#'attr(lon, 'variables') <- metadata
#'lat <- t(array(seq(-90, 90, length.out = num_lats),
#' dim = c(num_lats, num_lons)))
#'metadata <- list(lat = list(units = 'degrees_north'))
#'names(dim(lat)) <- c('x', 'y')
#'attr(lat, 'variables') <- metadata
#'tas2 <- CDORemap(tas, lon, lat, 'r100x50', 'bil')
#'
#'# An example of an interpolation from an irregular grid onto a gaussian grid
#'num_lats <- 25
#'num_lons <- 50
#'tas <- array(1:(10*num_lats*10*num_lons*10),
#' dim = c(10, num_lats, 10, num_lons))
#'names(dim(tas)) <- c('a', 'j', 'b', 'i')
#'lon <- array(seq(0, 360 - 360/num_lons, length.out = num_lons),
#' dim = c(num_lons, num_lats))
#'metadata <- list(lon = list(units = 'degrees_east'))
#'names(dim(lon)) <- c('i', 'j')
#'attr(lon, 'variables') <- metadata
#'lat <- t(array(seq(-90, 90, length.out = num_lats),
#' dim = c(num_lats, num_lons)))
#'metadata <- list(lat = list(units = 'degrees_north'))
#'names(dim(lat)) <- c('i', 'j')
#'attr(lat, 'variables') <- metadata
#'tas2 <- CDORemap(tas, lon, lat, 't17grid', 'bil')
#'
#'# Again, the dimensions can be in any order
#'num_lats <- 25
#'num_lons <- 50
#'tas <- array(1:(10*num_lats*10*num_lons),
#' dim = c(10, num_lats, 10, num_lons))
#'names(dim(tas)) <- c('a', 'j', 'b', 'i')
#'lon <- array(seq(0, 360 - 360/num_lons, length.out = num_lons),
#' dim = c(num_lons, num_lats))
#'names(dim(lon)) <- c('i', 'j')
#'lat <- t(array(seq(-90, 90, length.out = num_lats),
#' dim = c(num_lats, num_lons)))
#'names(dim(lat)) <- c('i', 'j')
#'tas2 <- CDORemap(tas, lon, lat, 't17grid', 'bil')
#'tas2 <- CDORemap(tas, lon, lat, 't17grid', 'bil', FALSE)
#'# It is ossible to specify an external NetCDF file as target grid reference
#'tas2 <- CDORemap(tas, lon, lat, 'external_file.nc', 'bil')
#'}
#'@import ncdf4
#'@importFrom easyNCDF ArrayToNc
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#'@importFrom stats lm predict setNames
#'@export
CDORemap <- function(data_array = NULL, lons, lats, grid, method,
avoid_writes = TRUE, crop = TRUE,
force_remap = FALSE, write_dir = tempdir()) { #, mask = NULL) {
.isRegularVector <- function(x, tol = 0.1) {
if (length(x) < 2) {
#stop("The provided vector must be of length 2 or greater.")
TRUE
} else {
spaces <- x[2:length(x)] - x[1:(length(x) - 1)]
(sum(abs(spaces - mean(spaces)) > mean(spaces) / (1 / tol)) < 2)
}
}
# Check parameters data_array, lons and lats.
known_lon_names <- .KnownLonNames()
known_lat_names <- .KnownLatNames()
if (!is.numeric(lons) || !is.numeric(lats)) {
stop("Expected numeric 'lons' and 'lats'.")
}
if (any(is.na(lons > 0))) {
stop("Found invalid values in 'lons'.")
}
if (any(is.na(lats > 0))) {
stop("Found invalid values in 'lats'.")
}
if (is.null(dim(lons))) {
dim(lons) <- length(lons)
}
if (is.null(dim(lats))) {
dim(lats) <- length(lats)
}
if (length(dim(lons)) > 2 || length(dim(lats)) > 2) {
stop("'lons' and 'lats' can only have up to 2 dimensions.")
}
if (length(dim(lons)) != length(dim(lats))) {
stop("'lons' and 'lats' must have the same number of dimensions.")
}
if (length(dim(lons)) == 2 && !all(dim(lons) == dim(lats))) {
stop("'lons' and 'lats' must have the same dimension sizes.")
}
return_array <- TRUE
if (is.null(data_array)) {
return_array <- FALSE
if (length(dim(lons)) == 1) {
array_dims <- c(length(lats), length(lons))
new_lon_dim_name <- 'lon'
new_lat_dim_name <- 'lat'
if (!is.null(names(dim(lons)))) {
if (any(known_lon_names %in% names(dim(lons)))) {
new_lon_dim_name <- known_lon_names[which(known_lon_names %in% names(dim(lons)))[1]]
}
if (!is.null(names(dim(lats)))) {
if (any(known_lat_names %in% names(dim(lats)))) {
new_lat_dim_name <- known_lat_names[which(known_lat_names %in% names(dim(lats)))[1]]
}
}
names(array_dims) <- c(new_lat_dim_name, new_lon_dim_name)
} else { # irregular
array_dims <- dim(lons)
if (is.null(names(array_dims))) {
new_lon_dim_name <- 'i'
new_lat_dim_name <- 'j'
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data_array <- array(as.numeric(NA), array_dims)
}
if (!(is.logical(data_array) || is.numeric(data_array)) || !is.array(data_array)) {
stop("Parameter 'data_array' must be a numeric array.")
}
if (is.null(names(dim(data_array)))) {
stop("Parameter 'data_array' must have named dimensions.")
}
lon_dim <- which(known_lon_names %in% names(dim(data_array)))
if (length(lon_dim) < 1) {
stop("Could not find a known longitude dimension name in the provided 'data_array'.")
}
if (length(lon_dim) > 1) {
stop("Found more than one known longitude dimension names in the provided 'data_array'.")
}
lon_dim <- known_lon_names[lon_dim]
lat_dim <- which(known_lat_names %in% names(dim(data_array)))
if (length(lat_dim) < 1) {
stop("Could not find a known latitude dimension name in the provided 'data_array'.")
}
if (length(lat_dim) > 1) {
stop("Found more than one known latitude dimension name in the provided 'data_array'.")
}
lat_dim <- known_lat_names[lat_dim]
if (is.null(names(dim(lons)))) {
if (length(dim(lons)) == 1) {
names(dim(lons)) <- lon_dim
} else {
stop("Parameter 'lons' must be provided with dimension names.")
}
} else {
if (!(lon_dim %in% names(dim(lons)))) {
stop("Parameter 'lon' must have the same longitude dimension name as the 'data_array'.")
}
if (length(dim(lons)) > 1 && !(lat_dim %in% names(dim(lons)))) {
stop("Parameter 'lon' must have the same latitude dimension name as the 'data_array'.")
}
}
if (is.null(names(dim(lats)))) {
if (length(dim(lats)) == 1) {
names(dim(lats)) <- lat_dim
} else {
stop("Parameter 'lats' must be provided with dimension names.")
}
} else {
if (!(lat_dim %in% names(dim(lats)))) {
stop("Parameter 'lat' must have the same latitude dimension name as the 'data_array'.")
}
if (length(dim(lats)) > 1 && !(lon_dim %in% names(dim(lats)))) {
stop("Parameter 'lat' must have the same longitude dimension name as the 'data_array'.")
}
}
lons_attr_bk <- attributes(lons)
if (is.null(lons_attr_bk)) {
lons_attr_bk <- list()
}
lats_attr_bk <- attributes(lats)
if (is.null(lats_attr_bk)) {
lats_attr_bk <- list()
}
if (length(attr(lons, 'variables')) == 0) {
new_metadata <- list(list())
if (length(dim(lons)) == 1) {
names(new_metadata) <- lon_dim
} else {
names(new_metadata) <- paste0(lon_dim, '_var')
}
attr(lons, 'variables') <- new_metadata
}
if (!('units' %in% names(attr(lons, 'variables')[[1]]))) {
new_metadata <- attr(lons, 'variables')
#names(new_metadata)[1] <- lon_dim
new_metadata[[1]][['units']] <- 'degrees_east'
attr(lons, 'variables') <- new_metadata
}
if (length(attr(lats, 'variables')) == 0) {
new_metadata <- list(list())
if (length(dim(lats)) == 1) {
names(new_metadata) <- lat_dim
} else {
names(new_metadata) <- paste0(lat_dim, '_var')
}
attr(lats, 'variables') <- new_metadata
}
if (!('units' %in% names(attr(lats, 'variables')[[1]]))) {
new_metadata <- attr(lats, 'variables')
#names(new_metadata)[1] <- lat_dim
new_metadata[[1]][['units']] <- 'degrees_north'
attr(lats, 'variables') <- new_metadata
}
# Check grid.
if (!is.character(grid)) {
stop("Parameter 'grid' must be a character string specifying a ",
"target CDO grid, 'rXxY' or 'tRESgrid', or a path to another ",
"NetCDF file.")
}
if (grepl('^r[0-9]{1,}x[0-9]{1,}$', grid)) {
grid_type <- 'regular'
grid_lons <- as.numeric(strsplit(strsplit(grid, 'x')[[1]][1], 'r')[[1]][2])
grid_lats <- as.numeric(strsplit(grid, 'x')[[1]][2])
} else if (grepl('^t[0-9]{1,}grid$', grid)) {
grid_type <- 'gaussian'
grid_t <- as.numeric(strsplit(strsplit(grid, 'grid')[[1]][1], 't')[[1]][2])
grid_size <- .t2nlatlon(grid_t)
grid_lons <- grid_size[2]
grid_lats <- grid_size[1]
} else {
grid_type <- 'custom'
}
# Check method.
if (method %in% c('bil', 'bilinear')) {
method <- 'bil'
} else if (method %in% c('bic', 'bicubic')) {
method <- 'bic'
} else if (method %in% c('con', 'conservative')) {
method <- 'con'
} else if (method %in% c('dis', 'distance-weighted')) {
method <- 'dis'
} else {
stop("Unsupported CDO remap method. 'bilinear', 'bicubic', 'conservative' or 'distance-weighted' supported only.")
}
# Check avoid_writes
if (!is.logical(avoid_writes)) {
stop("Parameter 'avoid_writes' must be a logical value.")
}
# Check crop
crop_tight <- FALSE
if (is.character(crop)) {
if (crop == 'tight') {
crop_tight <- TRUE
} else if (crop != 'preserve') {
stop("Parameter 'crop' can only take the values 'tight' or 'preserve' if specified as a character string.")
}
crop <- TRUE
}
if (is.logical(crop)) {
if (crop) {
warning("Parameter 'crop' = 'TRUE'. The output grid range will follow the input lons and lats.")
if (length(lons) == 1 || length(lats) == 1) {
stop("CDORemap cannot remap if crop = TRUE and values for only one ",
"longitude or one latitude are provided. Either a) provide ",
"values for more than one longitude/latitude, b) explicitly ",
"specify the crop limits in the parameter crop, or c) set ",
"crop = FALSE.")
}
if (crop_tight) {
lon_extremes <- c(min(lons), max(lons))
lat_extremes <- c(min(lats), max(lats))
} else {
# Here we are trying to look for the extreme lons and lats in the data.
# Not the centers of the extreme cells, but the borders of the extreme cells.
###---
if (length(dim(lons)) == 1) {
tmp_lon <- lons
} else {
min_pos <- which(lons == min(lons), arr.ind = TRUE)[1, ]
tmp_lon <- Subset(lons, lat_dim, min_pos[which(names(dim(lons)) == lat_dim)], drop = 'selected')
}
i <- 1:length(tmp_lon)
degree <- min(3, length(i) - 1)
lon_model <- lm(tmp_lon ~ poly(i, degree))
lon_extremes <- c(NA, NA)
left_is_min <- FALSE
right_is_max <- FALSE
if (which.min(tmp_lon) == 1) {
left_is_min <- TRUE
prev_lon <- predict(lon_model, data.frame(i = 0))
first_lon_cell_width <- (tmp_lon[1] - prev_lon)
# The signif is needed because cdo sellonlatbox crashes with too many digits
lon_extremes[1] <- tmp_lon[1] - first_lon_cell_width / 2
} else {
lon_extremes[1] <- min(tmp_lon)
}
if (which.max(tmp_lon) == length(tmp_lon)) {
right_is_max <- TRUE
next_lon <- predict(lon_model, data.frame(i = length(tmp_lon) + 1))
last_lon_cell_width <- (next_lon - tmp_lon[length(tmp_lon)])
lon_extremes[2] <- tmp_lon[length(tmp_lon)] + last_lon_cell_width / 2
} else {
lon_extremes[2] <- max(tmp_lon)
}
# Adjust the crop window if possible in order to keep lons from 0 to 360
# or from -180 to 180 when the extremes of the cropped window are contiguous.
if (right_is_max) {
if (lon_extremes[1] < -180) {
if (!((lon_extremes[2] < 180) && !((180 - lon_extremes[2]) <= last_lon_cell_width / 2))) {
lon_extremes[1] <- -180
lon_extremes[2] <- 180
}
} else if (lon_extremes[1] < 0) {
if (!((lon_extremes[2] < 360) && !((360 - lon_extremes[2]) <= last_lon_cell_width / 2))) {
lon_extremes[1] <- 0
lon_extremes[2] <- 360
}
}
}
if (left_is_min) {
if (lon_extremes[2] > 360) {
if (!((lon_extremes[1] > 0) && !(lon_extremes[1] <= first_lon_cell_width / 2))) {
lon_extremes[1] <- 0
lon_extremes[2] <- 360
}
} else if (lon_extremes[2] > 180) {
if (!((lon_extremes[1] > -180) && !((180 + lon_extremes[1]) <= first_lon_cell_width / 2))) {
lon_extremes[1] <- -180
lon_extremes[2] <- 180
}
}
}
## lon_extremes <- signif(lon_extremes, 5)
## lon_extremes <- lon_extremes + 0.00001
###---
if (length(dim(lats)) == 1) {
tmp_lat <- lats
} else {
min_pos <- which(lats == min(lats), arr.ind = TRUE)[1, ]
tmp_lat <- Subset(lats, lon_dim, min_pos[which(names(dim(lats)) == lon_dim)], drop = 'selected')
}
i <- 1:length(tmp_lat)
degree <- min(3, length(i) - 1)
lat_model <- lm(tmp_lat ~ poly(i, degree))
lat_extremes <- c(NA, NA)
if (which.min(tmp_lat) == 1) {
prev_lat <- predict(lat_model, data.frame(i = 0))
lat_extremes[1] <- tmp_lat[1] - (tmp_lat[1] - prev_lat) / 2
} else {
lat_extremes[1] <- min(tmp_lat)
}
if (which.max(tmp_lat) == length(tmp_lat)) {
next_lat <- predict(lat_model, data.frame(i = length(tmp_lat) + 1))
lat_extremes[2] <- tmp_lat[length(tmp_lat)] + (next_lat - tmp_lat[length(tmp_lat)]) / 2
} else {
lat_extremes[2] <- max(tmp_lat)
}
## lat_extremes <- signif(lat_extremes, 5)
# Adjust crop window
if (lat_extremes[1] < -90) {
lat_extremes[1] <- -90
} else if (lat_extremes[1] > 90) {
lat_extremes[1] <- 90
}
if (lat_extremes[2] < -90) {
lat_extremes[2] <- -90
} else if (lat_extremes[2] > 90) {
lat_extremes[2] <- 90
}
###---
}
} else if (crop == FALSE) {
warning("Parameter 'crop' = 'FALSE'. The output grid range will follow parameter 'grid'.")
}
} else if (is.numeric(crop)) {
if (length(crop) != 4) {
stop("Paramrter 'crop' must be a logical value or a numeric vector of length 4: c(western border, eastern border, southern border, northern border.")
} else {
lon_extremes <- crop[1:2]
lat_extremes <- crop[3:4]
crop <- TRUE
}
} else {
stop("Parameter 'crop' must be a logical value or a numeric vector.")
}
# Check force_remap
if (!is.logical(force_remap)) {
stop("Parameter 'force_remap' must be a logical value.")
}
# Check write_dir
if (!is.character(write_dir)) {
stop("Parameter 'write_dir' must be a character string.")
}
if (!dir.exists(write_dir)) {
stop("Parameter 'write_dir' must point to an existing directory.")
}
# if (!is.null(mask)) {
# if (!is.numeric(mask) || !is.array(mask)) {
# stop("Parameter 'mask' must be a numeric array.")
# }
# if (length(dim(mask)) != 2) {
# stop("Parameter 'mask' must have two dimensions.")
# }
# if (is.null(names(dim(mask)))) {
# if (dim(data_array)[lat_dim] == dim(data_array)[lon_dim]) {
# stop("Cannot disambiguate which is the longitude dimension of ",
# "the provided 'mask'. Provide it with dimension names.")
# }
# names(dim(mask)) <- c('', '')
# found_lon_dim <- which(dim(mask) == dim(data_array)[lon_dim])
# if (length(found_lon_dim) < 0) {
# stop("The dimension sizes of the provided 'mask' do not match ",
# "the spatial dimension sizes of the array to interpolate.")
# } else {
# names(dim(mask)[found_lon_dim]) <- lon_dim
# }
# found_lat_dim <- which(dim(mask) == dim(data_array)[lat_dim])
# if (length(found_lat_dim) < 0) {
# stop("The dimension sizes of the provided 'mask' do not match ",
# "the spatial dimension sizes of the array to interpolate.")
# } else {
# names(dim(mask)[found_lat_dim]) <- lat_dim
# }
# }
# lon_position <- which(names(dim(data_array)) == lon_dim)
# lat_position <- which(names(dim(data_array)) == lat_dim)
# if (lon_position > lat_position) {
# if (names(dim(mask))[1] == lon_dim) {
# mask <- t(mask)
# }
# } else {
# if (names(dim(mask))[1] == lat_dim) {
# mask <- t(mask)
# }
# }
# ## TODO: Apply mask!!! Preserve attributes
# }
# Check if interpolation can be skipped.
interpolation_needed <- TRUE
if (!force_remap) {
if (!(grid_type == 'custom')) {
if (length(lons) == grid_lons && length(lats) == grid_lats) {
if (grid_type == 'regular') {
if (.isRegularVector(lons) && .isRegularVector(lats)) {
interpolation_needed <- FALSE
}
} else if (grid_type == 'gaussian') {
# TODO: improve this check. Gaussian quadrature should be used.
if (.isRegularVector(lons) && !.isRegularVector(lats)) {
interpolation_needed <- FALSE
}
}
}
}
}
found_lons <- lons
found_lats <- lats
if (interpolation_needed) {
if (nchar(Sys.which('cdo')[1]) < 1) {
stop("CDO must be installed in order to use the .CDORemap.")
}
cdo_version <- as.numeric_version(
strsplit(suppressWarnings(system2("cdo", args = '-V', stderr = TRUE))[[1]], ' ')[[1]][5]
)
warning("CDORemap: Using CDO version ", cdo_version, ".")
if ((cdo_version >= as.numeric_version('1.7.0')) && (method == 'con')) {
method <- 'ycon'
}
# CDO takes arrays of 3 dimensions or 4 if one of them is unlimited.
# The unlimited dimension can only be the left-most (right-most in R).
# There are no restrictions for the dimension names or variable names.
# The longitude and latitude are detected by their units.
# There are no restrictions for the order of the limited dimensions.
# The longitude/latitude variables and dimensions must have the same name.
# The procedure consists in:
# - take out the array metadata
# - be aware of var dimension (replacing the dimension names would do).
# - take arrays of 4 dimensions always if possible
# - make the last dimension unlimited when saving to netcdf
# - if the last dimension is lon or lat, either reorder the array and
# then reorder back or iterate over the dimensions at the right
# side of lon AND lat.
# If the input array has more than 4 dimensions, it is needed to
# run CDO on each sub-array of 4 dimensions because it can handle
# only up to 4 dimensions. The shortest dimensions are chosen to
# iterate over.
is_irregular <- FALSE
if (length(dim(lats)) > 1 && length(dim(lons)) > 1) {
is_irregular <- TRUE
}
attribute_backup <- attributes(data_array)
other_dims <- which(!(names(dim(data_array)) %in% c(lon_dim, lat_dim)))
permutation <- NULL
unlimited_dim <- NULL
dims_to_iterate <- NULL
total_slices <- 1
other_dims_per_chunk <- ifelse(is_irregular, 1, 2) # 4 (the maximum accepted by CDO) - 2 (lon, lat) = 2.
if (length(other_dims) > 1 || (length(other_dims) > 0 && (is_irregular))) {
if (!(length(dim(data_array)) %in% other_dims)) {
if (avoid_writes || is_irregular) {
dims_mod <- dim(data_array)
dims_mod[which(names(dim(data_array)) %in%
c(lon_dim, lat_dim))] <- 0
dim_to_move <- which.max(dims_mod)
permutation <- (1:length(dim(data_array)))[-dim_to_move]
permutation <- c(permutation, dim_to_move)
permutation_back <- sort(permutation, index.return = TRUE)$ix
dim_backup <- dim(data_array)
data_array <- aperm(data_array, permutation)
dim(data_array) <- dim_backup[permutation]
other_dims <- which(!(names(dim(data_array)) %in% c(lon_dim, lat_dim)))
} else {
# We allow only lon, lat and 1 more dimension per chunk, so
# CDO has no restrictions in the order.
other_dims_per_chunk <- 1
}
}
other_dims_ordered_by_size <- other_dims[sort(dim(data_array)[other_dims], index.return = TRUE)$ix]
dims_to_iterate <- sort(head(other_dims_ordered_by_size, length(other_dims) - other_dims_per_chunk))
if (length(dims_to_iterate) == 0) {
dims_to_iterate <- NULL
} else {
slices_to_iterate <- array(1:prod(dim(data_array)[dims_to_iterate]),
dim(data_array)[dims_to_iterate])
total_slices <- prod(dim(slices_to_iterate))
}
if ((other_dims_per_chunk > 1) || (other_dims_per_chunk > 0 && is_irregular)) {
unlimited_dim <- tail(sort(tail(other_dims_ordered_by_size, other_dims_per_chunk)), 1)
#unlimited_dim <- tail(other_dims)
}
}
result_array <- NULL
lon_pos <- which(names(dim(data_array)) == lon_dim)
lat_pos <- which(names(dim(data_array)) == lat_dim)
dim_backup <- dim(data_array)
attributes(data_array) <- NULL
dim(data_array) <- dim_backup
names(dim(data_array)) <- paste0('dim', 1:length(dim(data_array)))
names(dim(data_array))[c(lon_pos, lat_pos)] <- c(lon_dim, lat_dim)
if (!is.null(unlimited_dim)) {
# This will make ArrayToNc create this dim as unlimited.
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names(dim(data_array))[unlimited_dim] <- 'time'
}
if (length(dim(lons)) == 1) {
names(dim(lons)) <- lon_dim
}
if (length(dim(lats)) == 1) {
names(dim(lats)) <- lat_dim
}
if (length(dim(lons)) > 1) {
lon_var_name <- paste0(lon_dim, '_var')
} else {
lon_var_name <- lon_dim
}
if (length(dim(lats)) > 1) {
lat_var_name <- paste0(lat_dim, '_var')
} else {
lat_var_name <- lat_dim
}
if (is_irregular) {
metadata <- list(list(coordinates = paste(lon_var_name, lat_var_name)))
names(metadata) <- 'var'
attr(data_array, 'variables') <- metadata
}
names(attr(lons, 'variables')) <- lon_var_name
names(attr(lats, 'variables')) <- lat_var_name
if (!is.null(attr(lons, 'variables')[[1]][['dim']])) {
attr(lons, 'variables')[[1]][['dim']] <- NULL
}
if (!is.null(attr(lats, 'variables')[[1]][['dim']])) {
attr(lats, 'variables')[[1]][['dim']] <- NULL
}
lons_lats_taken <- FALSE
for (i in 1:total_slices) {
tmp_file <- tempfile('R_CDORemap_', write_dir, fileext = '.nc')
tmp_file2 <- tempfile('R_CDORemap_', write_dir, fileext = '.nc')
if (!is.null(dims_to_iterate)) {
slice_indices <- which(slices_to_iterate == i, arr.ind = TRUE)
subset <- Subset(data_array, dims_to_iterate, as.list(slice_indices), drop = 'selected')
# Fix issue 259, curvilinear grid, the order of the dimensions in slices and
# coordinates needs to match
if (is_irregular) {
pos_lon <- which(names(dim(subset)) == lon_dim)
pos_lat <- which(names(dim(subset)) == lat_dim)
pos_lon_dim_in_lons <- which(names(dim(lons)) == lon_dim)
pos_lat_dim_in_lons <- which(names(dim(lons)) == lat_dim)
if ((pos_lon > pos_lat && pos_lon_dim_in_lons < pos_lat_dim_in_lons) ||
(pos_lon < pos_lat && pos_lon_dim_in_lons > pos_lat_dim_in_lons)) {
new_pos <- 1:length(dim(subset))
new_pos[pos_lon] <- pos_lat
new_pos[pos_lat] <- pos_lon
subset <- .aperm2(subset, new_pos)
}
# The unlimited dimension should be placed in the last position
if ('time' %in% names(dim(subset)) &&
which(names(dim(subset)) == 'time') != length(dim(subset))) {
new_pos <- 2:length(dim(subset))
new_pos[length(dim(subset))] <- 1
subset <- .aperm2(subset, new_pos)
}
}
# dims_before_crop <- dim(subset)
# Make sure subset goes along with metadata
easyNCDF::ArrayToNc(setNames(list(subset, lons, lats), c('var', lon_var_name, lat_var_name)), tmp_file)
if (is_irregular) {
pos_lon <- which(names(dim(data_array)) == lon_dim)
pos_lat <- which(names(dim(data_array)) == lat_dim)
pos_lon_dim_in_lons <- which(names(dim(lons)) == lon_dim)
pos_lat_dim_in_lons <- which(names(dim(lons)) == lat_dim)
if ((pos_lon > pos_lat && pos_lon_dim_in_lons < pos_lat_dim_in_lons) ||
(pos_lon < pos_lat && pos_lon_dim_in_lons > pos_lat_dim_in_lons)) {
new_pos <- 1:length(dim(data_array))
new_pos[pos_lon] <- pos_lat
new_pos[pos_lat] <- pos_lon
data_array <- .aperm2(data_array, new_pos)
}
}
# dims_before_crop <- dim(data_array)
easyNCDF::ArrayToNc(setNames(list(data_array, lons, lats), c('var', lon_var_name, lat_var_name)), tmp_file)
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}
sellonlatbox <- ''
if (crop) {
sellonlatbox <- paste0('sellonlatbox,', format(lon_extremes[1], scientific = FALSE),
',', format(lon_extremes[2], scientific = FALSE),
',', format(lat_extremes[1], scientific = FALSE),
',', format(lat_extremes[2], scientific = FALSE), ' -')
}
err <- try({
system(paste0("cdo -s ", sellonlatbox, "remap", method, ",", grid, " ", tmp_file, " ", tmp_file2))
})
file.remove(tmp_file)
if (('try-error' %in% class(err)) || err > 0) {
stop("CDO remap failed.")
}
ncdf_remapped <- nc_open(tmp_file2)
if (!lons_lats_taken) {
found_dim_names <- sapply(ncdf_remapped$var$var$dim, '[[', 'name')
found_lon_dim <- found_dim_names[which(found_dim_names %in% .KnownLonNames())[1]]
found_lat_dim <- found_dim_names[which(found_dim_names %in% .KnownLatNames())[1]]
found_lon_dim_size <- length(ncdf_remapped$dim[[found_lon_dim]]$vals)
found_lat_dim_size <- length(ncdf_remapped$dim[[found_lat_dim]]$vals)
found_var_names <- names(ncdf_remapped$var)
found_lon_var_name <- which(found_var_names %in% .KnownLonNames())
found_lat_var_name <- which(found_var_names %in% .KnownLatNames())
if (length(found_lon_var_name) > 0) {
found_lon_var_name <- found_var_names[found_lon_var_name[1]]
} else {
found_lon_var_name <- NULL
}
if (length(found_lat_var_name) > 0) {
found_lat_var_name <- found_var_names[found_lat_var_name[1]]
} else {
found_lat_var_name <- NULL
}
if (length(found_lon_var_name) > 0) {
found_lons <- ncvar_get(ncdf_remapped, found_lon_var_name,
collapse_degen = FALSE)
} else {
found_lons <- ncdf_remapped$dim[[found_lon_dim]]$vals
dim(found_lons) <- found_lon_dim_size
}
if (length(found_lat_var_name) > 0) {
found_lats <- ncvar_get(ncdf_remapped, found_lat_var_name,
collapse_degen = FALSE)
} else {
found_lats <- ncdf_remapped$dim[[found_lat_dim]]$vals
dim(found_lats) <- found_lat_dim_size
}
if (length(dim(lons)) == length(dim(found_lons))) {
new_lon_name <- lon_dim
} else {
new_lon_name <- found_lon_dim
}
if (length(dim(lats)) == length(dim(found_lats))) {
new_lat_name <- lat_dim
} else {
new_lat_name <- found_lat_dim
}
if (length(dim(found_lons)) > 1) {
if (which(sapply(ncdf_remapped$var$lon$dim, '[[', 'name') == found_lon_dim) <
which(sapply(ncdf_remapped$var$lon$dim, '[[', 'name') == found_lat_dim)) {
names(dim(found_lons)) <- c(new_lon_name, new_lat_name)
} else {
names(dim(found_lons)) <- c(new_lat_name, new_lon_name)
}
} else {
names(dim(found_lons)) <- new_lon_name
}
if (length(dim(found_lats)) > 1) {
if (which(sapply(ncdf_remapped$var$lat$dim, '[[', 'name') == found_lon_dim) <
which(sapply(ncdf_remapped$var$lat$dim, '[[', 'name') == found_lat_dim)) {
names(dim(found_lats)) <- c(new_lon_name, new_lat_name)
} else {
names(dim(found_lats)) <- c(new_lat_name, new_lon_name)
}
} else {
names(dim(found_lats)) <- new_lat_name
}
lons_lats_taken <- TRUE
}
if (!is.null(dims_to_iterate)) {
if (is.null(result_array)) {
if (return_array) {
new_dims <- dim(data_array)
new_dims[c(lon_dim, lat_dim)] <- c(found_lon_dim_size, found_lat_dim_size)
lon_pos <- which(names(new_dims) == lon_dim)
lat_pos <- which(names(new_dims) == lat_dim)
# Fix issue 259, expected order from CDO output is lon lat
# If is irregular, lat and lon position need to be checked:
if (is_irregular) {
if (lon_pos > lat_pos) {
new_pos <- 1:length(new_dims)
new_pos[lon_pos] <- lat_pos
new_pos[lat_pos] <- lon_pos
new_dims <- new_dims[new_pos]
}
}
result_array <- array(dim = new_dims)
store_indices <- as.list(rep(TRUE, length(dim(result_array))))
}
}
if (return_array) {
store_indices[dims_to_iterate] <- as.list(slice_indices)
# If is irregular, the order of dimenesions in result_array and file may be different and need to be checked before reading the temporal file:
if (is_irregular) {
test_dims <- dim(ncvar_get(ncdf_remapped, 'var',
collapse_degen = FALSE))
test_dims <- test_dims[which(test_dims > 1)]
pos_test_dims <- match(dim(result_array), test_dims)
if (is.unsorted(pos_test_dims, na.rm = TRUE)) {
# pos_new_dims is used later in the code. Don't overwrite
pos_new_dims <- 1:length(dim(result_array))
pos_new_dims[which(!is.na(pos_test_dims))] <-
match(test_dims, dim(result_array))
backup_result_array_dims <- dim(result_array)
dim(result_array) <- dim(result_array)[pos_new_dims]
}
}
result_array <- do.call('[<-', c(list(x = result_array), store_indices,
list(value = ncvar_get(ncdf_remapped, 'var', collapse_degen = FALSE))))
}
} else {
new_dims <- dim(data_array)
new_dims[c(lon_dim, lat_dim)] <- c(found_lon_dim_size, found_lat_dim_size)
if (is_irregular) {
lon_pos <- which(names(new_dims) == lon_dim)
lat_pos <- which(names(new_dims) == lat_dim)
if (lon_pos > lat_pos) {
new_pos <- 1:length(new_dims)
new_pos[lon_pos] <- lat_pos
new_pos[lat_pos] <- lon_pos
new_dims <- new_dims[new_pos]
}
}
result_array <- ncvar_get(ncdf_remapped, 'var', collapse_degen = FALSE)
dim(result_array) <- new_dims
}
nc_close(ncdf_remapped)
file.remove(tmp_file2)
}
# If is irregular, the order of dimension may need to be recovered after reading all the file:
if (is_irregular & (!is.null(dims_to_iterate))) {
if (exists('pos_new_dims')) {
pos_new_dims <- 1:length(dim(result_array))
dims_to_change <- match(backup_result_array_dims, dim(result_array))
pos_new_dims[which(dims_to_change != 1)] <-
dims_to_change[which(dims_to_change != 1)]
result_array <- .aperm2(result_array, pos_new_dims)
}
}
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if (!is.null(permutation)) {
dim_backup <- dim(result_array)
result_array <- aperm(result_array, permutation_back)
dim(result_array) <- dim_backup[permutation_back]
}
# Now restore the metadata
result_is_irregular <- FALSE
if (length(dim(found_lats)) > 1 && length(dim(found_lons)) > 1) {
result_is_irregular <- TRUE
}
attribute_backup[['dim']][which(names(dim(result_array)) == lon_dim)] <- dim(result_array)[lon_dim]
attribute_backup[['dim']][which(names(dim(result_array)) == lat_dim)] <- dim(result_array)[lat_dim]
names(attribute_backup[['dim']])[which(names(dim(result_array)) == lon_dim)] <- new_lon_name
names(attribute_backup[['dim']])[which(names(dim(result_array)) == lat_dim)] <- new_lat_name
if (!is.null(attribute_backup[['variables']]) && (length(attribute_backup[['variables']]) > 0)) {
for (var in 1:length(attribute_backup[['variables']])) {
if (length(attribute_backup[['variables']][[var]][['dim']]) > 0) {
for (dim in 1:length(attribute_backup[['variables']][[var]][['dim']])) {
dim_name <- NULL
if ('name' %in% names(attribute_backup[['variables']][[var]][['dim']][[dim]])) {
dim_name <- attribute_backup[['variables']][[var]][['dim']][[dim]][['name']]
if (dim_name %in% c(lon_dim, lat_dim)) {
if (dim_name == lon_dim) {
attribute_backup[['variables']][[var]][['dim']][[dim]][['name']] <- new_lon_name
} else {
attribute_backup[['variables']][[var]][['dim']][[dim]][['name']] <- new_lat_name
}
}
} else if (!is.null(names(attribute_backup[['variables']][[var]][['dim']]))) {
dim_name <- names(attribute_backup[['variables']][[var]][['dim']])[dim]
if (dim_name %in% c(lon_dim, lat_dim)) {
if (dim_name == lon_dim) {
names(attribute_backup[['variables']][[var]][['dim']])[which(names(attribute_backup[['variables']][[var]][['dim']]) == lon_dim)] <- new_lon_name
} else {
names(attribute_backup[['variables']][[var]][['dim']])[which(names(attribute_backup[['variables']][[var]][['dim']]) == lat_dim)] <- new_lat_name
}
}
}
if (!is.null(dim_name)) {
if (dim_name %in% c(lon_dim, lat_dim)) {
if (dim_name == lon_dim) {
new_vals <- found_lons[TRUE]
} else if (dim_name == lat_dim) {
new_vals <- found_lats[TRUE]
}
if (!is.null(attribute_backup[['variables']][[var]][['dim']][[dim]][['len']])) {
attribute_backup[['variables']][[var]][['dim']][[dim]][['len']] <- length(new_vals)
}
if (!is.null(attribute_backup[['variables']][[var]][['dim']][[dim]][['vals']])) {
if (!result_is_irregular) {
attribute_backup[['variables']][[var]][['dim']][[dim]][['vals']] <- new_vals
} else {
attribute_backup[['variables']][[var]][['dim']][[dim]][['vals']] <- 1:length(new_vals)
}
}
}
}
}
}
if (!is_irregular && result_is_irregular) {
attribute_backup[['coordinates']] <- paste(lon_var_name, lat_var_name)
} else if (is_irregular && !result_is_irregular) {
attribute_backup[['coordinates']] <- NULL
}
}
}
attributes(result_array) <- attribute_backup
lons_attr_bk[['dim']] <- dim(found_lons)
if (!is.null(lons_attr_bk[['variables']]) && (length(lons_attr_bk[['variables']]) > 0)) {
for (var in 1:length(lons_attr_bk[['variables']])) {
if (length(lons_attr_bk[['variables']][[var]][['dim']]) > 0) {
dims_to_remove <- NULL
for (dim in 1:length(lons_attr_bk[['variables']][[var]][['dim']])) {
dim_name <- NULL
if ('name' %in% names(lons_attr_bk[['variables']][[var]][['dim']][[dim]])) {
dim_name <- lons_attr_bk[['variables']][[var]][['dim']][[dim]][['name']]