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## Function to tell if a regexpr() match is a complete match to a specified name
.IsFullMatch <- function(x, name) {
ifelse(x > 0 && attributes(x)$match.length == nchar(name), TRUE, FALSE)
}
.ConfigReplaceVariablesInString <- function(string, replace_values, allow_undefined_key_vars = FALSE) {
# This function replaces all the occurrences of a variable in a string by
# their corresponding string stored in the replace_values.
if (length(strsplit(string, "\\$")[[1]]) > 1) {
parts <- strsplit(string, "\\$")[[1]]
output <- ""
i <- 0
for (part in parts) {
if (i %% 2 == 0) {
output <- paste(output, part, sep = "")
} else {
if (part %in% names(replace_values)) {
output <- paste(output, .ConfigReplaceVariablesInString(replace_values[[part]], replace_values, allow_undefined_key_vars), sep = "")
} else if (allow_undefined_key_vars) {
output <- paste0(output, "$", part, "$")
} else {
stop(paste('Error: The variable $', part, '$ was not defined in the configuration file.', sep = ''))
}
}
i <- i + 1
}
output
} else {
string
}
}
.KnownLonNames <- function() {
known_lon_names <- c('lon', 'longitude', 'x', 'i', 'nav_lon')
}
.KnownLatNames <- function() {
known_lat_names <- c('lat', 'latitude', 'y', 'j', 'nav_lat')
}
.t2nlatlon <- function(t) {
## As seen in cdo's griddes.c: ntr2nlat()
nlats <- (t * 3 + 1) / 2
if ((nlats > 0) && (nlats - trunc(nlats) >= 0.5)) {
nlats <- ceiling(nlats)
} else {
nlats <- round(nlats)
}
if (nlats %% 2 > 0) {
nlats <- nlats + 1
}
## As seen in cdo's griddes.c: compNlon(), and as specified in ECMWF
nlons <- 2 * nlats
keep_going <- TRUE
while (keep_going) {
n <- nlons
if (n %% 8 == 0) n <- trunc(n / 8)
while (n %% 6 == 0) n <- trunc(n / 6)
while (n %% 5 == 0) n <- trunc(n / 5)
while (n %% 4 == 0) n <- trunc(n / 4)
while (n %% 3 == 0) n <- trunc(n / 3)
if (n %% 2 == 0) n <- trunc(n / 2)
if (n <= 8) {
keep_going <- FALSE
} else {
nlons <- nlons + 2
if (nlons > 9999) {
stop("Error: pick another gaussian grid truncation. It doesn't fulfill the standards to apply FFT.")
}
}
}
c(nlats, nlons)
}
.nlat2t <- function(nlats) {
trunc((nlats * 2 - 1) / 3)
}
.LoadDataFile <- function(work_piece, explore_dims = FALSE, silent = FALSE) {
# The purpose, working modes, inputs and outputs of this function are
# explained in ?LoadDataFile
#suppressPackageStartupMessages({library(ncdf4)})
#suppressPackageStartupMessages({library(bigmemory)})
#suppressPackageStartupMessages({library(plyr)})
# Auxiliar function to convert array indices to lineal indices
arrayIndex2VectorIndex <- function(indices, dims) {
if (length(indices) > length(dims)) {
stop("Error: indices do not match dimensions in arrayIndex2VectorIndex.")
}
position <- 1
dims <- rev(dims)
indices <- rev(indices)
for (i in 1:length(indices)) {
position <- position + (indices[i] - 1) * prod(dims[-c(1:i)])
}
position
}
.t2nlatlon <- function(t) {
## As seen in cdo's griddes.c: ntr2nlat()
nlats <- (t * 3 + 1) / 2
if ((nlats > 0) && (nlats - trunc(nlats) >= 0.5)) {
nlats <- ceiling(nlats)
} else {
nlats <- round(nlats)
}
if (nlats %% 2 > 0) {
nlats <- nlats + 1
}
## As seen in cdo's griddes.c: compNlon(), and as specified in ECMWF
nlons <- 2 * nlats
keep_going <- TRUE
while (keep_going) {
n <- nlons
if (n %% 8 == 0) n <- trunc(n / 8)
while (n %% 6 == 0) n <- trunc(n / 6)
while (n %% 5 == 0) n <- trunc(n / 5)
while (n %% 4 == 0) n <- trunc(n / 4)
while (n %% 3 == 0) n <- trunc(n / 3)
if (n %% 2 == 0) n <- trunc(n / 2)
if (n <= 8) {
keep_going <- FALSE
} else {
nlons <- nlons + 2
if (nlons > 9999) {
stop("Error: pick another gaussian grid truncation. It doesn't fulfill the standards to apply FFT.")
}
}
}
c(nlats, nlons)
}
.nlat2t <- function(nlats) {
trunc((nlats * 2 - 1) / 3)
}
found_file <- NULL
dims <- NULL
grid_name <- units <- var_long_name <- NULL
is_2d_var <- array_across_gw <- NULL
data_across_gw <- NULL
filename <- work_piece[['filename']]
namevar <- work_piece[['namevar']]
output <- work_piece[['output']]
# The names of all data files in the directory of the repository that match
# the pattern are obtained.
if (length(grep("^http", filename)) > 0) {
is_url <- TRUE
files <- filename
## TODO: Check that the user is not using shell globbing exps.
} else {
is_url <- FALSE
files <- Sys.glob(filename)
}
# If we don't find any, we leave the flag 'found_file' with a NULL value.
if (length(files) > 0) {
# The first file that matches the pattern is chosen and read.
filename <- head(files, 1)
filein <- filename
found_file <- filename
mask <- work_piece[['mask']]
if (!silent) {
if (explore_dims) {
.message(paste("Exploring dimensions...", filename))
}
##} else {
## cat(paste("* Reading & processing data...", filename, '\n'))
##}
}
# We will fill in 'expected_dims' with the names of the expected dimensions of
# the data array we'll retrieve from the file.
expected_dims <- NULL
remap_needed <- FALSE
# But first we open the file and work out whether the requested variable is 2d
fnc <- nc_open(filein)
if (!(namevar %in% names(fnc$var))) {
stop(paste("Error: The variable", namevar, "is not defined in the file", filename))
}
var_long_name <- fnc$var[[namevar]]$longname
units <- fnc$var[[namevar]]$units
file_dimnames <- unlist(lapply(fnc$var[[namevar]][['dim']], '[[', 'name'))
# The following two 'ifs' are to allow for 'lon'/'lat' by default, instead of
# 'longitude'/'latitude'.
if (!(work_piece[['dimnames']][['lon']] %in% file_dimnames) &&
(work_piece[['dimnames']][['lon']] == 'longitude') &&
('lon' %in% file_dimnames)) {
work_piece[['dimnames']][['lon']] <- 'lon'
}
if (!(work_piece[['dimnames']][['lat']] %in% file_dimnames) &&
(work_piece[['dimnames']][['lat']] == 'latitude') &&
('lat' %in% file_dimnames)) {
work_piece[['dimnames']][['lat']] <- 'lat'
}
if (is.null(work_piece[['is_2d_var']])) {
is_2d_var <- all(c(work_piece[['dimnames']][['lon']],
work_piece[['dimnames']][['lat']]) %in%
unlist(lapply(fnc$var[[namevar]][['dim']],
'[[', 'name')))
} else {
is_2d_var <- work_piece[['is_2d_var']]
}
if ((is_2d_var || work_piece[['is_file_per_dataset']])) {
if (Sys.which("cdo")[[1]] == "") {
stop("Error: CDO libraries not available")
}
cdo_version <- strsplit(suppressWarnings(system2("cdo", args = '-V', stderr = TRUE))[[1]], ' ')[[1]][5]
cdo_version <- as.numeric_version(unlist(strsplit(cdo_version, "[A-Za-z]", fixed = FALSE))[[1]])
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}
# If the variable to load is 2-d, we need to determine whether:
# - interpolation is needed
# - subsetting is requested
if (is_2d_var) {
## We read the longitudes and latitudes from the file.
lon <- ncvar_get(fnc, work_piece[['dimnames']][['lon']])
lat <- ncvar_get(fnc, work_piece[['dimnames']][['lat']])
first_lon_in_original_file <- lon[1]
# If a common grid is requested or we are exploring the file dimensions
# we need to read the grid type and size of the file to finally work out the
# CDO grid name.
if (!is.null(work_piece[['grid']]) || explore_dims) {
# Here we read the grid type and its number of longitudes and latitudes
file_info <- system(paste('cdo -s griddes', filein, '2> /dev/null'), intern = TRUE)
grids_positions <- grep('# gridID', file_info)
if (length(grids_positions) < 1) {
stop("The grid should be defined in the files.")
}
grids_first_lines <- grids_positions + 2
grids_last_lines <- c((grids_positions - 2)[-1], length(file_info))
grids_info <- as.list(1:length(grids_positions))
grids_info <- lapply(grids_info, function (x) file_info[grids_first_lines[x]:grids_last_lines[x]])
grids_info <- lapply(grids_info, function (x) gsub(" *", " ", x))
grids_info <- lapply(grids_info, function (x) gsub("^ | $", "", x))
grids_info <- lapply(grids_info, function (x) unlist(strsplit(x, " | = ")))
grids_types <- unlist(lapply(grids_info, function (x) x[grep('gridtype', x) + 1]))
grids_matches <- unlist(lapply(grids_info, function (x) {
nlons <- if (length(grep('xsize', x)) > 0) {
as.numeric(x[grep('xsize', x) + 1])
} else {
NA
}
nlats <- if (length(grep('ysize', x)) > 0) {
as.numeric(x[grep('ysize', x) + 1])
} else {
NA
}
result <- FALSE
if (!any(is.na(c(nlons, nlats)))) {
if ((nlons == length(lon)) &&
(nlats == length(lat))) {
result <- TRUE
}
}
result
}))
grids_matches <- grids_matches[which(grids_types %in% c('gaussian', 'lonlat'))]
grids_info <- grids_info[which(grids_types %in% c('gaussian', 'lonlat'))]
grids_types <- grids_types[which(grids_types %in% c('gaussian', 'lonlat'))]
if (length(grids_matches) == 0) {
stop("Error: Only 'gaussian' and 'lonlat' grids supported. See e.g: cdo sinfo ", filename)
}
if (sum(grids_matches) > 1) {
if ((all(grids_types[which(grids_matches)] == 'gaussian') ||
all(grids_types[which(grids_matches)] == 'lonlat')) &&
all(unlist(lapply(grids_info[which(grids_matches)], identical,
grids_info[which(grids_matches)][[1]])))) {
grid_type <- grids_types[which(grids_matches)][1]
} else {
stop("Error: Load() can't disambiguate: More than one lonlat/gaussian grids with the same size as the requested variable defined in ", filename)
}
} else if (sum(grids_matches) == 1) {
grid_type <- grids_types[which(grids_matches)]
} else {
stop("Unexpected error.")
}
grid_lons <- length(lon)
grid_lats <- length(lat)
# Convert to CDO grid name as seen in cdo's griddes.c: nlat2ntr()
if (grid_type == 'lonlat') {
grid_name <- paste0('r', grid_lons, 'x', grid_lats)
} else {
grid_name <- paste0('t', .nlat2t(grid_lats), 'grid')
}
}
# If a common grid is requested, we will also calculate its size which we will use
# later on.
if (!is.null(work_piece[['grid']])) {
# Now we calculate the common grid type and its lons and lats
if (length(grep('^t\\d{1,+}grid$', work_piece[['grid']])) > 0) {
common_grid_type <- 'gaussian'
common_grid_res <- as.numeric(strsplit(work_piece[['grid']], '[^0-9]{1,+}')[[1]][2])
nlonlat <- .t2nlatlon(common_grid_res)
common_grid_lats <- nlonlat[1]
common_grid_lons <- nlonlat[2]
} else if (length(grep('^r\\d{1,+}x\\d{1,+}$', work_piece[['grid']])) > 0) {
common_grid_type <- 'lonlat'
common_grid_lons <- as.numeric(strsplit(work_piece[['grid']], '[^0-9]{1,+}')[[1]][2])
common_grid_lats <- as.numeric(strsplit(work_piece[['grid']], '[^0-9]{1,+}')[[1]][3])
} else {
stop("Error: Only supported grid types in parameter 'grid' are t<RES>grid and r<NX>x<NY>")
}
} else {
## If no 'grid' is specified, there is no common grid.
## But these variables are filled in for consistency in the code.
common_grid_lons <- length(lon)
common_grid_lats <- length(lat)
}
first_common_grid_lon <- 0
last_common_grid_lon <- 360 - 360/common_grid_lons
## This is not true for gaussian grids or for some regular grids, but
## is a safe estimation
first_common_grid_lat <- -90
last_common_grid_lat <- 90
# And finally determine whether interpolation is needed or not
remove_shift <- FALSE
if (!is.null(work_piece[['grid']])) {
if ((grid_lons != common_grid_lons) ||
(grid_lats != common_grid_lats) ||
(grid_type != common_grid_type) ||
(lon[1] != first_common_grid_lon)) {
if (grid_lons == common_grid_lons && grid_lats == common_grid_lats &&
grid_type == common_grid_type && lon[1] != first_common_grid_lon) {
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remove_shift <- TRUE
}
remap_needed <- TRUE
common_grid_name <- work_piece[['grid']]
}
} else if ((lon[1] != first_common_grid_lon) && explore_dims &&
!work_piece[['single_dataset']]) {
remap_needed <- TRUE
common_grid_name <- grid_name
remove_shift <- TRUE
}
if (remap_needed && (work_piece[['remap']] == 'con') &&
(cdo_version >= as.numeric_version('1.7.0'))) {
work_piece[['remap']] <- 'ycon'
}
if (remove_shift && !explore_dims) {
if (!is.null(work_piece[['progress_amount']])) {
cat("\n")
}
cat(paste0("! Warning: the dataset with index ",
tail(work_piece[['indices']], 1), " in '",
work_piece[['dataset_type']], "' doesn't start at longitude 0 and will be re-interpolated in order to align its longitudes with the standard CDO grids definable with the names 't<RES>grid' or 'r<NX>x<NY>', which are by definition starting at the longitude 0.\n"))
if (!is.null(mask)) {
cat(paste0("! Warning: a mask was provided for the dataset with index ",
tail(work_piece[['indices']], 1), " in '",
work_piece[['dataset_type']], "'. This dataset has been re-interpolated to align its longitudes to start at 0. You must re-interpolate the corresponding mask to align its longitudes to start at 0 as well, if you haven't done so yet. Running cdo remapcon,", common_grid_name, " original_mask_file.nc new_mask_file.nc will fix it.\n"))
}
}
if (remap_needed && (grid_lons < common_grid_lons || grid_lats < common_grid_lats)) {
if (!is.null(work_piece[['progress_amount']])) {
cat("\n")
}
if (!explore_dims) {
cat(paste0("! Warning: the dataset with index ", tail(work_piece[['indices']], 1),
" in '", work_piece[['dataset_type']], "' is originally on ",
"a grid coarser than the common grid and it has been ",
"extrapolated. Check the results carefully. It is ",
"recommended to specify as common grid the coarsest grid ",
"among all requested datasets via the parameter 'grid'.\n"))
}
}
# Now calculate if the user requests for a lonlat subset or for the
# entire field
lonmin <- work_piece[['lon_limits']][1]
lonmax <- work_piece[['lon_limits']][2]
latmin <- work_piece[['lat_limits']][1]
latmax <- work_piece[['lat_limits']][2]
lon_subsetting_requested <- FALSE
lonlat_subsetting_requested <- FALSE
if (lonmin <= lonmax) {
if ((lonmin > first_common_grid_lon) || (lonmax < last_common_grid_lon)) {
lon_subsetting_requested <- TRUE
}
} else {
if ((lonmin - lonmax) > 360/common_grid_lons) {
lon_subsetting_requested <- TRUE
} else {
gap_width <- floor(lonmin / (360/common_grid_lons)) -
floor(lonmax / (360/common_grid_lons))
if (gap_width > 0) {
if (!(gap_width == 1 && (lonmin %% (360/common_grid_lons) == 0) &&
(lonmax %% (360/common_grid_lons) == 0))) {
lon_subsetting_requested <- TRUE
}
}
}
}
if ((latmin > first_common_grid_lat) || (latmax < last_common_grid_lat)
|| (lon_subsetting_requested)) {
lonlat_subsetting_requested <- TRUE
}
# Now that we know if subsetting was requested, we can say if final data
# will go across greenwich
if (lonmax < lonmin) {
data_across_gw <- TRUE
} else {
data_across_gw <- !lon_subsetting_requested
}
# When remap is needed but no subsetting, the file is copied locally
# so that cdo works faster, and then interpolated.
# Otherwise the file is kept as is and the subset will have to be
# interpolated still.
if (!lonlat_subsetting_requested && remap_needed) {
nc_close(fnc)
filecopy <- tempfile(pattern = "load", fileext = ".nc")
file.copy(filein, filecopy)
filein <- tempfile(pattern = "loadRegridded", fileext = ".nc")
# "-L" is to serialize I/O accesses. It prevents potential segmentation fault in the
# underlying hdf5 library.
system(paste0("cdo -L -s remap", work_piece[['remap']], ",",
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common_grid_name,
" -selname,", namevar, " ", filecopy, " ", filein,
" 2>/dev/null", sep = ""))
file.remove(filecopy)
work_piece[['dimnames']][['lon']] <- 'lon'
work_piece[['dimnames']][['lat']] <- 'lat'
fnc <- nc_open(filein)
lon <- ncvar_get(fnc, work_piece[['dimnames']][['lon']])
lat <- ncvar_get(fnc, work_piece[['dimnames']][['lat']])
}
# Read and check also the mask
if (!is.null(mask)) {
###mask_file <- tempfile(pattern = 'loadMask', fileext = '.nc')
if (is.list(mask)) {
if (!file.exists(mask[['path']])) {
stop(paste("Error: Couldn't find the mask file", mask[['path']]))
}
mask_file <- mask[['path']]
###file.copy(work_piece[['mask']][['path']], mask_file)
fnc_mask <- nc_open(mask_file)
vars_in_mask <- sapply(fnc_mask$var, '[[', 'name')
if ('nc_var_name' %in% names(mask)) {
if (!(mask[['nc_var_name']] %in%
vars_in_mask)) {
stop(paste("Error: couldn't find variable", mask[['nc_var_name']],
"in the mask file", mask[['path']]))
}
} else {
if (length(vars_in_mask) != 1) {
stop(paste("Error: one and only one non-coordinate variable should be defined in the mask file",
mask[['path']], "if the component 'nc_var_name' is not specified. Currently found: ",
paste(vars_in_mask, collapse = ', '), "."))
} else {
mask[['nc_var_name']] <- vars_in_mask
}
}
if (sum(fnc_mask$var[[mask[['nc_var_name']]]]$size > 1) != 2) {
stop(paste0("Error: the variable '",
mask[['nc_var_name']],
"' must be defined only over the dimensions '",
work_piece[['dimnames']][['lon']], "' and '",
work_piece[['dimnames']][['lat']],
"' in the mask file ",
mask[['path']]))
}
mask <- ncvar_get(fnc_mask, mask[['nc_var_name']], collapse_degen = TRUE)
nc_close(fnc_mask)
### mask_lon <- ncvar_get(fnc_mask, work_piece[['dimnames']][['lon']])
### mask_lat <- ncvar_get(fnc_mask, work_piece[['dimnames']][['lat']])
###} else {
### dim_longitudes <- ncdim_def(work_piece[['dimnames']][['lon']], "degrees_east", lon)
### dim_latitudes <- ncdim_def(work_piece[['dimnames']][['lat']], "degrees_north", lat)
### ncdf_var <- ncvar_def('LSM', "", list(dim_longitudes, dim_latitudes), NA, 'double')
### fnc_mask <- nc_create(mask_file, list(ncdf_var))
### ncvar_put(fnc_mask, ncdf_var, work_piece[['mask']])
### nc_close(fnc_mask)
### fnc_mask <- nc_open(mask_file)
### work_piece[['mask']] <- list(path = mask_file, nc_var_name = 'LSM')
### mask_lon <- lon
### mask_lat <- lat
###}
###}
### Now ready to check that the mask is right
##if (!(lonlat_subsetting_requested && remap_needed)) {
### if ((dim(mask)[2] != length(lon)) || (dim(mask)[1] != length(lat))) {
### stop(paste("Error: the mask of the dataset with index ", tail(work_piece[['indices']], 1), " in '", work_piece[['dataset_type']], "' is wrong. It must be on the common grid if the selected output type is 'lonlat', 'lon' or 'lat', or 'areave' and 'grid' has been specified. It must be on the grid of the corresponding dataset if the selected output type is 'areave' and no 'grid' has been specified. For more information check ?Load and see help on parameters 'grid', 'maskmod' and 'maskobs'.", sep = ""))
### }
###if (!(identical(mask_lon, lon) && identical(mask_lat, lat))) {
### stop(paste0("Error: the longitudes and latitudes in the masks must be identical to the ones in the corresponding data files if output = 'areave' or, if the selected output is 'lon', 'lat' or 'lonlat', the longitudes in the mask file must start by 0 and the latitudes must be ordered from highest to lowest. See\n ",
### work_piece[['mask']][['path']], " and ", filein))
###}
}
}
lon_indices <- 1:length(lon)
if (!(lonlat_subsetting_requested && remap_needed)) {
lon[which(lon < 0)] <- lon[which(lon < 0)] + 360
}
if (lonmax >= lonmin) {
lon_indices <- lon_indices[which(((lon %% 360) >= lonmin) & ((lon %% 360) <= lonmax))]
} else if (!remap_needed) {
lon_indices <- lon_indices[which(((lon %% 360) <= lonmax) | ((lon %% 360) >= lonmin))]
}
lat_indices <- which(lat >= latmin & lat <= latmax)
## In most of the cases the latitudes are ordered from -90 to 90.
## We will reorder them to be in the order from 90 to -90, so mostly
## always the latitudes are reordered.
## TODO: This could be avoided in future.
if (lat[1] < lat[length(lat)]) {
lat_indices <- lat_indices[length(lat_indices):1]
}
if (!is.null(mask) && !(lonlat_subsetting_requested && remap_needed)) {
if ((dim(mask)[1] != length(lon)) || (dim(mask)[2] != length(lat))) {
stop(paste("Error: the mask of the dataset with index ", tail(work_piece[['indices']], 1), " in '", work_piece[['dataset_type']], "' is wrong. It must be on the common grid if the selected output type is 'lonlat', 'lon' or 'lat', or 'areave' and 'grid' has been specified. It must be on the grid of the corresponding dataset if the selected output type is 'areave' and no 'grid' has been specified. For more information check ?Load and see help on parameters 'grid', 'maskmod' and 'maskobs'.", sep = ""))
}
mask <- mask[lon_indices, lat_indices]
}
## If the user requests subsetting, we must extend the lon and lat limits if possible
## so that the interpolation after is done properly
maximum_extra_points <- work_piece[['remapcells']]
if (lonlat_subsetting_requested && remap_needed) {
if ((maximum_extra_points > (head(lon_indices, 1) - 1)) ||
(maximum_extra_points > (length(lon) - tail(lon_indices, 1)))) {
## if the requested number of points goes beyond the left or right
## sides of the map, we need to take the entire map so that the
## interpolation works properly
lon_indices <- 1:length(lon)
} else {
extra_points <- min(maximum_extra_points, head(lon_indices, 1) - 1)
if (extra_points > 0) {
lon_indices <- c((head(lon_indices, 1) - extra_points):(head(lon_indices, 1) - 1), lon_indices)
}
extra_points <- min(maximum_extra_points, length(lon) - tail(lon_indices, 1))
if (extra_points > 0) {
lon_indices <- c(lon_indices, (tail(lon_indices, 1) + 1):(tail(lon_indices, 1) + extra_points))
}
}
min_lat_ind <- min(lat_indices)
max_lat_ind <- max(lat_indices)
extra_points <- min(maximum_extra_points, min_lat_ind - 1)
if (extra_points > 0) {
if (lat[1] < tail(lat, 1)) {
lat_indices <- c(lat_indices, (min_lat_ind - 1):(min_lat_ind - extra_points))
} else {
lat_indices <- c((min_lat_ind - extra_points):(min_lat_ind - 1), lat_indices)
}
}
extra_points <- min(maximum_extra_points, length(lat) - max_lat_ind)
if (extra_points > 0) {
if (lat[1] < tail(lat, 1)) {
lat_indices <- c((max_lat_ind + extra_points):(max_lat_ind + 1), lat_indices)
} else {
lat_indices <- c(lat_indices, (max_lat_ind + 1):(max_lat_ind + extra_points))
}
}
}
lon <- lon[lon_indices]
lat <- lat[lat_indices]
expected_dims <- c(work_piece[['dimnames']][['lon']],
work_piece[['dimnames']][['lat']])
} else {
lon <- 0
lat <- 0
}
# We keep on filling the expected dimensions
var_dimnames <- unlist(lapply(fnc$var[[namevar]][['dim']], '[[', 'name'))
nmemb <- nltime <- NULL
## Sometimes CDO renames 'members' dimension to 'lev'
old_members_dimname <- NULL
if (('lev' %in% var_dimnames) && !(work_piece[['dimnames']][['member']] %in% var_dimnames)) {
old_members_dimname <- work_piece[['dimnames']][['member']]
work_piece[['dimnames']][['member']] <- 'lev'
}
if (work_piece[['dimnames']][['member']] %in% var_dimnames) {
nmemb <- fnc$var[[namevar]][['dim']][[match(work_piece[['dimnames']][['member']], var_dimnames)]]$len
expected_dims <- c(expected_dims, work_piece[['dimnames']][['member']])
} else {
nmemb <- 1
}
if (length(expected_dims) > 0) {
dim_matches <- match(expected_dims, var_dimnames)
if (any(is.na(dim_matches))) {
if (!is.null(old_members_dimname)) {
expected_dims[which(expected_dims == 'lev')] <- old_members_dimname
}
stop(paste("Error: the expected dimension(s)",
paste(expected_dims[which(is.na(dim_matches))], collapse = ', '),
"were not found in", filename))
}
time_dimname <- var_dimnames[-dim_matches]
} else {
time_dimname <- var_dimnames
}
if (length(time_dimname) > 0) {
if (length(time_dimname) == 1) {
nltime <- fnc$var[[namevar]][['dim']][[match(time_dimname, var_dimnames)]]$len
expected_dims <- c(expected_dims, time_dimname)
dim_matches <- match(expected_dims, var_dimnames)
} else {
if (!is.null(old_members_dimname)) {
expected_dims[which(expected_dims == 'lev')] <- old_members_dimname
}
stop(paste("Error: the variable", namevar,
"is defined over more dimensions than the expected (",
paste(c(expected_dims, 'time'), collapse = ', '),
"). It could also be that the members, longitude or latitude dimensions are named incorrectly. In that case, either rename the dimensions in the file or adjust Load() to recognize the actual name with the parameter 'dimnames'. See file", filename))
}
} else {
nltime <- 1
}
# Now we must retrieve the data from the file, but only the asked indices.
# So we build up the indices to retrieve.
# Longitudes or latitudes have been retrieved already.
if (explore_dims) {
# If we're exploring the file we only want one time step from one member,
# to regrid it and work out the number of longitudes and latitudes.
# We don't need more.
members <- 1
ltimes_list <- list(c(1))
} else {
# The data is arranged in the array 'tmp' with the dimensions in a
# common order:
# 1) Longitudes
# 2) Latitudes
# 3) Members (even if is not a file per member experiment)
# 4) Lead-times
if (work_piece[['is_file_per_dataset']]) {
time_indices <- 1:nltime
mons <- strsplit(system(paste('cdo showmon ', filein,
' 2>/dev/null'), intern = TRUE), split = ' ')
years <- strsplit(system(paste('cdo showyear ', filein,
' 2>/dev/null'), intern = TRUE), split = ' ')
mons <- as.numeric(mons[[1]][which(mons[[1]] != "")])
years <- as.numeric(years[[1]][which(years[[1]] != "")])
time_indices <- ts(time_indices, start = c(years[1], mons[1]),
end = c(years[length(years)], mons[length(mons)]),
frequency = 12)
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for (sdate in work_piece[['startdates']]) {
selected_time_indices <- window(time_indices, start = c(as.numeric(
substr(sdate, 1, 4)), as.numeric(substr(sdate, 5, 6))),
end = c(3000, 12), frequency = 12, extend = TRUE)
selected_time_indices <- selected_time_indices[work_piece[['leadtimes']]]
ltimes_list <- c(ltimes_list, list(selected_time_indices))
}
} else {
ltimes <- work_piece[['leadtimes']]
#if (work_piece[['dataset_type']] == 'exp') {
ltimes_list <- list(ltimes[which(ltimes <= nltime)])
#}
}
## TODO: Put, when reading matrices, this kind of warnings
# if (nmember < nmemb) {
# cat("Warning:
members <- 1:work_piece[['nmember']]
members <- members[which(members <= nmemb)]
}
# Now, for each list of leadtimes to load (usually only one list with all leadtimes),
# we'll join the indices and retrieve data
found_disordered_dims <- FALSE
for (ltimes in ltimes_list) {
if (is_2d_var) {
start <- c(min(lon_indices), min(lat_indices))
end <- c(max(lon_indices), max(lat_indices))
if (lonlat_subsetting_requested && remap_needed) {
subset_indices <- list(min(lon_indices):max(lon_indices) - min(lon_indices) + 1,
lat_indices - min(lat_indices) + 1)
dim_longitudes <- ncdim_def(work_piece[['dimnames']][['lon']], "degrees_east", lon)
dim_latitudes <- ncdim_def(work_piece[['dimnames']][['lat']], "degrees_north", lat)
ncdf_dims <- list(dim_longitudes, dim_latitudes)
} else {
subset_indices <- list(lon_indices - min(lon_indices) + 1,
lat_indices - min(lat_indices) + 1)
ncdf_dims <- list()
}
final_dims <- c(length(subset_indices[[1]]), length(subset_indices[[2]]), 1, 1)
} else {
start <- end <- c()
subset_indices <- list()
ncdf_dims <- list()
final_dims <- c(1, 1, 1, 1)
}
if (work_piece[['dimnames']][['member']] %in% expected_dims) {
start <- c(start, head(members, 1))
end <- c(end, tail(members, 1))
subset_indices <- c(subset_indices, list(members - head(members, 1) + 1))
dim_members <- ncdim_def(work_piece[['dimnames']][['member']], "", members)
ncdf_dims <- c(ncdf_dims, list(dim_members))
final_dims[3] <- length(members)
}
if (time_dimname %in% expected_dims) {
if (any(!is.na(ltimes))) {
start <- c(start, head(ltimes[which(!is.na(ltimes))], 1))
end <- c(end, tail(ltimes[which(!is.na(ltimes))], 1))
subset_indices <- c(subset_indices, list(ltimes - head(ltimes[which(!is.na(ltimes))], 1) + 1))
} else {
start <- c(start, NA)
end <- c(end, NA)
subset_indices <- c(subset_indices, list(ltimes))
}
dim_time <- ncdim_def(time_dimname, "", 1:length(ltimes), unlim = TRUE)
ncdf_dims <- c(ncdf_dims, list(dim_time))
final_dims[4] <- length(ltimes)
}
count <- end - start + 1
start <- start[dim_matches]
count <- count[dim_matches]
subset_indices <- subset_indices[dim_matches]
# Now that we have the indices to retrieve, we retrieve the data
if (prod(final_dims) > 0) {
tmp <- take(ncvar_get(fnc, namevar, start, count,
collapse_degen = FALSE),
1:length(subset_indices), subset_indices)
# The data is regridded if it corresponds to an atmospheric variable. When
# the chosen output type is 'areave' the data is not regridded to not
# waste computing time unless the user specified a common grid.
if (is_2d_var) {
###if (!is.null(work_piece[['mask']]) && !(lonlat_subsetting_requested && remap_needed)) {
### mask <- take(ncvar_get(fnc_mask, work_piece[['mask']][['nc_var_name']],
### start[dim_matches[1:2]], count[dim_matches[1:2]],
### collapse_degen = FALSE), 1:2, subset_indices[dim_matches[1:2]])
###}
if (lonlat_subsetting_requested && remap_needed) {
filein <- tempfile(pattern = "loadRegridded", fileext = ".nc")
filein2 <- tempfile(pattern = "loadRegridded2", fileext = ".nc")
ncdf_var <- ncvar_def(namevar, "", ncdf_dims[dim_matches],
fnc$var[[namevar]]$missval,
prec = if (fnc$var[[namevar]]$prec == 'int') {
'integer'
} else {
fnc$var[[namevar]]$prec
})
scale_factor <- ifelse(fnc$var[[namevar]]$hasScaleFact, fnc$var[[namevar]]$scaleFact, 1)
add_offset <- ifelse(fnc$var[[namevar]]$hasAddOffset, fnc$var[[namevar]]$addOffset, 0)
if (fnc$var[[namevar]]$hasScaleFact || fnc$var[[namevar]]$hasAddOffset) {
tmp <- (tmp - add_offset) / scale_factor
}
#nc_close(fnc)
fnc2 <- nc_create(filein2, list(ncdf_var))
ncvar_put(fnc2, ncdf_var, tmp)
if (add_offset != 0) {
ncatt_put(fnc2, ncdf_var, 'add_offset', add_offset)
}
if (scale_factor != 1) {
ncatt_put(fnc2, ncdf_var, 'scale_factor', scale_factor)
}
nc_close(fnc2)
system(paste0("cdo -L -s -sellonlatbox,", if (lonmin > lonmax) {
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"0,360,"
} else {
paste0(lonmin, ",", lonmax, ",")
}, latmin, ",", latmax,
" -remap", work_piece[['remap']], ",", common_grid_name,
" ", filein2, " ", filein, " 2>/dev/null", sep = ""))
file.remove(filein2)
fnc2 <- nc_open(filein)
sub_lon <- ncvar_get(fnc2, 'lon')
sub_lat <- ncvar_get(fnc2, 'lat')
## We read the longitudes and latitudes from the file.
## In principle cdo should put in order the longitudes
## and slice them properly unless data is across greenwich
sub_lon[which(sub_lon < 0)] <- sub_lon[which(sub_lon < 0)] + 360
sub_lon_indices <- 1:length(sub_lon)
if (lonmax < lonmin) {
sub_lon_indices <- sub_lon_indices[which((sub_lon <= lonmax) | (sub_lon >= lonmin))]
}
sub_lat_indices <- 1:length(sub_lat)
## In principle cdo should put in order the latitudes
if (sub_lat[1] < sub_lat[length(sub_lat)]) {
sub_lat_indices <- length(sub_lat):1
}
final_dims[c(1, 2)] <- c(length(sub_lon_indices), length(sub_lat_indices))
subset_indices[[dim_matches[1]]] <- sub_lon_indices
subset_indices[[dim_matches[2]]] <- sub_lat_indices
tmp <- take(ncvar_get(fnc2, namevar, collapse_degen = FALSE),
1:length(subset_indices), subset_indices)
if (!is.null(mask)) {
## We create a very simple 2d netcdf file that is then interpolated to the common
## grid to know what are the lons and lats of our slice of data
mask_file <- tempfile(pattern = 'loadMask', fileext = '.nc')
mask_file_remap <- tempfile(pattern = 'loadMask', fileext = '.nc')
dim_longitudes <- ncdim_def(work_piece[['dimnames']][['lon']], "degrees_east", c(0, 360))
dim_latitudes <- ncdim_def(work_piece[['dimnames']][['lat']], "degrees_north", c(-90, 90))
ncdf_var <- ncvar_def('LSM', "", list(dim_longitudes, dim_latitudes), NA, 'double')
fnc_mask <- nc_create(mask_file, list(ncdf_var))
ncvar_put(fnc_mask, ncdf_var, array(rep(0, 4), dim = c(2, 2)))
nc_close(fnc_mask)
system(paste0("cdo -L -s remap", work_piece[['remap']], ",", common_grid_name,
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" ", mask_file, " ", mask_file_remap, " 2>/dev/null", sep = ""))
fnc_mask <- nc_open(mask_file_remap)
mask_lons <- ncvar_get(fnc_mask, 'lon')
mask_lats <- ncvar_get(fnc_mask, 'lat')
nc_close(fnc_mask)
file.remove(mask_file, mask_file_remap)
if ((dim(mask)[1] != common_grid_lons) || (dim(mask)[2] != common_grid_lats)) {
stop(paste("Error: the mask of the dataset with index ", tail(work_piece[['indices']], 1), " in '", work_piece[['dataset_type']], "' is wrong. It must be on the common grid if the selected output type is 'lonlat', 'lon' or 'lat', or 'areave' and 'grid' has been specified. It must be on the grid of the corresponding dataset if the selected output type is 'areave' and no 'grid' has been specified. For more information check ?Load and see help on parameters 'grid', 'maskmod' and 'maskobs'.", sep = ""))
}
mask_lons[which(mask_lons < 0)] <- mask_lons[which(mask_lons < 0)] + 360
if (lonmax >= lonmin) {
mask_lon_indices <- which((mask_lons >= lonmin) & (mask_lons <= lonmax))
} else {
mask_lon_indices <- which((mask_lons >= lonmin) | (mask_lons <= lonmax))
}
mask_lat_indices <- which((mask_lats >= latmin) & (mask_lats <= latmax))
if (sub_lat[1] < sub_lat[length(sub_lat)]) {
mask_lat_indices <- mask_lat_indices[length(mask_lat_indices):1]
}
mask <- mask[mask_lon_indices, mask_lat_indices]
}
sub_lon <- sub_lon[sub_lon_indices]
sub_lat <- sub_lat[sub_lat_indices]
### nc_close(fnc_mask)
### system(paste0("cdo -s -sellonlatbox,", if (lonmin > lonmax) {
### "0,360,"
### } else {
### paste0(lonmin, ",", lonmax, ",")
### }, latmin, ",", latmax,
### " -remap", work_piece[['remap']], ",", common_grid_name,
###This is wrong: same files
### " ", mask_file, " ", mask_file, " 2>/dev/null", sep = ""))
### fnc_mask <- nc_open(mask_file)
### mask <- take(ncvar_get(fnc_mask, work_piece[['mask']][['nc_var_name']],
### collapse_degen = FALSE), 1:2, subset_indices[dim_matches[1:2]])
###}
}
}
if (!all(dim_matches == sort(dim_matches))) {
if (!found_disordered_dims && rev(work_piece[['indices']])[2] == 1 && rev(work_piece[['indices']])[3] == 1) {
found_disordered_dims <- TRUE
cat(paste0("! Warning: the dimensions for the variable ", namevar, " in the files of the experiment with index ", tail(work_piece[['indices']], 1), " are not in the optimal order for loading with Load(). The optimal order would be '", paste(expected_dims, collapse = ', '), "'. One of the files of the dataset is stored in ", filename))
}
tmp <- aperm(tmp, dim_matches)
}
dim(tmp) <- final_dims
# If we are exploring the file we don't need to process and arrange
# the retrieved data. We only need to keep the dimension sizes.
if (is_2d_var && lonlat_subsetting_requested && remap_needed) {
final_lons <- sub_lon
final_lats <- sub_lat
} else {
final_lons <- lon
final_lats <- lat
}
if (explore_dims) {
if (work_piece[['is_file_per_member']]) {
## TODO: When the exp_full_path contains asterisks and is file_per_member
## members from different datasets may be accounted.
## Also if one file member is missing the accounting will be wrong.
## Should parse the file name and extract number of members.
if (is_url) {
nmemb <- NULL
} else {
nmemb <- length(files)
}
}
dims <- list(member = nmemb, ftime = nltime, lon = final_lons, lat = final_lats)
} else {
# If we are not exploring, then we have to process the retrieved data
if (is_2d_var) {
tmp <- apply(tmp, c(3, 4), function(x) {
# Disable of large values.
if (!is.na(work_piece[['var_limits']][2])) {
x[which(x > work_piece[['var_limits']][2])] <- NA
}
if (!is.na(work_piece[['var_limits']][1])) {
x[which(x < work_piece[['var_limits']][1])] <- NA
}
if (!is.null(mask)) {
x[which(mask < 0.5)] <- NA
}
if (output == 'areave' || output == 'lon') {
weights <- InsertDim(cos(final_lats * pi / 180), 1, length(final_lons))
weights[which(is.na(x))] <- NA
if (output == 'areave') {
weights <- weights / mean(weights, na.rm = TRUE)
mean(x * weights, na.rm = TRUE)
} else {
weights <- weights / InsertDim(MeanDims(weights, 2, na.rm = TRUE), 2, length(final_lats))
MeanDims(x * weights, 2, na.rm = TRUE)
}
} else if (output == 'lat') {
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} else if (output == 'lonlat') {
signif(x, 5)
}
})
if (output == 'areave') {
dim(tmp) <- c(1, 1, final_dims[3:4])
} else if (output == 'lon') {
dim(tmp) <- c(final_dims[1], 1, final_dims[3:4])
} else if (output == 'lat') {
dim(tmp) <- c(1, final_dims[c(2, 3, 4)])
} else if (output == 'lonlat') {
dim(tmp) <- final_dims
}
}
var_data <- attach.big.matrix(work_piece[['out_pointer']])
if (work_piece[['dims']][['member']] > 1 && nmemb > 1 &&
work_piece[['dims']][['ftime']] > 1 &&
nltime < work_piece[['dims']][['ftime']]) {
work_piece[['indices']][2] <- work_piece[['indices']][2] - 1
for (jmemb in members) {
work_piece[['indices']][2] <- work_piece[['indices']][2] + 1
out_position <- arrayIndex2VectorIndex(work_piece[['indices']], work_piece[['dims']])
out_indices <- out_position:(out_position + length(tmp[, , jmemb, ]) - 1)
var_data[out_indices] <- as.vector(tmp[, , jmemb, ])
}
work_piece[['indices']][2] <- work_piece[['indices']][2] - tail(members, 1) + 1
} else {
out_position <- arrayIndex2VectorIndex(work_piece[['indices']], work_piece[['dims']])
out_indices <- out_position:(out_position + length(tmp) - 1)
a <- aperm(tmp, c(1, 2, 4, 3))
as.vector(a)
var_data[out_indices] <- as.vector(aperm(tmp, c(1, 2, 4, 3)))
}
work_piece[['indices']][3] <- work_piece[['indices']][3] + 1
}
}
}
nc_close(fnc)
if (is_2d_var) {
if (remap_needed) {
array_across_gw <- FALSE
file.remove(filein)
###if (!is.null(mask) && lonlat_subsetting_requested) {
### file.remove(mask_file)
###}
} else {
if (first_lon_in_original_file < 0) {
array_across_gw <- data_across_gw
} else {
array_across_gw <- FALSE
}
}
}
}
if (explore_dims) {
list(dims = dims, is_2d_var = is_2d_var, grid = grid_name,
units = units, var_long_name = var_long_name,
data_across_gw = data_across_gw, array_across_gw = array_across_gw)
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} else {
###if (!silent && !is.null(progress_connection) && !is.null(work_piece[['progress_amount']])) {
### foobar <- writeBin(work_piece[['progress_amount']], progress_connection)
###}
if (!silent && !is.null(work_piece[['progress_amount']])) {
message(paste0(work_piece[['progress_amount']]), appendLF = FALSE)
}
found_file
}
}
.LoadSampleData <- function(var, exp = NULL, obs = NULL, sdates,
nmember = NULL, nmemberobs = NULL,
nleadtime = NULL, leadtimemin = 1,
leadtimemax = NULL, storefreq = 'monthly',
sampleperiod = 1, lonmin = 0, lonmax = 360,
latmin = -90, latmax = 90, output = 'areave',
method = 'conservative', grid = NULL,
maskmod = vector("list", 15),
maskobs = vector("list", 15),
configfile = NULL, suffixexp = NULL,
suffixobs = NULL, varmin = NULL, varmax = NULL,
silent = FALSE, nprocs = NULL) {
## This function loads and selects sample data stored in sampleMap and
## sampleTimeSeries and is used in the examples instead of Load() so as
## to avoid nco and cdo system calls and computation time in the stage
## of running examples in the CHECK process on CRAN.
selected_start_dates <- match(sdates, c('19851101', '19901101', '19951101',
'20001101', '20051101'))
start_dates_position <- 3
lead_times_position <- 4
if (output == 'lonlat') {
if (is.null(leadtimemax)) {
leadtimemax <- dim(sampleData$mod)[lead_times_position]
}
selected_lead_times <- leadtimemin:leadtimemax
dataOut <- sampleData
dataOut$mod <- sampleData$mod[, , selected_start_dates, selected_lead_times, , ]
dataOut$obs <- sampleData$obs[, , selected_start_dates, selected_lead_times, , ]
}
else if (output == 'areave') {