Newer
Older
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), "]."))
}
2046
2047
2048
2049
2050
2051
2052
2053
2054
2055
2056
2057
2058
2059
2060
2061
2062
2063
2064
2065
2066
2067
2068
# 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)
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)
# NOTE: The case not considered: if lon reorder(decreasing = T)
# It doesn't make sense, but if someone uses it, here should
# occur error.
# 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]]
if ('crop' %in% names(transform_params) & is.null(dim_reorder_params[[inner_dim]])) {
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
})
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))
}
2277
2278
2279
2280
2281
2282
2283
2284
2285
2286
2287
2288
2289
2290
2291
2292
2293
2294
2295
2296
2297
2298
2299
2300
2301
2302
2303
2304
2305
2306
2307
2308
2309
2310
2311
2312
2313
2314
2315
2316
2317
2318
2319
2320
2321
2322
2323
2324
2325
2326
2327
2328
2329
2330
} 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)) {
2349
2350
2351
2352
2353
2354
2355
2356
2357
2358
2359
2360
2361
2362
2363
2364
2365
2366
2367
2368
2369
2370
2371
2372
2373
2374
2375
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))) {
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)
}
}
}
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)]
2409
2410
2411
2412
2413
2414
2415
2416
2417
2418
2419
2420
2421
2422
2423
2424
2425
2426
2427
2428
2429
2430
2431
2432
2433
2434
2435
2436
2437
2438
2439
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
2441
2442
2443
2444
2445
2446
2447
2448
2449
2450
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
2482
2483
2484
2485
2486
2487
2488
2489
2490
2491
2492
2493
2494
2495
2496
2497
2498
2499
2500
2501
2502
2503
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'))) {
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
2546
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
#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') {
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
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
}
}
}
2620
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
2661
2662
2663
2664
2665
2666
}
# 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)) {
2668
2669
2670
2671
2672
2673
2674
2675
2676
2677
2678
2679
2680
2681
2682
2683
2684
2685
2686
2687
2688
2689
2690
2691
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])
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
}
}
}
}
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)
}
# 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)
2826
2827
2828
2829
2830
2831
2832
2833
2834
2835
2836
2837
2838
2839
2840
2841
2842
2843
2844
2845
2846
2847
2848
2849
# 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]]
2874
2875
2876
2877
2878
2879
2880
2881
2882
2883
2884
2885
2886
2887
2888
2889
2890
2891
2892
2893
2894
2895
2896
2897
2898
2899
2900
2901
2902
2903
2904
2905
2906
2907
2908
2909
2910
2911
2912
2913
2914
2915
2916
2917
2918
2919
2920
2921
2922
2923
2924
2925
2926
}
})
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[[i]]) %in% transform_vars)
if (length(picked_vars_to_transform) > 0) {
picked_vars_to_transform <- names(picked_vars[[i]])[picked_vars_to_transform]
vars_to_transform <- c(vars_to_transform, picked_vars[[i]][picked_vars_to_transform])
if (any(picked_vars_to_transform %in% names(picked_vars_ordered[[i]]))) {
picked_vars_ordered_to_transform <- picked_vars_to_transform[which(picked_vars_to_transform %in% names(picked_vars_ordered[[i]]))]
vars_to_transform[picked_vars_ordered_to_transform] <- picked_vars_ordered[[i]][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)
}
2931
2932
2933
2934
2935
2936
2937
2938
2939
2940
2941
2942
2943
2944
2945
2946
2947
2948
2949
2950
2951
2952
2953
2954
2955
2956
2957
2958
2959
2960
2961
2962
2963
2964
2965
2966
2967
2968
2969
2970
2971
2972
2973
2974
2975
2976
2977
2978
2979
2980
2981
2982
2983
2984
2985
2986
2987
work_pieces <- c(work_pieces, list(work_piece))
}
}
j <- j + 1
}
}
}
#print("N")
if (debug) {
print("-> WORK PIECES BUILT")
}
# Calculate the progress %s that will be displayed and assign them to
# the appropriate work pieces.
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 != '') {