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}
}
# HERE change to the same code as below (under 'else'). Not sure why originally
#it uses additional lines, which make reorder not work.
sub_array_of_selectors <- as.list(dim_reorder_params[[inner_dim]](unlist(sub_array_of_selectors))$x)
#sub_array_reordered <- dim_reorder_params[[inner_dim]](unlist(sub_array_of_selectors))
#sub_array_unorder <- sort(sub_array_reordered$ix, index.return = TRUE)$ix
#sub_array_of_selectors <- as.list(sub_array_reordered$x[sub_array_unorder])
# Add warning if the boundary is out of range
if (sub_array_of_selectors[1] < range(var_ordered)[1] | sub_array_of_selectors[1] > range(var_ordered)[2]) {
.warning(paste0("The lower boundary of selector of ",
inner_dim,
" is out of range [",
min(var_ordered), ", ", max(var_ordered), "]. ",
"Check if the desired range is all included."))
}
if (sub_array_of_selectors[2] < range(var_ordered)[1] | sub_array_of_selectors[2] > range(var_ordered)[2]) {
.warning(paste0("The upper boundary of selector of ",
inner_dim,
" is out of range [",
min(var_ordered), ", ", max(var_ordered), "]. ",
"Check if the desired range is all included."))
}
} else {
sub_array_of_selectors <- dim_reorder_params[[inner_dim]](sub_array_of_selectors)$x
}
}
# NOTE: The ideal solution for selecting indices in goes_across_prime_meridian case
# is modified SelectorCheckor.R. But now SelectorCheckor doesn't know the info of
#goes_across_prime_meridian, so I do the adjustion after calling SelectorCheckor().
sub_array_of_indices <- selector_checker(sub_array_of_selectors, var_ordered,
tolerance = if (aiat) {
NULL
} else {
tolerance_params[[inner_dim]]
})
if (goes_across_prime_meridian & sub_array_of_indices[[1]] < sub_array_of_indices[[2]]) {
if (!(sub_array_of_selectors[[1]] %in% var_ordered)){
sub_array_of_indices[[1]] <- sub_array_of_indices[[1]] - 1
}
if (!(sub_array_of_selectors[[2]] %in% var_ordered)){
sub_array_of_indices[[2]] <- sub_array_of_indices[[2]] + 1
}
}
#NOTE: the possible case?
if (goes_across_prime_meridian & sub_array_of_indices[[1]] > sub_array_of_indices[[2]]) {
.stop("The case is goes_across_prime_meridian but no adjustion for the indices!")
}
if (any(is.na(sub_array_of_indices))) {
stop(paste0("The selectors of ", inner_dim,
" are out of range [", min(var_ordered),
", ", max(var_ordered), "]."))
}
} else {
# Add warning if the boundary is out of range
if (is.list(sub_array_of_selectors)) {
if (sub_array_of_selectors[1] <
min(sub_array_of_values) | sub_array_of_selectors[1] >
max(sub_array_of_values)) {
.warning(paste0("The lower boundary of selector of ",
inner_dim, " is out of range [",
min(sub_array_of_values), ", ",
max(sub_array_of_values), "]. ",
"Check if the desired range is all included."))
}
if (sub_array_of_selectors[2] <
min(sub_array_of_values) | sub_array_of_selectors[2] >
max(sub_array_of_values)) {
.warning(paste0("The upper boundary of selector of ",
inner_dim, " is out of range [",
min(sub_array_of_values), ", ",
max(sub_array_of_values), "]. ",
"Check if the desired range is all included."))
}
}
sub_array_of_indices <- selector_checker(sub_array_of_selectors, sub_array_of_values,
tolerance = if (aiat) {
NULL
} else {
tolerance_params[[inner_dim]]
})
if (any(is.na(sub_array_of_indices))) {
stop(paste0("The selectors of ", inner_dim,
" are out of range [", min(sub_array_of_values),
", ", max(sub_array_of_values), "]."))
}
}
## This 'if' runs in both Start() and Compute(). In Start(), it doesn't have any effect (no chunk).
## In Compute(), it creates the indices for each chunk. For example, if 'sub_array_of_indices'
## is c(5:10) and chunked into 2, 'sub_array_of_indices' becomes c(5:7) for chunk = 1, c(8:10)
## for chunk = 2. If 'sub_array_of_indices' is list(55, 62) and chunked into 2, it becomes
## list(55, 58) for chunk = 1 and list(59, 62) for chunk = 2.
## TODO: The list can be turned into vector here? So afterward no need to judge if it is list
## or vector.
if (!is.list(sub_array_of_indices)) {
sub_array_of_indices <- sub_array_of_indices[chunk_indices(length(sub_array_of_indices),
chunks[[inner_dim]]["chunk"],
chunks[[inner_dim]]["n_chunks"],
inner_dim)]
} else {
tmp <- chunk_indices(length(sub_array_of_indices[[1]]:sub_array_of_indices[[2]]),
chunks[[inner_dim]]["chunk"], chunks[[inner_dim]]["n_chunks"],
inner_dim)
vect <- sub_array_of_indices[[1]]:sub_array_of_indices[[2]]
sub_array_of_indices[[1]] <- vect[tmp[1]]
sub_array_of_indices[[2]] <- vect[tmp[length(tmp)]]
}
# The sub_array_of_indices now contains numeric indices of the values to be taken by each chunk.
# Check if all the files have the selectors assigned (e.g., region = 'Grnland') _20191015
if (is.character(sub_array_of_selectors)) {
array_of_var_files_check <- vector('list', length(selector_indices))
for (k in 1:length(selector_indices)) {
asdasd <- selector_indices[[k]]
array_of_var_files_check <- do.call('[', c(list(x = array_of_files_to_load), asdasd, list(drop = FALSE)))[j]
file_object <- file_opener(array_of_var_files_check)
var_values_check <- file_var_reader(NULL, file_object, NULL, var_to_read, synonims)
if (any(as.vector(var_values_check)[sub_array_of_indices] != sub_array_of_selectors)) {
.warning('Not all the files has correponding selectors. Check the selector attributes')
}
}
}
if (debug) {
if (inner_dim %in% dims_to_check) {
print("-> TRANSFORMATION REQUESTED?")
print(with_transform)
print("-> BETA:")
print(beta)
}
}
if (with_transform) {
# If there is a transformation and selector values are provided, these
# selectors will be processed in the same way either if aiat = TRUE or
# aiat = FALSE.
## TODO: If sub_array_of_selectors was integer and aiat then... do what's commented 50 lines below.
## otherwise, do what's coded.
if (debug) {
if (inner_dim %in% dims_to_check) {
print("-> SELECTORS REQUESTED BEFORE TRANSFORM.")
}
}
###NOTE: Here, the transform, is different from the below part of non-transform.
# search 'if (goes_across_prime_meridian' to find the lines below.
if (goes_across_prime_meridian) {
# NOTE: before changing, the return is already correct.
#NOTE: The fix below has the same explanation as no with_transform part below.
# Search the next next 'if (goes_across_prime_meridian) {'.
if (sub_array_of_indices[[1]] == sub_array_of_indices[[2]]) {
# global longitude
sub_array_of_fri <- 1:n
# Warning if transform_extra_cell != 0
if (beta != 0) {
.warning(paste0("Adding parameter transform_extra_cells = ",
transform_extra_cells, " to the transformed index excesses ",
"the border. The border index is used for transformation."))
}
} else {
# normal case, i.e., not global
first_index <- min(unlist(sub_array_of_indices))
last_index <- max(unlist(sub_array_of_indices))
gap_width <- last_index - first_index - 1
sub_array_of_fri <- c(1:(min(unlist(sub_array_of_indices)) + min(gap_width, beta)),
(max(unlist(sub_array_of_indices)) - min(gap_width, beta)):n)
if (min(gap_width, beta) != beta) {
.warning(paste0("Adding parameter transform_extra_cells = ",
transform_extra_cells, " to the transformed index excesses ",
"the border. The border index is used for transformation."))
}
}
} 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]]
}
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 (exists("is_circular_dim")) {
if (!is_circular_dim) { #latitude
sub_array_of_fri <- (first_index - start_padding):(last_index + end_padding)
if (start_padding != beta | end_padding != beta) {
.warning(paste0("Adding parameter transform_extra_cells = ",
transform_extra_cells, " to the transformed index excesses ",
"the border. The border index is used for transformation."))
}
} else { #longitude
if ((last_index - first_index + 1 + beta * 2) >= n) {
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 { # 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) {
.warning(paste0("Adding parameter transform_extra_cells = ",
transform_extra_cells, " to the transformed index excesses ",
"the border. The border index is used for transformation."))
}
}
}
subset_vars_to_transform <- vars_to_transform
if (!is.null(var_ordered)) {
##NOTE: if var_ordered is common_vars, it doesn't have attributes and it is a vector.
## Turn it into array and add dimension name.
if (!is.array(var_ordered)) {
var_ordered <- as.array(var_ordered)
names(dim(var_ordered)) <- inner_dim
}
subset_vars_to_transform[[var_with_selectors_name]] <- Subset(var_ordered, inner_dim, sub_array_of_fri)
} else {
##NOTE: It should be redundant because without reordering the var should remain array
## But just stay same with above...
if (!is.array(sub_array_of_values)) {
sub_array_of_values <- as.array(sub_array_of_values)
names(dim(sub_array_of_values)) <- inner_dim
}
subset_vars_to_transform[[var_with_selectors_name]] <- Subset(sub_array_of_values, inner_dim, sub_array_of_fri)
}
# Change the order of longitude crop if no reorder + from big to small.
# cdo -sellonlatbox, the lon is west, east (while lat can be north
# to south or opposite)
# Before changing crop, first we need to find the name of longitude.
# NOTE: The potential bug here (also the bug for CDORemapper): the lon name
# is limited (only the ones listed in .KnownLonNames() are available.
known_lon_names <- s2dverification:::.KnownLonNames()
lon_name <- names(subset_vars_to_transform)[which(names(subset_vars_to_transform) %in% known_lon_names)[1]]
# NOTE: The cases not considered: (1) if lon reorder(decreasing = T)
# It doesn't make sense, but if someone uses it, here should
# occur error. (2) crop = TRUE/FALSE
if ('crop' %in% names(transform_params) & var_with_selectors_name == lon_name & is.null(dim_reorder_params[[inner_dim]])) {
if (is.numeric(class(transform_params$crop))) {
if (transform_params$crop[1] > transform_params$crop[2]) {
tmp <- transform_params$crop[1]
transform_params$crop[1] <- transform_params$crop[2]
transform_params$crop[2] <- tmp
}
}
}
transformed_subset_var <- do.call(transform, c(list(data_array = NULL,
variables = subset_vars_to_transform,
file_selectors = selectors_of_first_files_with_data[[i]]),
transform_params))$variables[[var_with_selectors_name]]
# Sorting the transformed variable and working out the indices again after transform.
if (!is.null(dim_reorder_params[[inner_dim]])) {
transformed_subset_var_reorder <- dim_reorder_params[[inner_dim]](transformed_subset_var)
transformed_subset_var <- transformed_subset_var_reorder$x
#NOTE: The fix here solves the mis-ordered lon when across_meridian.
transformed_subset_var_unorder <- transformed_subset_var_reorder$ix
# transformed_subset_var_unorder <- sort(transformed_subset_var_reorder$ix, index.return = TRUE)$ix
} else {
transformed_subset_var_unorder <- 1:length(transformed_subset_var)
}
sub_array_of_sri <- selector_checker(sub_array_of_selectors, transformed_subset_var,
tolerance = if (aiat) {
tolerance_params[[inner_dim]]
} else {
NULL
})
# Check if selectors fall out of the range of the transform grid
# It may happen when original lon is [-180, 180] while want to regrid to
# [0, 360], and lon selector = [-20, -10].
if (any(is.na(sub_array_of_sri))) {
stop(paste0("The selectors of ",
inner_dim, " are out of range of transform grid '",
transform_params$grid, "'. Use parameter '",
inner_dim, "_reorder' or change ", inner_dim,
" selectors."))
}
if (goes_across_prime_meridian) {
if (sub_array_of_sri[[1]] == sub_array_of_sri[[2]]) {
# global longitude
sub_array_of_sri <- c(1:length(transformed_subset_var))
} else {
# the common case, i.e., non-global
# NOTE: Because sub_array_of_sri order is exchanged due to
# previous development, here [[1]] and [[2]] should exchange
sub_array_of_sri <- c(1:sub_array_of_sri[[1]],
sub_array_of_sri[[2]]:length(transformed_subset_var))
}
} else if (is.list(sub_array_of_sri)) {
sub_array_of_sri <- sub_array_of_sri[[1]]:sub_array_of_sri[[2]]
}
ordered_sri <- sub_array_of_sri
sub_array_of_sri <- transformed_subset_var_unorder[sub_array_of_sri]
# In this case, the tvi are not defined and the 'transformed_subset_var'
# will be taken instead of the var transformed before in the code.
if (debug) {
if (inner_dim %in% dims_to_check) {
print("-> FIRST INDEX:")
print(first_index)
print("-> LAST INDEX:")
print(last_index)
print("-> STRUCTURE OF FIRST ROUND INDICES:")
print(str(sub_array_of_fri))
print("-> STRUCTURE OF SECOND ROUND INDICES:")
print(str(sub_array_of_sri))
print("-> STRUCTURE OF TRANSFORMED VARIABLE INDICES:")
print(str(tvi))
}
}
### # If the selectors are expressed after transformation
### } else {
###if (debug) {
###if (inner_dim %in% dims_to_check) {
###print("-> SELECTORS REQUESTED AFTER TRANSFORM.")
###}
###}
### if (goes_across_prime_meridian) {
### sub_array_of_indices <- c(sub_array_of_indices[[1]]:m,
### 1:sub_array_of_indices[[2]])
### }
### first_index <- min(unlist(sub_array_of_indices))
### last_index <- max(unlist(sub_array_of_indices))
### first_index_before_transform <- max(transform_indices(first_index, m, n) - beta, 1)
### last_index_before_transform <- min(transform_indices(last_index, m, n) + beta, n)
### sub_array_of_fri <- first_index_before_transform:last_index_before_transform
### n_of_extra_cells <- round(beta / n * m)
### if (is.list(sub_array_of_indices) && (length(sub_array_of_indices) > 1)) {
### sub_array_of_sri <- 1:(last_index - first_index + 1)
### if (is.null(tvi)) {
### tvi <- sub_array_of_sri + first_index - 1
### }
### } else {
### sub_array_of_sri <- sub_array_of_indices - first_index + 1
### if (is.null(tvi)) {
### tvi <- sub_array_of_indices
### }
### }
### sub_array_of_sri <- sub_array_of_sri + n_of_extra_cells
sri <- do.call('[[<-', c(list(x = sri), as.list(selector_store_position),
list(value = sub_array_of_sri)))
} else {
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 <- c(1:n)
} else {
# the common case, i.e., non-global
sub_array_of_fri <- c(1:min(unlist(sub_array_of_indices)),
max(unlist(sub_array_of_indices)):n)
}
} 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 (!is.null(var_unorder_indices)) {
if (is.null(ordered_fri)) {
ordered_fri <- sub_array_of_fri
}
sub_array_of_fri <- var_unorder_indices[sub_array_of_fri]
}
fri <- do.call('[[<-', c(list(x = fri), as.list(selector_store_position),
list(value = sub_array_of_fri)))
if (!is.null(file_dim)) {
taken_chunks[selector_store_position[[file_dim]]] <- TRUE
} else {
taken_chunks <- TRUE
}
}
} else {
if (debug) {
if (inner_dim %in% dims_to_check) {
print("-> THE INNER DIMENSION GOES ACROSS A FILE DIMENSION.")
}
}
if (inner_dim %in% names(dim(sub_array_of_selectors))) {
if (is.null(var_with_selectors_name)) {
if (any(na.omit(unlist(sub_array_of_selectors)) < 1) ||
any(na.omit(unlist(sub_array_of_selectors)) > data_dims[inner_dim] * chunk_amount)) {
stop("Provided indices out of range for dimension '", inner_dim, "' ",
"for dataset '", dat[[i]][['name']], "' (accepted range: 1 to ",
data_dims[inner_dim] * chunk_amount, ").")
}
} else {
if (inner_dim %in% names(dim(sub_array_of_values))) {
# NOTE: Put across-inner-dim at the 1st position.
# POSSIBLE PROB!! Only organize inner dim, the rest dims may not in the same order as sub_array_of_selectors below.
inner_dim_pos_in_sub_array <- which(names(dim(sub_array_of_values)) == inner_dim)
if (inner_dim_pos_in_sub_array != 1) {
new_sub_array_order <- (1:length(dim(sub_array_of_values)))[-inner_dim_pos_in_sub_array]
new_sub_array_order <- c(inner_dim_pos_in_sub_array, new_sub_array_order)
sub_array_of_values <- .aperm2(sub_array_of_values, new_sub_array_order)
}
}
}
# NOTE: Put across-inner-dim at the 1st position.
# POSSIBLE PROB!! Only organize inner dim, the rest dims may not in the same order as sub_array_of_values above.
inner_dim_pos_in_sub_array <- which(names(dim(sub_array_of_selectors)) == inner_dim)
if (inner_dim_pos_in_sub_array != 1) {
new_sub_array_order <- (1:length(dim(sub_array_of_selectors)))[-inner_dim_pos_in_sub_array]
new_sub_array_order <- c(inner_dim_pos_in_sub_array, new_sub_array_order)
sub_array_of_selectors <- .aperm2(sub_array_of_selectors, new_sub_array_order)
}
sub_array_of_indices <- selector_checker(sub_array_of_selectors, sub_array_of_values,
tolerance = tolerance_params[[inner_dim]])
# It is needed to expand the indices here, otherwise for
# values(list(date1, date2)) only 2 values are picked.
if (is.list(sub_array_of_indices)) {
sub_array_of_indices <- sub_array_of_indices[[1]]:sub_array_of_indices[[2]]
}
sub_array_of_indices <- sub_array_of_indices[chunk_indices(length(sub_array_of_indices),
chunks[[inner_dim]]['chunk'],
chunks[[inner_dim]]['n_chunks'],
inner_dim)]
sub_array_is_list <- FALSE
if (is.list(sub_array_of_indices)) {
sub_array_is_list <- TRUE
sub_array_of_indices <- unlist(sub_array_of_indices)
}
if (is.null(var_with_selectors_name)) {
indices_chunk <- floor((sub_array_of_indices - 1) / data_dims[inner_dim]) + 1
transformed_indices <- ((sub_array_of_indices - 1) %% data_dims[inner_dim]) + 1
} else {
indices_chunk <- floor((sub_array_of_indices - 1) / var_full_dims[inner_dim]) + 1
transformed_indices <- ((sub_array_of_indices - 1) %% var_full_dims[inner_dim]) + 1
}
if (sub_array_is_list) {
sub_array_of_indices <- as.list(sub_array_of_indices)
}
if (debug) {
if (inner_dim %in% dims_to_check) {
print("-> GOING TO ITERATE ALONG CHUNKS.")
}
}
for (chunk in 1:chunk_amount) {
if (!is.null(names(selector_store_position))) {
selector_store_position[file_dim] <- chunk
} else {
selector_store_position <- chunk
}
chunk_selectors <- transformed_indices[which(indices_chunk == chunk)]
sub_array_of_indices <- chunk_selectors
if (with_transform) {
# If the provided selectors are expressed in the world
# before transformation
if (!aiat) {
first_index <- min(unlist(sub_array_of_indices))
last_index <- max(unlist(sub_array_of_indices))
sub_array_of_fri <- max(c(first_index - beta, 1)):min(c(last_index + beta, n))
sub_array_of_sri <- transform_indices(unlist(sub_array_of_indices) - first_index + 1, n, m)
if (is.list(sub_array_of_indices)) {
if (length(sub_array_of_sri) > 1) {
sub_array_of_sri <- sub_array_of_sri[[1]]:sub_array_of_sri[[2]]
}
}
##TODO: TRANSFORM SUBSET VARIABLE AS ABOVE, TO COMPUTE SRI
# If the selectors are expressed after transformation
} else {
first_index <- min(unlist(sub_array_of_indices))
last_index <- max(unlist(sub_array_of_indices))
first_index_before_transform <- max(transform_indices(first_index, m, n) - beta, 1)
last_index_before_transform <- min(transform_indices(last_index, m, n) + beta, n)
sub_array_of_fri <- first_index_before_transform:last_index_before_transform
if (is.list(sub_array_of_indices) && (length(sub_array_of_indices) > 1)) {
sub_array_of_sri <- 1:(last_index - first_index + 1) +
round(beta / n * m)
} else {
sub_array_of_sri <- sub_array_of_indices - first_index + 1 +
round(beta / n * m)
}
##TODO: FILL IN TVI
}
sri <- do.call('[[<-', c(list(x = sri), as.list(selector_store_position),
list(value = sub_array_of_sri)))
if (length(sub_array_of_sri) > 0) {
taken_chunks[chunk] <- TRUE
}
} else {
sub_array_of_fri <- sub_array_of_indices
if (length(sub_array_of_fri) > 0) {
taken_chunks[chunk] <- TRUE
}
}
if (!is.null(var_unorder_indices)) {
ordered_fri <- sub_array_of_fri
sub_array_of_fri <- var_unorder_indices[sub_array_of_fri]
}
fri <- do.call('[[<-', c(list(x = fri), as.list(selector_store_position),
list(value = sub_array_of_fri)))
}
if (debug) {
if (inner_dim %in% dims_to_check) {
print("-> FINISHED ITERATING ALONG CHUNKS")
}
}
} else {
stop("Provided array of indices for dimension '", inner_dim, "', ",
"which goes across the file dimension '", file_dim, "', but ",
"the provided array does not have the dimension '", inner_dim,
"', which is mandatory.")
}
}
}
}
if (debug) {
if (inner_dim %in% dims_to_check) {
print("-> PROCEEDING TO CROP VARIABLES")
}
}
#if ((length(selector_array) == 1) && (selector_array %in% c('all', 'first', 'last'))) {
#if (!is.null(var_with_selectors_name) || (is.null(var_with_selectors_name) && is.character(selector_array) &&
# (length(selector_array) == 1) && (selector_array %in% c('all', 'first', 'last')))) {
empty_chunks <- which(!taken_chunks)
if (length(empty_chunks) >= length(taken_chunks)) {
stop("Selectors do not match any of the possible values for the dimension '", inner_dim, "'.")
}
if (length(empty_chunks) > 0) {
# # Get the first group of chunks to remove, and remove them.
# # E.g., from c(1, 2, 4, 5, 6, 8, 9) remove only 1 and 2
# dist <- abs(rev(empty_chunks) - c(rev(empty_chunks)[1] - 1, head(rev(empty_chunks), length(rev(empty_chunks)) - 1)))
# if (all(dist == 1)) {
# start_chunks_to_remove <- NULL
# } else {
# first_chunk_to_remove <- tail(which(dist > 1), 1)
# start_chunks_to_remove <- rev(rev(empty_chunks)[first_chunk_to_remove:length(empty_chunks)])
# }
# # Get the last group of chunks to remove, and remove them.
# # E.g., from c(1, 2, 4, 5, 6, 8, 9) remove only 8 and 9
# dist <- abs(empty_chunks - c(empty_chunks[1] - 1, head(empty_chunks, length(empty_chunks) - 1)))
# if (all(dist == 1)) {
# first_chunk_to_remove <- 1
# } else {
# first_chunk_to_remove <- tail(which(dist > 1), 1)
# }
# end_chunks_to_remove <- empty_chunks[first_chunk_to_remove:length(empty_chunks)]
# chunks_to_keep <- which(!((1:length(taken_chunks)) %in% c(start_chunks_to_remove, end_chunks_to_remove)))
chunks_to_keep <- which(taken_chunks)
dims_to_crop[[file_dim]] <- c(dims_to_crop[[file_dim]], list(chunks_to_keep))
# found_indices <- Subset(found_indices, file_dim, chunks_to_keep)
# # Crop dataset variables file dims.
# for (picked_var in names(picked_vars[[i]])) {
# if (file_dim %in% names(dim(picked_vars[[i]][[picked_var]]))) {
# picked_vars[[i]][[picked_var]] <- Subset(picked_vars[[i]][[picked_var]], file_dim, chunks_to_keep)
# }
# }
}
#}
dat[[i]][['selectors']][[inner_dim]] <- list(fri = fri, sri = sri)
# Crop dataset variables inner dims.
# Crop common variables inner dims.
types_of_var_to_crop <- 'picked'
if (with_transform) {
types_of_var_to_crop <- c(types_of_var_to_crop, 'transformed')
}
if (!is.null(dim_reorder_params[[inner_dim]])) {
types_of_var_to_crop <- c(types_of_var_to_crop, 'reordered')
}
for (type_of_var_to_crop in types_of_var_to_crop) {
if (type_of_var_to_crop == 'transformed') {
if (is.null(tvi)) {
if (!is.null(dim_reorder_params[[inner_dim]])) {
crop_indices <- unique(unlist(ordered_sri))
} else {
crop_indices <- unique(unlist(sri))
}
} else {
crop_indices <- unique(unlist(tvi))
}
vars_to_crop <- transformed_vars[[i]]
common_vars_to_crop <- transformed_common_vars
} else if (type_of_var_to_crop == 'reordered') {
crop_indices <- unique(unlist(ordered_fri))
vars_to_crop <- picked_vars_ordered[[i]]
common_vars_to_crop <- picked_common_vars_ordered
} else {
crop_indices <- unique(unlist(fri))
vars_to_crop <- picked_vars[[i]]
common_vars_to_crop <- picked_common_vars
}
for (var_to_crop in names(vars_to_crop)) {
if (inner_dim %in% names(dim(vars_to_crop[[var_to_crop]]))) {
if (!is.null(crop_indices)) {
if (type_of_var_to_crop == 'transformed') {
if (!aiat) {
vars_to_crop[[var_to_crop]] <- Subset(transformed_subset_var, inner_dim, crop_indices)
} else {
vars_to_crop[[var_to_crop]] <- Subset(vars_to_crop[[var_to_crop]], inner_dim, crop_indices)
}
} else {
vars_to_crop[[var_to_crop]] <- Subset(vars_to_crop[[var_to_crop]], inner_dim, crop_indices)
}
}
}
}
if (i == length(dat)) {
for (common_var_to_crop in names(common_vars_to_crop)) {
if (inner_dim %in% names(dim(common_vars_to_crop[[common_var_to_crop]]))) {
common_vars_to_crop[[common_var_to_crop]] <- Subset(common_vars_to_crop[[common_var_to_crop]], inner_dim, crop_indices)
}
}
}
if (type_of_var_to_crop == 'transformed') {
if (!is.null(vars_to_crop)) {
transformed_vars[[i]] <- vars_to_crop
}
if (i == length(dat)) {
transformed_common_vars <- common_vars_to_crop
}
} else if (type_of_var_to_crop == 'reordered') {
if (!is.null(vars_to_crop)) {
picked_vars_ordered[[i]] <- vars_to_crop
}
if (i == length(dat)) {
picked_common_vars_ordered <- common_vars_to_crop
}
} else {
if (!is.null(vars_to_crop)) {
picked_vars[[i]] <- vars_to_crop
}
if (i == length(dat)) {
picked_common_vars <- common_vars_to_crop
}
}
}
#}
}
# After the selectors have been picked (using the original variables),
# the variables are transformed. At that point, the original selectors
# for the transformed variables are also kept in the variable original_selectors.
#print("L")
}
}
}
# if (!is.null(transformed_common_vars)) {
# picked_common_vars[names(transformed_common_vars)] <- transformed_common_vars
# }
# Remove the trailing chunks, if any.
for (file_dim in names(dims_to_crop)) {
# indices_to_keep <- min(sapply(dims_to_crop[[file_dim]], min)):max(sapply(dims_to_crop[[file_dim]], max))
## TODO: Merge indices in dims_to_crop with some advanced mechanism?
indices_to_keep <- unique(unlist(dims_to_crop[[file_dim]]))
array_of_files_to_load <- Subset(array_of_files_to_load, file_dim, indices_to_keep)
array_of_not_found_files <- Subset(array_of_not_found_files, file_dim, indices_to_keep)
for (i in 1:length(dat)) {
# Crop selectors
for (selector_dim in names(dat[[i]][['selectors']])) {
if (selector_dim == file_dim) {
for (j in 1:length(dat[[i]][['selectors']][[selector_dim]][['fri']])) {
dat[[i]][['selectors']][[selector_dim]][['fri']][[j]] <- dat[[i]][['selectors']][[selector_dim]][['fri']][[j]][indices_to_keep]
}
for (j in 1:length(dat[[i]][['selectors']][[selector_dim]][['sri']])) {
dat[[i]][['selectors']][[selector_dim]][['sri']][[j]] <- dat[[i]][['selectors']][[selector_dim]][['sri']][[j]][indices_to_keep]
}
}
if (file_dim %in% names(dim(dat[[i]][['selectors']][[selector_dim]][['fri']]))) {
dat[[i]][['selectors']][[selector_dim]][['fri']] <- Subset(dat[[i]][['selectors']][[selector_dim]][['fri']], file_dim, indices_to_keep)
dat[[i]][['selectors']][[selector_dim]][['sri']] <- Subset(dat[[i]][['selectors']][[selector_dim]][['sri']], file_dim, indices_to_keep)
}
}
# Crop dataset variables file dims.
for (picked_var in names(picked_vars[[i]])) {
if (file_dim %in% names(dim(picked_vars[[i]][[picked_var]]))) {
picked_vars[[i]][[picked_var]] <- Subset(picked_vars[[i]][[picked_var]], file_dim, indices_to_keep)
}
}
for (transformed_var in names(transformed_vars[[i]])) {
if (file_dim %in% names(dim(transformed_vars[[i]][[transformed_var]]))) {
transformed_vars[[i]][[transformed_var]] <- Subset(transformed_vars[[i]][[transformed_var]], file_dim, indices_to_keep)
}
}
}
# Crop common variables file dims.
for (picked_common_var in names(picked_common_vars)) {
if (file_dim %in% names(dim(picked_common_vars[[picked_common_var]]))) {
picked_common_vars[[picked_common_var]] <- Subset(picked_common_vars[[picked_common_var]], file_dim, indices_to_keep)
}
}
for (transformed_common_var in names(transformed_common_vars)) {
if (file_dim %in% names(dim(transformed_common_vars[[transformed_common_var]]))) {
transformed_common_vars[[transformed_common_var]] <- Subset(transformed_common_vars[[transformed_common_var]], file_dim, indices_to_keep)
}
}
}
# Calculate the size of the final array.
total_inner_dims <- NULL
for (i in 1:length(dat)) {
if (dataset_has_files[i]) {
inner_dims <- expected_inner_dims[[i]]
inner_dims <- sapply(inner_dims,
function(x) {
if (!all(sapply(dat[[i]][['selectors']][[x]][['sri']], is.null))) {
max(sapply(dat[[i]][['selectors']][[x]][['sri']], length))
} else {
if (length(var_params[[x]]) > 0) {
if (var_params[[x]] %in% names(transformed_vars[[i]])) {
length(transformed_vars[[i]][[var_params[[x]]]])
} else if (var_params[[x]] %in% names(transformed_common_vars)) {
length(transformed_common_vars[[var_params[[x]]]])
} else {
max(sapply(dat[[i]][['selectors']][[x]][['fri']], length))
}
} else {
max(sapply(dat[[i]][['selectors']][[x]][['fri']], length))
}
}
})
names(inner_dims) <- expected_inner_dims[[i]]
if (is.null(total_inner_dims)) {
total_inner_dims <- inner_dims
} else {
new_dims <- .MergeArrayDims(total_inner_dims, inner_dims)
total_inner_dims <- new_dims[[3]]
}
}
}
new_dims <- .MergeArrayDims(dim(array_of_files_to_load), total_inner_dims)
final_dims <- new_dims[[3]][dim_names]
# final_dims_fake is the vector of final dimensions after having merged the
# 'across' file dimensions with the respective 'across' inner dimensions, and
# after having broken into multiple dimensions those dimensions for which
# multidimensional selectors have been provided.
# final_dims will be used for collocation of data, whereas final_dims_fake
# will be used for shaping the final array to be returned to the user.
final_dims_fake <- final_dims
if (merge_across_dims) {
if (!is.null(inner_dims_across_files)) {
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()
if (inner_dim_pos > 1) {
new_dims <- c(new_dims, final_dims_fake[1:(inner_dim_pos - 1)])
}
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]]))
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
}
}
}
all_split_dims <- NULL
if (split_multiselected_dims) {
for (dim_param in 1:length(dim_params)) {
if (!is.null(dim(dim_params[[dim_param]]))) {
if (length(dim(dim_params[[dim_param]])) > 1) {
split_dims <- dim(dim_params[[dim_param]])
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])
# 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
}
}
}
}
if (merge_across_dims_narm) {
# only merge_across_dims -> the 'time' dim length needs to be adjusted
across_inner_dim <- inner_dims_across_files[[1]] #TODO: more than one?
across_file_dim <- names(inner_dims_across_files) #TODO: more than one?
# Get the length of each inner_dim ('time') along each file_dim ('file_date')
length_inner_across_dim <- lapply(dat[[i]][['selectors']][[across_inner_dim]][['fri']], length)
if (!split_multiselected_dims) {
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
}
}
if (!silent) {
.message("Detected dimension sizes:")
longest_dim_len <- max(sapply(names(final_dims_fake), nchar))
longest_size_len <- max(sapply(paste0(final_dims_fake, ''), nchar))
sapply(names(final_dims_fake),
function(x) {
message(paste0("* ", paste(rep(' ', longest_dim_len - nchar(x)), collapse = ''),
x, ": ", paste(rep(' ', longest_size_len - nchar(paste0(final_dims_fake[x], ''))), collapse = ''),
final_dims_fake[x]))
})
bytes <- prod(c(final_dims_fake, 8))
dim_sizes <- paste(final_dims_fake, collapse = ' x ')
if (retrieve) {
.message(paste("Total size of requested data:"))
} else {
.message(paste("Total size of involved data:"))
}
.message(paste(dim_sizes, " x 8 bytes =",
format(structure(bytes, class = "object_size"), units = "auto")),
indent = 2)
}
# NOTE: 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.
# Cannot control the rest dims are in the same order or not...
# Suppose users put the same order of across inner and file dims.
if (split_multiselected_dims & merge_across_dims) {
# TODO: More than one split?
inner_dim_pos_in_split_dims <- which(names(all_split_dims[[1]]) == inner_dims_across_files)
# if inner_dim is not the first, change!
if (inner_dim_pos_in_split_dims != 1) {
split_dims <- c(split_dims[inner_dim_pos_in_split_dims],
split_dims[1:length(split_dims)][-inner_dim_pos_in_split_dims])
split_dims_pos <- which(!is.na(match(names(final_dims_fake), names(split_dims))))
# Save the current final_dims_fake for later reorder back
final_dims_fake_output <- final_dims_fake
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, 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
}
}
# The following several lines will only be run if retrieve = TRUE
if (retrieve) {
########## CREATING THE SHARED MATRIX AND DISPATCHING WORK PIECES ###########
# TODO: try performance of storing all in cols instead of rows
# Create the shared memory array, and a pointer to it, to be sent
# to the work pieces.
data_array <- big.matrix(nrow = prod(final_dims), ncol = 1)
shared_matrix_pointer <- describe(data_array)
if (is.null(num_procs)) {
num_procs <- availableCores()
}
# Creating a shared tmp folder to store metadata from each chunk
array_of_metadata_flags <- array(FALSE, dim = dim(array_of_files_to_load))
if (!is.null(metadata_dims)) {
metadata_indices_to_load <- as.list(rep(1, length(dim(array_of_files_to_load))))
names(metadata_indices_to_load) <- names(dim(array_of_files_to_load))
metadata_indices_to_load[metadata_dims] <- as.list(rep(TRUE, length(metadata_dims)))
array_of_metadata_flags <- do.call('[<-', c(list(array_of_metadata_flags), metadata_indices_to_load,
list(value = rep(TRUE, prod(dim(array_of_files_to_load)[metadata_dims])))))
}
metadata_file_counter <- 0
metadata_folder <- tempfile('metadata')
dir.create(metadata_folder)
# Build the work pieces, each with:
# - file path
# - total size (dims) of store array
# - start position in store array
# - file selectors (to provide extra info. useful e.g. to select variable)
# - indices to take from file
work_pieces <- list()
for (i in 1:length(dat)) {
if (dataset_has_files[i]) {
selectors <- dat[[i]][['selectors']]
file_dims <- found_file_dims[[i]]
inner_dims <- expected_inner_dims[[i]]
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
}
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]])) {
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]])) {
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)) {