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# Take *_var parameters apart
take_var_params <- function(dim_params) {
# Take *_var parameters apart
var_params_ind <- grep('_var$', names(dim_params))
var_params <- dim_params[var_params_ind]
# Check all *_var are NULL or vectors of character strings, and
# that they all have a matching dimension param.
i <- 1
for (var_param in var_params) {
if (!is.character(var_param)) {
stop("All '*_var' parameters must be character strings.")
} else if (!any(grepl(paste0('^', strsplit(names(var_params)[i],
'_var$')[[1]][1], '$'),
names(dim_params)))) {
stop(paste0("All '*_var' parameters must be associated to a dimension parameter. Found parameter '",
names(var_params)[i], "' but no parameter '",
strsplit(names(var_params)[i], '_var$')[[1]][1], "'."))
}
i <- i + 1
}
# Make the keys of 'var_params' to be the name of
# the corresponding dimension.
if (length(var_params) < 1) {
var_params <- NULL
} else {
names(var_params) <- gsub('_var$', '', names(var_params))
}
return(var_params)
}
# Take *_reorder parameters apart
take_var_reorder <- function(dim_params) {
# Take *_reorder parameters apart
dim_reorder_params_ind <- grep('_reorder$', names(dim_params))
dim_reorder_params <- dim_params[dim_reorder_params_ind]
# Make the keys of 'dim_reorder_params' to be the name of
# the corresponding dimension.
if (length(dim_reorder_params) < 1) {
dim_reorder_params <- NULL
} else {
names(dim_reorder_params) <- gsub('_reorder$', '', names(dim_reorder_params))
}
return(dim_reorder_params)
}
# Take *_depends parameters apart
take_var_depends <- function(dim_params) {
depends_params_ind <- grep('_depends$', names(dim_params))
depends_params <- dim_params[depends_params_ind]
# Check all *_depends are NULL or vectors of character strings, and
# that they all have a matching dimension param.
i <- 1
for (depends_param in depends_params) {
if (!is.character(depends_param) || (length(depends_param) > 1)) {
stop("All '*_depends' parameters must be single character strings.")
} else if (!any(grepl(paste0('^', strsplit(names(depends_params)[i],
'_depends$')[[1]][1], '$'),
names(dim_params)))) {
stop(paste0("All '*_depends' parameters must be associated to a dimension parameter. Found parameter '",
names(depends_params)[i], "' but no parameter '",
strsplit(names(depends_params)[i], '_depends$')[[1]][1], "'."))
}
i <- i + 1
}
# Make the keys of 'depends_params' to be the name of
# the corresponding dimension.
if (length(depends_params) < 1) {
depends_params <- NULL
} else {
names(depends_params) <- gsub('_depends$', '', names(depends_params))
}
return(depends_params)
}
# Take *_across parameters apart
take_var_across <- function(dim_params) {
across_params_ind <- grep('_across$', names(dim_params))
across_params <- dim_params[across_params_ind]
# Check all *_across are NULL or vectors of character strings, and
# that they all have a matching dimension param.
i <- 1
for (across_param in across_params) {
if (!is.character(across_param) || (length(across_param) > 1)) {
stop("All '*_across' parameters must be single character strings.")
} else if (!any(grepl(paste0('^', strsplit(names(across_params)[i],
'_across$')[[1]][1], '$'),
names(dim_params)))) {
stop(paste0("All '*_across' parameters must be associated to a dimension parameter. Found parameter '",
names(across_params)[i], "' but no parameter '",
strsplit(names(across_params)[i], '_across$')[[1]][1], "'."))
}
i <- i + 1
}
# Make the keys of 'across_params' to be the name of
# the corresponding dimension.
if (length(across_params) < 1) {
across_params <- NULL
} else {
names(across_params) <- gsub('_across$', '', names(across_params))
}
return(across_params)
}
# Leave alone the dimension parameters in the variable dim_params
rebuild_dim_params <- function(dim_params, merge_across_dims,
inner_dims_across_files) {
var_params_ind <- grep('_var$', names(dim_params))
dim_reorder_params_ind <- grep('_reorder$', names(dim_params))
tolerance_params_ind <- grep('_tolerance$', names(dim_params))
depends_params_ind <- grep('_depends$', names(dim_params))
across_params_ind <- grep('_across$', names(dim_params))
# Leave alone the dimension parameters in the variable dim_params
if (length(c(var_params_ind, dim_reorder_params_ind, tolerance_params_ind,
depends_params_ind, across_params_ind)) > 0) {
dim_params <- dim_params[-c(var_params_ind, dim_reorder_params_ind,
tolerance_params_ind, depends_params_ind,
across_params_ind)]
# Reallocating pairs of across file and inner dimensions if they have
# to be merged. They are put one next to the other to ease merge later.
if (merge_across_dims) {
for (inner_dim_across in names(inner_dims_across_files)) {
inner_dim_pos <- which(names(dim_params) == inner_dim_across)
file_dim_pos <- which(names(dim_params) == inner_dims_across_files[[inner_dim_across]])
new_pos <- inner_dim_pos
if (file_dim_pos < inner_dim_pos) {
new_pos <- new_pos - 1
}
dim_params_to_move <- dim_params[c(inner_dim_pos, file_dim_pos)]
dim_params <- dim_params[-c(inner_dim_pos, file_dim_pos)]
new_dim_params <- list()
if (new_pos > 1) {
new_dim_params <- c(new_dim_params, dim_params[1:(new_pos - 1)])
}
new_dim_params <- c(new_dim_params, dim_params_to_move)
if (length(dim_params) >= new_pos) {
new_dim_params <- c(new_dim_params, dim_params[new_pos:length(dim_params)])
}
dim_params <- new_dim_params
}
}
}
dim_names <- names(dim_params)
if (is.null(dim_names)) {
stop("At least one pattern dim must be specified.")
}
return(dim_params)
}
# Look for chunked dims
look_for_chunks <- function(dim_params, dim_names) {
chunks <- vector('list', length(dim_names))
names(chunks) <- dim_names
for (dim_name in dim_names) {
if (!is.null(attr(dim_params[[dim_name]], 'chunk'))) {
chunks[[dim_name]] <- attr(dim_params[[dim_name]], 'chunk')
attributes(dim_params[[dim_name]]) <- attributes(dim_params[[dim_name]])[-which(names(attributes(dim_params[[dim_name]])) == 'chunk')]
} else {
chunks[[dim_name]] <- c(chunk = 1, n_chunks = 1)
}
}
return(chunks)
}
# This is a helper function to compute the chunk indices to take once the total
# number of indices for a dimension has been discovered.
chunk_indices <- function(n_indices, chunk, n_chunks, dim_name) {
if (n_chunks > n_indices) {
stop("Requested to divide dimension '", dim_name, "' of length ",
n_indices, " in ", n_chunks, " chunks, which is not possible.")
}
chunk_sizes <- rep(floor(n_indices / n_chunks), n_chunks)
chunks_to_extend <- n_indices - chunk_sizes[1] * n_chunks
if (chunks_to_extend > 0) {
chunk_sizes[1:chunks_to_extend] <- chunk_sizes[1:chunks_to_extend] + 1
}
chunk_size <- chunk_sizes[chunk]
offset <- 0
if (chunk > 1) {
offset <- sum(chunk_sizes[1:(chunk - 1)])
}
indices <- 1:chunk_sizes[chunk] + offset
array(indices, dim = setNames(length(indices), dim_name))
}
# Check pattern_dims
# Function found_pattern_dims may change pattern_dims in the parent.env
found_pattern_dims <- function(pattern_dims, dim_names, var_params,
dim_params, dim_reorder_params) {
if (is.null(pattern_dims)) {
.warning(paste0("Parameter 'pattern_dims' not specified. Taking the first dimension, '",
dim_names[1], "' as 'pattern_dims'."))
assign('pattern_dims', dim_names[1], envir = parent.frame())
pattern_dims <- dim_names[1]
} else if (is.character(pattern_dims) && (length(pattern_dims) > 0)) {
assign('pattern_dims', unique(pattern_dims), envir = parent.frame())
pattern_dims <- unique(pattern_dims)
} else {
stop("Parameter 'pattern_dims' must be a vector of character strings.")
}
if (any(names(var_params) %in% pattern_dims)) {
stop("'*_var' parameters specified for pattern dimensions. Remove or fix them.")
}
# Find the pattern dimension with the pattern specifications
found_pattern_dim <- NULL
for (pattern_dim in pattern_dims) {
# Check all specifications in pattern_dim are valid
# dat <- datasets <- dim_params[[pattern_dim]]
dat <- dim_params[[pattern_dim]]
if (is.null(dat) || !(is.character(dat) && all(nchar(dat) > 0)) && !is.list(dat)) {
stop(paste0("Parameter '", pattern_dim,
"' must be a list of lists with pattern specifications or a vector of character strings."))
}
if (!is.null(dim_reorder_params[[pattern_dim]])) {
.warning(paste0("A reorder for the selectors of '", pattern_dim,
"' has been specified, but it is a pattern dimension and the reorder will be ignored."))
}
if (is.list(dat) || any(sapply(dat, is.list))) {
if (is.null(found_pattern_dim)) {
found_pattern_dim <- pattern_dim
} else {
stop("Found more than one pattern dim with pattern specifications (list of lists). One and only one pattern dim must contain pattern specifications.")
}
}
}
if (is.null(found_pattern_dim)) {
.warning(paste0("Could not find any pattern dim with explicit data set descriptions (in the form of list of lists). Taking the first pattern dim, '", pattern_dims[1], "', as dimension with pattern specifications."))
found_pattern_dim <- pattern_dims[1]
}
return(found_pattern_dim)
}
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# The variable 'dat' is mounted with the information (name, path) of each dataset.
# NOTE: This function creates the object 'dat_names' in the parent env.
mount_dat <- function(dat, pattern_dim, found_pattern_dim) {
# dat_info_names <- c('name', 'path')#, 'nc_var_name', 'suffix', 'var_min', 'var_max', 'dimnames')
dat_to_fetch <- c()
dat_names <- c()
if (!is.list(dat)) {
dat <- as.list(dat)
} else {
if (!any(sapply(dat, is.list))) {
dat <- list(dat)
}
}
for (i in 1:length(dat)) {
if (is.character(dat[[i]]) && length(dat[[i]]) == 1 && nchar(dat[[i]]) > 0) {
if (grepl('^(\\./|\\.\\./|/.*/|~/)', dat[[i]])) {
dat[[i]] <- list(path = dat[[i]])
} else {
dat[[i]] <- list(name = dat[[i]])
}
} else if (!is.list(dat[[i]])) {
stop(paste0("Parameter '", pattern_dim,
"' is incorrect. It must be a list of lists or character strings."))
}
#if (!(all(names(dat[[i]]) %in% dat_info_names))) {
# stop("Error: parameter 'dat' is incorrect. There are unrecognized components in the information of some of the datasets. Check 'dat' in ?Load for details.")
#}
if (!('name' %in% names(dat[[i]]))) {
dat[[i]][['name']] <- paste0('dat', i)
if (!('path' %in% names(dat[[i]]))) {
stop(paste0("Parameter '", found_pattern_dim,
"' is incorrect. A 'path' should be provided for each dataset if no 'name' is provided."))
}
} else if (!('path' %in% names(dat[[i]]))) {
dat_to_fetch <- c(dat_to_fetch, i)
}
#if ('path' %in% names(dat[[i]])) {
# if (!('nc_var_name' %in% names(dat[[i]]))) {
# dat[[i]][['nc_var_name']] <- '$var_name$'
# }
# if (!('suffix' %in% names(dat[[i]]))) {
# dat[[i]][['suffix']] <- ''
# }
# if (!('var_min' %in% names(dat[[i]]))) {
# dat[[i]][['var_min']] <- ''
# }
# if (!('var_max' %in% names(dat[[i]]))) {
# dat[[i]][['var_max']] <- ''
# }
#}
dat_names <- c(dat_names, dat[[i]][['name']])
}
if ((length(dat_to_fetch) > 0) && (length(dat_to_fetch) < length(dat))) {
.warning("'path' has been provided for some datasets. Any information in the configuration file related to these will be ignored.")
}
if (length(dat_to_fetch) > 0) {
stop("Specified only the name for some data sets, but not the path ",
"pattern. This option has not been yet implemented.")
}
assign('dat_names', dat_names, envir = parent.frame())
return(dat)
}
# Add attributes indicating whether this dimension selector is value or indice
add_value_indices_flag <- function(x) {
if (is.null(attr(x, 'values')) || is.null(attr(x, 'indices'))) {
flag <- (any(x %in% c('all', 'first', 'last')) || is.numeric(unlist(x)))
attr(x, 'values') <- !flag
attr(x, 'indices') <- flag
}
return(x)
}
# Find the value for the undefined selector (i.e., indices()). Use the value from the first
# found file.
# Note that "dat[[i]][['path']]" in parent env. is changed in this function.
find_ufd_value <- function(undefined_file_dims, dat, i, replace_values,
first_file, file_dims, path_glob_permissive,
depending_file_dims, dat_selectors, selector_checker, chunks) {
first_values <- vector('list', length = length(undefined_file_dims))
names(first_values) <- undefined_file_dims
found_values <- 0
stop <- FALSE
try_dim <- 1
last_success <- 1
while ((found_values < length(undefined_file_dims)) && !stop) {
u_file_dim <- undefined_file_dims[try_dim]
if (is.null(first_values[[u_file_dim]])) {
path_with_globs_and_tag <- .ReplaceVariablesInString(dat[[i]][['path']],
replace_values[-which(file_dims == u_file_dim)],
allow_undefined_key_vars = TRUE)
found_value <- .FindTagValue(path_with_globs_and_tag,
first_file, u_file_dim)
if (!is.null(found_value)) {
found_values <- found_values + 1
last_success <- try_dim
first_values[[u_file_dim]] <- found_value
replace_values[[u_file_dim]] <- found_value
}
}
try_dim <- (try_dim %% length(undefined_file_dims)) + 1
if (try_dim == last_success) {
stop <- TRUE
}
}
if (found_values < length(undefined_file_dims)) {
stop(paste0("Path pattern of dataset '", dat[[i]][['name']],
"' is too complex. Could not automatically ",
"detect values for all non-explicitly defined ",
"indices. Check its pattern: ", dat[[i]][['path']]))
}
## TODO: Replace ReplaceGlobExpressions by looped call to FindTagValue? As done above
## Maybe it can solve more cases actually. I got warnings in ReplGlobExp with a typical
## cmor case, requesting all members and chunks for fixed var and sdate. Not fixing
## sdate raised 'too complex' error.
# Replace shell globs in path pattern and keep the file_dims as tags
dat[[i]][['path']] <- .ReplaceGlobExpressions(dat[[i]][['path']], first_file, replace_values,
file_dims, dat[[i]][['name']], path_glob_permissive)
# Now time to look for the available values for the non
# explicitly defined selectors for the file dimensions.
#print("H")
# Check first the ones that do not depend on others.
ufd <- c(undefined_file_dims[which(!(undefined_file_dims %in% names(depending_file_dims)))],
undefined_file_dims[which(undefined_file_dims %in% names(depending_file_dims))])
for (u_file_dim in ufd) {
replace_values[undefined_file_dims] <- first_values
replace_values[[u_file_dim]] <- '*'
depended_dim <- NULL
depended_dim_values <- NA
#NOTE: Here 'selectors' is always 1. Is it supposed to be like this?
selectors <- dat_selectors[[u_file_dim]][[1]]
if (u_file_dim %in% names(depending_file_dims)) {
depended_dim <- depending_file_dims[[u_file_dim]]
depended_dim_values <- dat_selectors[[depended_dim]][[1]]
dat_selectors[[u_file_dim]] <- vector('list', length = length(depended_dim_values))
names(dat_selectors[[u_file_dim]]) <- depended_dim_values
} else {
dat_selectors[[u_file_dim]] <- list()
}
if (u_file_dim %in% unlist(depending_file_dims)) {
depending_dims <- names(depending_file_dims)[which(sapply(depending_file_dims, function(x) u_file_dim %in% x))]
replace_values[depending_dims] <- rep('*', length(depending_dims))
}
for (j in 1:length(depended_dim_values)) {
parsed_values <- c()
if (!is.null(depended_dim)) {
replace_values[[depended_dim]] <- depended_dim_values[j]
}
path_with_globs <- .ReplaceVariablesInString(dat[[i]][['path']], replace_values)
found_files <- Sys.glob(path_with_globs)
## TODO: Enhance this error message, or change by warning.
## Raises if a wrong sdate is specified, for example.
if (length(found_files) == 0) {
.warning(paste0("Could not find files for any '", u_file_dim,
"' for '", depended_dim, "' = '",
depended_dim_values[j], "'."))
dat_selectors[[u_file_dim]][[j]] <- NA
} else {
for (found_file in found_files) {
path_with_globs_and_tag <- .ReplaceVariablesInString(dat[[i]][['path']],
replace_values[-which(file_dims == u_file_dim)],
allow_undefined_key_vars = TRUE)
parsed_values <- c(parsed_values,
.FindTagValue(path_with_globs_and_tag, found_file,
u_file_dim))
}
#TODO: selector_checker() doesn't allow selectors to be characters. For selectors
# like "member = 'r7i1p1f1", it cannot be defined with values.
dat_selectors[[u_file_dim]][[j]] <- selector_checker(selectors = selectors,
var = unique(parsed_values),
return_indices = FALSE)
# Take chunk if needed
dat_selectors[[u_file_dim]][[j]] <- dat_selectors[[u_file_dim]][[j]][chunk_indices(length(dat_selectors[[u_file_dim]][[j]]),
chunks[[u_file_dim]]['chunk'],
chunks[[u_file_dim]]['n_chunks'],
u_file_dim)]
}
}
}
#NOTE: change 'dat' in parent env. because "dat[[i]][['path']]" is changed.
assign('dat', dat, envir = parent.frame())
return(dat_selectors)
}
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# Adjust the argument 'return_vars' if users don't assign them properly.
# Force return_vars = (time = NULL) to (time = 'sdate') if one of the situations:
# (1) selector = [sdate = 2, time = 4], or
# (2) time_across = 'sdate'.
correct_return_vars <- function(inner_dim, inner_dims_across_files, found_pattern_dim,
file_dim_as_selector_array_dim) {
# inner_dim is not in return_vars or is NULL
if (is.character(file_dim_as_selector_array_dim)) { #(1)
if (file_dim_as_selector_array_dim %in% found_pattern_dim) {
stop(paste0("Found '", inner_dim, "' selector has dimension of the pattern dim '",
found_pattern_dim,
"', which is not allowed. To assign the dependency on the pattern dim, ",
"use 'return_vars = list(", inner_dim, " = 'dat')' instead."))
} else {
corrected_value <- file_dim_as_selector_array_dim
}
} else if (inner_dim %in% inner_dims_across_files) { #(2)
file_dim_name <- names(which(inner_dim == inner_dims_across_files))
if (file_dim_name %in% found_pattern_dim) {
stop(paste0("Found '", inner_dim, "' has across dependency on the pattern dim '",
found_pattern_dim, "', which is not allowed."))
} else {
corrected_value <- file_dim_name
}
}
.warning(paste0("Found ", inner_dim, " dependency on file diemnsion '", corrected_value,
"', but '", inner_dim, "' is not in return_vars list or is NULL. ",
"To provide the correct metadata, the value of ", inner_dim,
" in 'return_vars' is specified as '", corrected_value, "'."))
return(corrected_value)
}
# The time classes that are needed to adjust time zone back to UTC.
time_special_types <- function() {
list('POSIXct' = as.POSIXct, 'POSIXlt' = as.POSIXlt, 'Date' = as.Date)
}
# Replace the dim names read from netCDF file with the user-specified synonims.
replace_with_synonmins <- function(read_dims, synonims) {
corrected_dim_name <- sapply(names(read_dims),
function(x) {
which_entry <- which(sapply(synonims, function(y) x %in% y))
if (length(which_entry) > 0) {
names(synonims)[which_entry]
} else {
x
}
})
return(corrected_dim_name)
}
# Prepare vars_to_read for this dataset (i loop) and this file (j loop)
generate_vars_to_read <- function(return_vars, changed_dims, first_found_file, common_return_vars,
common_first_found_file, i) {
vars_to_read <- NULL
if (length(return_vars) > 0) {
#NOTE: because return_vars has changed 'dat' to character(0) above (line 1775),
# 'dat' won't be included in vars_to_read here.
vars_to_read <- names(return_vars)[sapply(return_vars, function(x) any(names(changed_dims) %in% x))]
}
if (!is.null(first_found_file)) {
if (any(!first_found_file)) {
vars_to_read <- c(vars_to_read, names(first_found_file[which(!first_found_file)]))
}
}
if ((i == 1) && (length(common_return_vars) > 0)) {
vars_to_read <- c(vars_to_read, names(common_return_vars)[sapply(common_return_vars, function(x) any(names(changed_dims) %in% x))])
}
if (!is.null(common_first_found_file)) {
if (any(!common_first_found_file)) {
vars_to_read <- c(vars_to_read, names(common_first_found_file[which(!common_first_found_file)]))
}
}
return(vars_to_read)
}
# Find the largest dims length within one dataset.
find_largest_dims_length <- function(selectors_total_list, array_of_files_to_load,
selector_indices_save, dat, expected_inner_dims,
synonims, file_dim_reader) {
# Open and get all the dims from all the files
data_dims_all_files <- vector('list', length = length(selectors_total_list))
for (selectors_kk in 1:length(data_dims_all_files)) {
file_to_open <- do.call("[", c(list(array_of_files_to_load),
as.list(selector_indices_save[[selectors_kk]])))
data_dims_all_files[[selectors_kk]] <- try(
file_dim_reader(file_to_open, NULL, selectors_total_list[[selectors_kk]],
lapply(dat[['selectors']][expected_inner_dims], '[[', 1),
synonims), silent = TRUE)
}
# Remove the missing files (i.e., fail try above)
if (!identical(which(substr(data_dims_all_files, 1, 5) == 'Error'), integer(0))) {
tmp <- which(substr(data_dims_all_files, 1, 5) == 'Error')
data_dims_all_files <- data_dims_all_files[-tmp]
}
# Find the longest dimensions from all the files
largest_data_dims <- rep(0, length(data_dims_all_files[[1]]))
# The inner dim order may differ among files. Need to align them before
# find out the largest dim length.
dim_names_first_file <- names(data_dims_all_files[[1]])
same_dim_order <-lapply(lapply(data_dims_all_files, names),
identical, dim_names_first_file)
for (to_fix in which(!unlist(same_dim_order))) {
data_dims_all_files[[to_fix]] <- data_dims_all_files[[to_fix]][match(dim_names_first_file,
names(data_dims_all_files[[to_fix]]))]
}
for (kk in 1:length(data_dims_all_files[[1]])) {
largest_data_dims[kk] <- max(sapply(data_dims_all_files, '[', kk))
}
names(largest_data_dims) <- names(data_dims_all_files[[1]])
return(largest_data_dims)
}
# Gererate vars_to_transform from picked_vars[[i]] and picked_common_vars
generate_vars_to_transform <- function(vars_to_transform, picked_vars, transform_vars,
picked_vars_ordered) {
# In Start(), picked_vars can be picked_vars[[i]] or picked_common_vars
picked_vars_to_transform <- which(names(picked_vars) %in% transform_vars)
if (length(picked_vars_to_transform) > 0) {
picked_vars_to_transform <- names(picked_vars)[picked_vars_to_transform]
new_vars_to_transform <- picked_vars[picked_vars_to_transform]
which_are_ordered <- which(!sapply(picked_vars_ordered[picked_vars_to_transform], is.null))
if (length(which_are_ordered) > 0) {
tmp <- which(!is.na(match(names(picked_vars_ordered), names(which_are_ordered))))
new_vars_to_transform[which_are_ordered] <- picked_vars_ordered[tmp]
}
vars_to_transform <- c(vars_to_transform, new_vars_to_transform)
}
return(vars_to_transform)
}
# Out-of-range warning
show_out_of_range_warning <- function(inner_dim, range, bound) {
# bound: 'lower' or 'upper'
.warning(paste0("The ", bound, " boundary of selector of ", inner_dim,
" is out of range [", min(range), ", ", max(range), "]. ",
"Check if the desired range is all included."))
}
# Generate sub_array_of_fri
generate_sub_array_of_fri <- function(with_transform, goes_across_prime_meridian, sub_array_of_indices, n, beta,
is_circular_dim) {
print_warning <- FALSE
if (goes_across_prime_meridian) {
#NOTE: The potential problem here is, if it is global longitude,
# and the indices overlap (e.g., lon = [0, 359.723] and
# CircularSort(-180, 180), then sub_array_of_indices = list(649, 649)).
# Therefore, sub_array_of_fri will be c(1:649, 649:1296). We'll get two 649.
# The fix below may not be the best solution, but it works for the example above.
if (sub_array_of_indices[[1]] == sub_array_of_indices[[2]]) {
# global longitude
sub_array_of_fri <- 1:n # n = prod(dim(var_with_selectors))
if (with_transform & beta != 0) {
# Warning if transform_extra_cell != 0
print_warning <- TRUE
}
} else {
# normal case, i.e., not global
first_index <- min(unlist(sub_array_of_indices))
last_index <- max(unlist(sub_array_of_indices))
if (with_transform) {
gap_width <- last_index - first_index - 1
actual_beta <- min(gap_width, beta)
sub_array_of_fri <- c(1:(first_index + actual_beta),
(last_index - actual_beta):n)
if (actual_beta != beta) {
print_warning <- TRUE
}
} else {
sub_array_of_fri <- c(1:first_index, last_index:n)
}
}
} else {
#NOTE: This if seems redundant.
# if (is.list(sub_array_of_indices)) {
# sub_array_of_indices <- sub_array_of_indices[[1]]:sub_array_of_indices[[2]]
# }
#NOTE: sub_array_of_indices may be vector or list
if (with_transform) {
first_index <- min(unlist(sub_array_of_indices))
last_index <- max(unlist(sub_array_of_indices))
start_padding <- min(beta, first_index - 1)
end_padding <- min(beta, n - last_index)
if (!is_circular_dim) { #latitude or when <var>_reorder is not used
sub_array_of_fri <- (first_index - start_padding):(last_index + end_padding)
if (start_padding != beta | end_padding != beta) {
print_warning <- TRUE
}
} else { #longitude
# if (((last_index + beta) - (first_index - beta) + 1) >= n) {
if (start_padding <= beta & end_padding <= beta) {
sub_array_of_fri <- 1:n
} else if (start_padding < beta) { # left side too close to border, need to go to right side
sub_array_of_fri <- c((first_index - start_padding):(last_index + end_padding), (n - (beta - start_padding - 1)):n)
} else if (end_padding < beta) { # right side too close to border, need to go to left side
sub_array_of_fri <- c(1: (beta - end_padding), (first_index - start_padding):(last_index + end_padding))
} else { #normal
sub_array_of_fri <- (first_index - start_padding):(last_index + end_padding)
}
}
} else {
if (is.list(sub_array_of_indices)) {
sub_array_of_fri <- sub_array_of_indices[[1]]:sub_array_of_indices[[2]]
} else {
sub_array_of_fri <- sub_array_of_indices
}
}
}
if (print_warning) {
.warning(paste0("Adding parameter transform_extra_cells = ", beta,
" to the transformed index excesses ",
"the border. The border index is used for transformation."))
}
return(sub_array_of_fri)
}
# This function merges two dimensions (e.g., time and sdate if "time_across = 'sdate'") into one.
# The two dimensions have to be next to each other. In Start(), it is used to reshape
# final_dims_fake if merge_across_dims = TRUE
dims_merge <- function(inner_dims_across_files, final_dims_fake) {
# inner_dims_across_files would be like: $sdate: "time"
for (file_dim_across in names(inner_dims_across_files)) {
inner_dim_pos <- which(names(final_dims_fake) == inner_dims_across_files[[file_dim_across]])
new_dims <- c()
# part 1: Put the dims before 'time' in new_dims
if (inner_dim_pos > 1) {
new_dims <- c(new_dims, final_dims_fake[1:(inner_dim_pos - 1)])
}
# part 2: Merge time and sdate together, and name this dim as 'time'
# The cross and being crossed dims are next to each other, e.g., [time, sdate]
new_dims <- c(new_dims, setNames(prod(final_dims_fake[c(inner_dim_pos, inner_dim_pos + 1)]),
inner_dims_across_files[[file_dim_across]]))
# part 3: Put the dimes after 'sdate' in new_dims
if (inner_dim_pos + 1 < length(final_dims_fake)) {
new_dims <- c(new_dims, final_dims_fake[(inner_dim_pos + 2):length(final_dims_fake)])
}
final_dims_fake <- new_dims
}
return(final_dims_fake)
}
# This function splits one dimension into two. In Start(), it is used to reshape final_dims_fake
# if split_multiselected_dims = TRUE.
dims_split <- function(dim_params, final_dims_fake) {
all_split_dims <- NULL
for (dim_param in 1:length(dim_params)) {
split_dims <- dim(dim_params[[dim_param]])
if (!is.null(split_dims)) {
if (length(split_dims) > 1) {
all_split_dims <- c(all_split_dims, setNames(list(split_dims),
names(dim_params)[dim_param]))
if (is.null(names(split_dims))) {
names(split_dims) <- paste0(names(dim_params)[dim_param],
1:length(split_dims))
}
old_dim_pos <- which(names(final_dims_fake) == names(dim_params)[dim_param])
# If merge_across_dims and split_multiselected_dims are both used,
# on one file dim, and this file dim is multi-dim, it doesn't work.
if (identical(old_dim_pos, integer(0))) {
stop(paste0("The dimension '", names(dim_params)[dim_param],
"' to be split cannot be found after 'merge_across_dims' ",
"is used. Check if the reshape parameters are used appropriately."))
}
# NOTE: Three steps to create new dims.
# 1st: Put in the dims before split_dim.
# 2nd: Replace the old_dim with split_dims.
# 3rd: Put in the dims after split_dim.
new_dims <- c()
if (old_dim_pos > 1) {
new_dims <- c(new_dims, final_dims_fake[1:(old_dim_pos - 1)])
}
new_dims <- c(new_dims, split_dims)
if (old_dim_pos < length(final_dims_fake)) {
new_dims <- c(new_dims, final_dims_fake[(old_dim_pos + 1):length(final_dims_fake)])
}
final_dims_fake <- new_dims
}
}
}
return(list(final_dims_fake, all_split_dims))
}
# This function sums up the length of all the inner across dim (e.g., time: list(31, 29, 31, 30))
# and use it to replace the value of that inner dim. That is, it returns the actual length of
# time rather than using the one including NAs. In Start(), it is used to reshape final_dims_fake
# if merge_across_dims = TRUE, merge_across_dims_narm = TRUE, and split_multiselected_dims = FALSE.
merge_narm_dims <- function(final_dims_fake, across_inner_dim, length_inner_across_dim) {
final_dims_fake_name <- names(final_dims_fake)
pos_across_inner_dim <- which(final_dims_fake_name == across_inner_dim)
new_length_inner_dim <- sum(unlist(length_inner_across_dim))
if (pos_across_inner_dim != length(final_dims_fake)) {
final_dims_fake <- c(final_dims_fake[1:(pos_across_inner_dim - 1)],
new_length_inner_dim,
final_dims_fake[(pos_across_inner_dim + 1):length(final_dims_fake)])
} else {
final_dims_fake <- c(final_dims_fake[1:(pos_across_inner_dim - 1)],
new_length_inner_dim)
}
names(final_dims_fake) <- final_dims_fake_name
return(final_dims_fake)
}
# Adjust the dim order. If split_multiselected_dims + merge_across_dims, the dim order may
# need to be changed. The inner_dim needs to be the first dim among split dims.
reorder_split_dims <- function(all_split_dims, inner_dim_pos_in_split_dims, final_dims_fake) {
all_split_dims <- c(all_split_dims[inner_dim_pos_in_split_dims],
all_split_dims[1:length(all_split_dims)][-inner_dim_pos_in_split_dims])
split_dims_pos <- which(!is.na(match(names(final_dims_fake), names(all_split_dims))))
new_dims <- c()
if (split_dims_pos[1] != 1) {
new_dims <- c(new_dims, final_dims_fake[1:(split_dims_pos[1] - 1)])
}
new_dims <- c(new_dims, all_split_dims)
if (split_dims_pos[length(split_dims_pos)] < length(final_dims_fake)) {
new_dims <- c(new_dims, final_dims_fake[(split_dims_pos[length(split_dims_pos)] + 1):length(final_dims_fake)])
}
final_dims_fake <- new_dims
return(list(final_dims_fake, all_split_dims))
}
# Build the work pieces.
build_work_pieces <- function(work_pieces, i, selectors, file_dims, inner_dims, final_dims,
found_pattern_dim, inner_dims_across_files, array_of_files_to_load,
array_of_not_found_files, array_of_metadata_flags,
metadata_file_counter, depending_file_dims, transform,
transform_vars, picked_vars, picked_vars_ordered, picked_common_vars,
metadata_folder, debug = debug) {
sub_array_dims <- final_dims[file_dims]
sub_array_dims[found_pattern_dim] <- 1
sub_array_of_files_to_load <- array(1:prod(sub_array_dims),
dim = sub_array_dims)
names(dim(sub_array_of_files_to_load)) <- names(sub_array_dims)
# Detect which of the dimensions of the dataset go across files.
file_dim_across_files <- lapply(inner_dims,
function(x) {
dim_across <- sapply(inner_dims_across_files, function(y) x %in% y)
if (any(dim_across)) {
names(inner_dims_across_files)[which(dim_across)[1]]
} else {
NULL
}
})
names(file_dim_across_files) <- inner_dims
j <- 1
while (j <= prod(sub_array_dims)) {
# Work out file path.
file_to_load_sub_indices <- which(sub_array_of_files_to_load == j, arr.ind = TRUE)[1, ]
names(file_to_load_sub_indices) <- names(sub_array_dims)
file_to_load_sub_indices[found_pattern_dim] <- i
big_dims <- rep(1, length(dim(array_of_files_to_load)))
names(big_dims) <- names(dim(array_of_files_to_load))
file_to_load_indices <- .MergeArrayDims(file_to_load_sub_indices, big_dims)[[1]]
file_to_load <- do.call('[[', c(list(array_of_files_to_load),
as.list(file_to_load_indices)))
not_found_file <- do.call('[[', c(list(array_of_not_found_files),
as.list(file_to_load_indices)))
load_file_metadata <- do.call('[', c(list(array_of_metadata_flags),
as.list(file_to_load_indices)))
if (load_file_metadata) {
metadata_file_counter <- metadata_file_counter + 1
assign('metadata_file_counter', metadata_file_counter, envir = parent.frame())
}
if (!is.na(file_to_load) && !not_found_file) {
# Work out indices to take
first_round_indices <- lapply(inner_dims,
function (x) {
if (is.null(file_dim_across_files[[x]])) {
x_dim_name <- attr(attr(selectors[[x]][['fri']], "dim"), "names")
if (!is.null(x_dim_name)) {
which_chunk <- file_to_load_sub_indices[x_dim_name]
selectors[[x]][['fri']][[which_chunk]]
} else {
selectors[[x]][['fri']][[1]]
}
} else {
which_chunk <- file_to_load_sub_indices[file_dim_across_files[[x]]]
selectors[[x]][['fri']][[which_chunk]]
}
})
names(first_round_indices) <- inner_dims
second_round_indices <- lapply(inner_dims,
function (x) {
if (is.null(file_dim_across_files[[x]])) {
x_dim_name <- attr(attr(selectors[[x]][['sri']], "dim"), "names")
if (!is.null(x_dim_name)) {
which_chunk <- file_to_load_sub_indices[x_dim_name]
selectors[[x]][['sri']][[which_chunk]]
} else {
selectors[[x]][['sri']][[1]]
}
} else {
which_chunk <- file_to_load_sub_indices[file_dim_across_files[[x]]]
selectors[[x]][['sri']][[which_chunk]]
}
})
if (debug) {
print("-> BUILDING A WORK PIECE")
#print(str(selectors))
}
names(second_round_indices) <- inner_dims
if (!any(sapply(first_round_indices, length) == 0)) {
work_piece <- list()
work_piece[['first_round_indices']] <- first_round_indices
work_piece[['second_round_indices']] <- second_round_indices
work_piece[['file_indices_in_array_of_files']] <- file_to_load_indices
work_piece[['file_path']] <- file_to_load
work_piece[['store_dims']] <- final_dims
# Work out store position
store_position <- final_dims
store_position[names(file_to_load_indices)] <- file_to_load_indices
store_position[inner_dims] <- rep(1, length(inner_dims))
work_piece[['store_position']] <- store_position
# Work out file selectors
file_selectors <- sapply(file_dims,
function (x) {
vector_to_pick <- 1
if (x %in% names(depending_file_dims)) {
vector_to_pick <- file_to_load_indices[depending_file_dims[[x]]]
}
selectors[file_dims][[x]][[vector_to_pick]][file_to_load_indices[x]]
})
names(file_selectors) <- file_dims
work_piece[['file_selectors']] <- file_selectors
# Send variables for transformation
if (!is.null(transform) && (length(transform_vars) > 0)) {
vars_to_transform <- NULL
picked_vars_to_transform <- which(names(picked_vars) %in% transform_vars)
if (length(picked_vars_to_transform) > 0) {
picked_vars_to_transform <- names(picked_vars)[picked_vars_to_transform]
vars_to_transform <- c(vars_to_transform, picked_vars[picked_vars_to_transform])
if (any(picked_vars_to_transform %in% names(picked_vars_ordered))) {
picked_vars_ordered_to_transform <- picked_vars_to_transform[which(picked_vars_to_transform %in% names(picked_vars_ordered))]
vars_to_transform[picked_vars_ordered_to_transform] <- picked_vars_ordered[picked_vars_ordered_to_transform]
}
}
picked_common_vars_to_transform <- which(names(picked_common_vars) %in% transform_vars)
if (length(picked_common_vars_to_transform) > 0) {
picked_common_vars_to_transform <- names(picked_common_vars)[picked_common_vars_to_transform]
vars_to_transform <- c(vars_to_transform, picked_common_vars[picked_common_vars_to_transform])
if (any(picked_common_vars_to_transform %in% names(picked_common_vars_ordered))) {
picked_common_vars_ordered_to_transform <- picked_common_vars_to_transform[which(picked_common_vars_to_transform %in% names(picked_common_vars_ordered))]
vars_to_transform[picked_common_vars_ordered_to_transform] <- picked_common_vars_ordered[picked_common_vars_ordered_to_transform]
}
}
work_piece[['vars_to_transform']] <- vars_to_transform
}
# Send flag to load metadata
if (load_file_metadata) {
work_piece[['save_metadata_in']] <- paste0(metadata_folder, '/', metadata_file_counter)
}
work_pieces <- c(work_pieces, list(work_piece))
}
}
j <- j + 1
}
return(work_pieces)
}
# Calculate the progress %s that will be displayed and assign them to the appropriate work pieces.
retrieve_progress_message <- function(work_pieces, num_procs, silent) {
if (length(work_pieces) / num_procs >= 2 && !silent) {
if (length(work_pieces) / num_procs < 10) {
amount <- 100 / ceiling(length(work_pieces) / num_procs)
reps <- ceiling(length(work_pieces) / num_procs)
} else {
amount <- 10
reps <- 10
}
progress_steps <- rep(amount, reps)
if (length(work_pieces) < (reps + 1)) {
selected_pieces <- length(work_pieces)
progress_steps <- c(sum(head(progress_steps, reps)),
tail(progress_steps, reps))
} else {
selected_pieces <- round(seq(1, length(work_pieces),
length.out = reps + 1))[-1]
}
progress_steps <- paste0(' + ', round(progress_steps, 2), '%')
progress_message <- 'Progress: 0%'
} else {
progress_message <- ''
selected_pieces <- NULL
}
piece_counter <- 1
step_counter <- 1
work_pieces <- lapply(work_pieces,
function (x) {
if (piece_counter %in% selected_pieces) {
wp <- c(x, list(progress_amount = progress_steps[step_counter]))
step_counter <<- step_counter + 1
} else {
wp <- x
}
piece_counter <<- piece_counter + 1
wp
})
if (!silent) {
.message("If the size of the requested data is close to or above the free shared RAM memory, R may crash.")
.message("If the size of the requested data is close to or above the half of the free RAM memory, R may crash.")
.message(paste0("Will now proceed to read and process ", length(work_pieces), " data files:"))
if (length(work_pieces) < 30) {
lapply(work_pieces, function (x) .message(x[['file_path']], indent = 2))
} else {
.message("The list of files is long. You can check it after Start() finishes in the output '$Files'.", indent = 2, exdent = 5)
}
}
# Build the cluster of processes that will do the work and dispatch work pieces.
# The function .LoadDataFile is applied to each work piece. This function will
# open the data file, regrid if needed, subset, apply the mask,
# compute and apply the weights if needed,
# disable extreme values and store in the shared memory matrix.
#print("O")
if (!silent) {
.message("Loading... This may take several minutes...")
if (progress_message != '') {
.message(progress_message, appendLF = FALSE)
}
}
return(work_pieces)
}
# If merge_across_dims = TRUE and merge_across_dims_narm = TRUE, remove the additional NAs
# due to unequal inner_dim ('time') length across file_dim ('sdate').
remove_additional_na_from_merge <- function(inner_dims_across_files, final_dims, across_inner_dim,
length_inner_across_dim, data_array) {
across_file_dim <- names(inner_dims_across_files) #TODO: more than one?
# Get the length of these two dimensions in final_dims
length_inner_across_store_dims <- final_dims[across_inner_dim]
length_file_across_store_dims <- final_dims[across_file_dim]
# Create a logical array for merge_across_dims
logi_array <- array(rep(FALSE,
length_file_across_store_dims * length_inner_across_store_dims),
dim = c(length_inner_across_store_dims, length_file_across_store_dims))
for (i in 1:length_file_across_store_dims) { #1:4
logi_array[1:length_inner_across_dim[[i]], i] <- TRUE
}
# First, get the data array with final_dims dimension
data_array_final_dims <- array(bigmemory::as.matrix(data_array), dim = final_dims)
# Change the NA derived from additional spaces to -9999, then remove these -9999
func_remove_blank <- function(data_array, logi_array) {
# dim(data_array) = [time, file_date]
# dim(logi_array) = [time, file_date]
# Change the blank spaces from NA to -9999
data_array[which(!logi_array)] <- -9999
return(data_array)
}
data_array_final_dims <- multiApply::Apply(data_array_final_dims,
target_dims = c(across_inner_dim, across_file_dim), #c('time', 'file_date')
output_dims = c(across_inner_dim, across_file_dim),