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#'Interpolates arrays with longitude and latitude dimensions using CDO
#'
#'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. \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). 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:
#'\itemize{
#' \item\code{$data_array} {The interpolated data array (if an input array is provided at all, NULL otherwise).}
#' \item\code{$lons} {The longitudes of the data on the destination grid.}
#' \item\code{$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')
#'}
#'@export
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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'
} else {
array_dims <- dim(lons)
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)
Nicolau Manubens
committed
data_array <- array(as.numeric(NA), array_dims)
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}
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'.")
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}
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) {
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')
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
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} 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 {
tmp_lat <- Subset(lats, lon_dim, min_pos[which(names(dim(lats)) == lon_dim)], drop = 'selected')
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
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} 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 (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'
}
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# 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)
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}
}
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 ArrayToNetCDF create this dim as unlimited.
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
Nicolau Manubens
committed
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
}
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')
# Make sure subset goes along with metadata
ArrayToNetCDF(setNames(list(subset, lons, lats), c('var', lon_var_name, lat_var_name)), tmp_file)
} else {
ArrayToNetCDF(setNames(list(data_array, lons, lats), c('var', lon_var_name, lat_var_name)), tmp_file)
}
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)
Nicolau Manubens
committed
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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]]$vals
dim(found_lats) <- found_lat_dim_size
}
if (length(dim(lons)) == length(dim(found_lons))) {
new_lon_name <- lon_dim
if (length(dim(lats)) == length(dim(found_lats))) {
new_lat_name <- lat_dim
if (length(dim(found_lons)) > 1) {
if (which(sapply(ncdf_remapped$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)
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)
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)
result_array <- ncvar_get(ncdf_remapped, 'var', collapse_degen = FALSE)
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}
nc_close(ncdf_remapped)
file.remove(tmp_file2)
}
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']]
if (dim_name %in% c(lon_dim, lat_dim)) {
if (dim_name == lon_dim) {
lons_attr_bk[['variables']][[var]][['dim']][[dim]][['name']] <- new_lon_name
} else {
lons_attr_bk[['variables']][[var]][['dim']][[dim]][['name']] <- new_lat_name
}
}
} else if (!is.null(names(lons_attr_bk[['variables']][[var]][['dim']]))) {
dim_name <- names(lons_attr_bk[['variables']][[var]][['dim']])[dim]
if (dim_name %in% c(lon_dim, lat_dim)) {
if (dim_name == lon_dim) {
names(lons_attr_bk[['variables']][[var]][['dim']])[which(names(lons_attr_bk[['variables']][[var]][['dim']]) == lon_dim)] <- new_lon_name
} else {
names(lons_attr_bk[['variables']][[var]][['dim']])[which(names(lons_attr_bk[['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 (!result_is_irregular) {
dims_to_remove <- c(dims_to_remove, dim)
}
}
if (!is.null(lons_attr_bk[['variables']][[var]][['dim']][[dim]][['len']])) {
lons_attr_bk[['variables']][[var]][['dim']][[dim]][['len']] <- length(new_vals)
}
if (!is.null(lons_attr_bk[['variables']][[var]][['dim']][[dim]][['vals']])) {
if (!result_is_irregular) {
lons_attr_bk[['variables']][[var]][['dim']][[dim]][['vals']] <- new_vals
} else {
lons_attr_bk[['variables']][[var]][['dim']][[dim]][['vals']] <- 1:length(new_vals)
}
}
}
}
}
if (length(dims_to_remove) > 1) {
lons_attr_bk[['variables']][[var]][['dim']] <- lons_attr_bk[['variables']][[var]][['dim']][[-dims_to_remove]]
}
}
}
names(lons_attr_bk[['variables']])[1] <- lon_var_name
lons_attr_bk[['variables']][[1]][['units']] <- 'degrees_east'
}
attributes(found_lons) <- lons_attr_bk
lats_attr_bk[['dim']] <- dim(found_lats)
if (!is.null(lats_attr_bk[['variables']]) && (length(lats_attr_bk[['variables']]) > 0)) {
for (var in 1:length(lats_attr_bk[['variables']])) {
if (length(lats_attr_bk[['variables']][[var]][['dim']]) > 0) {
dims_to_remove <- NULL
for (dim in 1:length(lats_attr_bk[['variables']][[var]][['dim']])) {
dim_name <- NULL
if ('name' %in% names(lats_attr_bk[['variables']][[var]][['dim']][[dim]])) {
dim_name <- lats_attr_bk[['variables']][[var]][['dim']][[dim]][['name']]
if (dim_name %in% c(lon_dim, lat_dim)) {
if (dim_name == lon_dim) {
lons_attr_bk[['variables']][[var]][['dim']][[dim]][['name']] <- new_lon_name
} else {
lons_attr_bk[['variables']][[var]][['dim']][[dim]][['name']] <- new_lat_name
}
}
} else if (!is.null(names(lats_attr_bk[['variables']][[var]][['dim']]))) {
dim_name <- names(lats_attr_bk[['variables']][[var]][['dim']])[dim]
if (dim_name %in% c(lon_dim, lat_dim)) {
if (dim_name == lon_dim) {
names(lats_attr_bk[['variables']][[var]][['dim']])[which(names(lats_attr_bk[['variables']][[var]][['dim']]) == lon_dim)] <- new_lon_name
} else {
names(lats_attr_bk[['variables']][[var]][['dim']])[which(names(lats_attr_bk[['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]
if (!result_is_irregular) {
dims_to_remove <- c(dims_to_remove, dim)
}
} else if (dim_name == lat_dim) {
new_vals <- found_lats[TRUE]
}
if (!is.null(lats_attr_bk[['variables']][[var]][['dim']][[dim]][['len']])) {
lats_attr_bk[['variables']][[var]][['dim']][[dim]][['len']] <- length(new_vals)
}
if (!is.null(lats_attr_bk[['variables']][[var]][['dim']][[dim]][['vals']])) {
if (!result_is_irregular) {
lats_attr_bk[['variables']][[var]][['dim']][[dim]][['vals']] <- new_vals
} else {
lats_attr_bk[['variables']][[var]][['dim']][[dim]][['vals']] <- 1:length(new_vals)
}
}
}
}
}
if (length(dims_to_remove) > 1) {
lats_attr_bk[['variables']][[var]][['dim']] <- lats_attr_bk[['variables']][[var]][['dim']][[-dims_to_remove]]
}
}
}
names(lats_attr_bk[['variables']])[1] <- lat_var_name
lats_attr_bk[['variables']][[1]][['units']] <- 'degrees_north'
}
attributes(found_lats) <- lats_attr_bk
}
list(data_array = if (return_array) {
if (interpolation_needed) {
result_array
} else {
data_array
}
} else {
NULL
},
lons = found_lons, lats = found_lats)
}