Newer
Older
#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), "]."))
}
2058
2059
2060
2061
2062
2063
2064
2065
2066
2067
2068
2069
2070
2071
2072
2073
2074
2075
2076
2077
2078
2079
2080
# 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."))
}
#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 (!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)
}
aho
committed
# 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)
aho
committed
# 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$crop[2] <- tmp
}
aho
committed
}
}
transformed_subset_var <- do.call(transform, c(list(data_array = NULL,
variables = subset_vars_to_transform,
file_selectors = selectors_of_first_files_with_data[[i]]),
transform_params))$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
})
aho
committed
# 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))
}
2314
2315
2316
2317
2318
2319
2320
2321
2322
2323
2324
2325
2326
2327
2328
2329
2330
2331
2332
2333
2334
2335
2336
2337
2338
2339
2340
2341
2342
2343
2344
2345
2346
2347
2348
2349
2350
2351
2352
2353
2354
2355
2356
2357
2358
2359
2360
2361
2362
2363
2364
2365
2366
2367
} 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)) {
2386
2387
2388
2389
2390
2391
2392
2393
2394
2395
2396
2397
2398
2399
2400
2401
2402
2403
2404
2405
2406
2407
2408
2409
2410
2411
2412
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))) {
aho
committed
# 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)
}
}
}
aho
committed
# 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)]
2451
2452
2453
2454
2455
2456
2457
2458
2459
2460
2461
2462
2463
2464
2465
2466
2467
2468
2469
2470
2471
2472
2473
2474
2475
2476
2477
2478
2479
2480
2481
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
2483
2484
2485
2486
2487
2488
2489
2490
2491
2492
2493
2494
2495
2496
2497
2498
2499
2500
2501
2502
2503
2504
2505
2506
2507
2508
2509
2510
2511
2512
2513
2514
2515
2516
2517
2518
2519
2520
2521
2522
2523
2524
2525
2526
2527
2528
2529
2530
2531
2532
2533
2534
2535
2536
2537
2538
2539
2540
2541
2542
2543
2544
2545
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'))) {
2547
2548
2549
2550
2551
2552
2553
2554
2555
2556
2557
2558
2559
2560
2561
2562
2563
2564
2565
2566
2567
2568
2569
2570
2571
2572
2573
2574
2575
2576
2577
2578
2579
2580
2581
2582
2583
2584
2585
2586
2587
2588
2589
2590
2591
2592
2593
2594
2595
2596
2597
2598
2599
2600
2601
2602
2603
2604
2605
2606
2607
2608
2609
2610
2611
2612
2613
2614
2615
2616
2617
2618
2619
#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') {
2621
2622
2623
2624
2625
2626
2627
2628
2629
2630
2631
2632
2633
2634
2635
2636
2637
2638
2639
2640
2641
2642
2643
2644
2645
2646
2647
2648
2649
2650
2651
2652
2653
2654
2655
2656
2657
2658
2659
2660
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
}
}
}
2662
2663
2664
2665
2666
2667
2668
2669
2670
2671
2672
2673
2674
2675
2676
2677
2678
2679
2680
2681
2682
2683
2684
2685
2686
2687
2688
2689
2690
2691
2692
2693
2694
2695
2696
2697
2698
2699
2700
2701
2702
2703
2704
2705
2706
2707
2708
}
# 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)) {
2710
2711
2712
2713
2714
2715
2716
2717
2718
2719
2720
2721
2722
2723
2724
2725
2726
2727
2728
2729
2730
2731
2732
2733
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)
}
}
}
new_dims <- .MergeArrayDims(dim(array_of_files_to_load), total_inner_dims)
Nicolau Manubens
committed
# 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]])
Nicolau Manubens
committed
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]]))
Nicolau Manubens
committed
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
}
}
}
Nicolau Manubens
committed
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))
Nicolau Manubens
committed
}
old_dim_pos <- which(names(final_dims_fake) == names(dim_params)[dim_param])
aho
committed
# 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.
Nicolau Manubens
committed
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)])
}
Nicolau Manubens
committed
}
}
}
}
aho
committed
if (merge_across_dims_narm) {
aho
committed
# 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')
aho
committed
length_inner_across_dim <- lapply(dat[[i]][['selectors']][[across_inner_dim]][['fri']], length)
aho
committed
aho
committed
if (!split_multiselected_dims) {
aho
committed
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
}
aho
committed
}
Nicolau Manubens
committed
if (!silent) {
.message("Detected dimension sizes:")
Nicolau Manubens
committed
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 = ''),
Nicolau Manubens
committed
x, ": ", paste(rep(' ', longest_size_len - nchar(paste0(final_dims_fake[x], ''))), collapse = ''),
final_dims_fake[x]))
Nicolau Manubens
committed
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)
}
2854
2855
2856
2857
2858
2859
2860
2861
2862
2863
2864
2865
2866
2867
2868
2869
2870
2871
2872
2873
2874
2875
2876
2877
2878
2879
# 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)) {
# 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)
2921
2922
2923
2924
2925
2926
2927
2928
2929
2930
2931
2932
2933
2934
2935
2936
2937
2938
2939
2940
2941
2942
2943
2944
# 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]]
2969
2970
2971
2972
2973
2974
2975
2976
2977
2978
2979
2980
2981
2982
2983
2984
2985
2986
2987
2988
2989
2990
2991
2992
2993
2994
2995
2996
2997
2998
2999
3000
}
})
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)) {