Newer
Older
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
wp
})
if (!silent) {
.message("If the size of the requested data is close to or above the free shared RAM memory, R may crash.")
.message("If the size of the requested data is close to or above the half of the free RAM memory, R may crash.")
.message(paste0("Will now proceed to read and process ", length(work_pieces), " data files:"))
if (length(work_pieces) < 30) {
lapply(work_pieces, function (x) .message(x[['file_path']], indent = 2))
} else {
.message("The list of files is long. You can check it after Start() finishes in the output '$Files'.", indent = 2, exdent = 5)
}
}
# Build the cluster of processes that will do the work and dispatch work pieces.
# The function .LoadDataFile is applied to each work piece. This function will
# open the data file, regrid if needed, subset, apply the mask,
# compute and apply the weights if needed,
# disable extreme values and store in the shared memory matrix.
#print("O")
if (!silent) {
.message("Loading... This may take several minutes...")
if (progress_message != '') {
.message(progress_message, appendLF = FALSE)
}
}
return(work_pieces)
}
# If merge_across_dims = TRUE and merge_across_dims_narm = TRUE, remove the additional NAs
# due to unequal inner_dim ('time') length across file_dim ('sdate').
remove_additional_na_from_merge <- function(inner_dims_across_files, final_dims,
length_inner_across_dim, data_array, merge_dim_metadata) {
across_file_dim <- names(inner_dims_across_files) #TODO: more than one?
across_inner_dim <- inner_dims_across_files[[1]] #TODO: more than one?
# Get the length of these two dimensions in final_dims
length_inner_across_store_dims <- final_dims[across_inner_dim]
length_file_across_store_dims <- final_dims[across_file_dim]
# Create a logical array for merge_across_dims
logi_array <- array(rep(FALSE,
length_file_across_store_dims * length_inner_across_store_dims),
dim = c(length_inner_across_store_dims, length_file_across_store_dims))
for (i in 1:length_file_across_store_dims) { #1:4
logi_array[1:length_inner_across_dim[[i]], i] <- TRUE
}
# First, get the data array with final_dims dimension
data_array_final_dims <- array(bigmemory::as.matrix(data_array), dim = final_dims)
# Change the NA derived from additional spaces to -9999, then remove these -9999
func_remove_blank <- function(data_array, logi_array) {
# dim(data_array) = [time, file_date]
# dim(logi_array) = [time, file_date]
# Change the blank spaces from NA to -9999
if (any(class(data_array) %in% c("POSIXct", "POSIXt"))) {
# change to numeric first
data_array <- array(as.vector(data_array), dim = dim(data_array))
} else {
data_array[which(!logi_array)] <- -9999
}
return(data_array)
}
data_array_final_dims <- multiApply::Apply(data_array_final_dims,
target_dims = c(across_inner_dim, across_file_dim), #c('time', 'file_date')
output_dims = c(across_inner_dim, across_file_dim),
fun = func_remove_blank,
logi_array = logi_array)$output1
tmp_attr <- attributes(merge_dim_metadata)$variables
merge_dim_metadata <- multiApply::Apply(merge_dim_metadata,
target_dims = c(across_inner_dim, across_file_dim),
output_dims = c(across_inner_dim, across_file_dim),
fun = func_remove_blank,
logi_array = logi_array)$output1
## reorder back to the correct dim
tmp <- match(names(final_dims), names(dim(data_array_final_dims)))
data_array_final_dims <- .aperm2(data_array_final_dims, tmp)
data_array_tmp <- data_array_final_dims[data_array_final_dims != -9999] # become a vector
# Reorder metadata dim as final dim
tmp <- match(names(final_dims), names(dim(merge_dim_metadata)))
merge_dim_metadata <- aperm(merge_dim_metadata, tmp[!is.na(tmp)])
merge_dim_metadata <- merge_dim_metadata[merge_dim_metadata != -12^10]
attr(merge_dim_metadata, 'variables') <- tmp_attr
#NOTE: both outputs are vectors. If 'merge_dim_metadata' is actually time, it is just numeric here.
return(list(data_array_tmp = data_array_tmp, merge_dim_metadata = merge_dim_metadata))
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
}
# When merge_across_dims = TRUE and split_multiselected_dims = TRUE, rearrange the chunks
# (i.e., work_piece) is necessary if one file contains values for discrete dimensions
rebuild_array_merge_split <- function(data_array_tmp, indices_chunk, all_split_dims,
final_dims_fake, across_inner_dim, length_inner_across_dim) {
# generate the correct order list from indices_chunk
final_order_list <- list()
i <- 1
j <- 1
a <- indices_chunk[i]
while (i <= length(indices_chunk)) {
while (indices_chunk[i+1] == indices_chunk[i] & i < length(indices_chunk)) {
a <- c(a, indices_chunk[i+1])
i <- i + 1
}
final_order_list[[j]] <- a
a <- indices_chunk[i+1]
i <- i + 1
j <- j + 1
}
names(final_order_list) <- sapply(final_order_list, '[[', 1)
final_order_list <- lapply(final_order_list, length)
if (!all(diff(as.numeric(names(final_order_list))) > 0)) {
# shape the vector into the array without split_dims
split_dims_pos <- match(names(all_split_dims[[1]]), names(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, prod(all_split_dims[[1]]))
names(new_dims)[split_dims_pos[1]] <- across_inner_dim
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)])
}
data_array_no_split <- array(data_array_tmp, dim = new_dims)
# seperate 'time' dim into each work_piece length
data_array_seperate <- list()
tmp <- cumsum(unlist(length_inner_across_dim))
tmp <- c(0, tmp)
for (i in 1:length(length_inner_across_dim)) {
data_array_seperate[[i]] <- ClimProjDiags::Subset(data_array_no_split,
across_inner_dim,
(tmp[i] + 1):tmp[i + 1])
}
# re-build the array: chunk
which_chunk <- as.numeric(names(final_order_list))
sort_which_chunk <- sort(unique(which_chunk))
which_chunk <- sapply(lapply(which_chunk, '==', sort_which_chunk), which)
how_many_indices <- unlist(final_order_list)
array_piece <- list()
ind_in_array_seperate <- as.list(rep(1, length(data_array_seperate)))
for (i in 1:length(final_order_list)) {
array_piece[[i]] <- ClimProjDiags::Subset(
data_array_seperate[[which_chunk[i]]], across_inner_dim,
ind_in_array_seperate[[which_chunk[i]]]:(ind_in_array_seperate[[which_chunk[i]]] + how_many_indices[i] - 1))
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174
ind_in_array_seperate[[which_chunk[i]]] <- ind_in_array_seperate[[which_chunk[i]]] + how_many_indices[i]
}
# re-build the array: paste
data_array_tmp <- array_piece[[1]]
along_pos <- which(names(dim(data_array_tmp)) == across_inner_dim)
if (length(array_piece) > 1) {
for (i in 2:length(array_piece)) {
data_array_tmp <- abind::abind(data_array_tmp, array_piece[[i]],
along = along_pos)
}
}
}
return(data_array_tmp)
}
# Create a list of metadata of the variable (e.g., tas)
create_metadata_list <- function(array_of_metadata_flags, metadata_dims, pattern_dims,
loaded_metadata_files, loaded_metadata, dat_names,
dataset_has_files) {
#NOTE: Here, metadata can be saved in one of two ways: one for $common and the other for $dat
# for $common, it is a list of metadata length. For $dat, it is a list of dat length,
# and each sublist has the metadata for each dat.
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191
1192
1193
1194
1195
1196
1197
1198
1199
1200
dim_of_metadata <- dim(array_of_metadata_flags)[metadata_dims]
if (!any(names(dim_of_metadata) == pattern_dims) |
(any(names(dim_of_metadata) == pattern_dims) &
dim_of_metadata[pattern_dims] == 1)) { # put under $common; old code
return_metadata <- vector('list',
length = prod(dim_of_metadata))
return_metadata[as.numeric(loaded_metadata_files)] <- loaded_metadata
dim(return_metadata) <- dim_of_metadata
} else { # put under $dat. metadata_dims has 'dat' and dat length > 1
return_metadata <- vector('list',
length = dim_of_metadata[pattern_dims])
names(return_metadata) <- dat_names
for (kk in 1:length(return_metadata)) {
return_metadata[[kk]] <- vector('list', length = prod(dim_of_metadata[-1])) # 1 is dat
}
loaded_metadata_count <- 1
for (kk in 1:length(return_metadata)) {
for (jj in 1:length(return_metadata[[kk]])) {
if (dataset_has_files[kk]) {
if (loaded_metadata_count %in% loaded_metadata_files) {
return_metadata[[kk]][jj] <- loaded_metadata[[which(loaded_metadata_files == loaded_metadata_count)]]
names(return_metadata[[kk]])[jj] <- names(loaded_metadata[[which(loaded_metadata_files == loaded_metadata_count)]])
} else {
return_metadata[[kk]][jj] <- NULL
loaded_metadata_count <- loaded_metadata_count + 1
} else {
return_metadata[[kk]][jj] <- NULL
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
return(return_metadata)
}
# This function adds the metadata of the variable (e.g., tas) into the list of picked_vars or
# picked_common_vars. The metadata is only retrieved when 'retrieve = TRUE'.
combine_metadata_picked_vars <- function(return_metadata, picked_vars, picked_common_vars,
metadata_dims, pattern_dims, length_dat) {
#NOTE: The metadata of variables can be saved in one of the two different structures.
# (1) metadata_dims != 'dat', or (metadata_dims == 'dat' & length(dat) == 1):
# put under $common
# (2) (metadata_dims == 'dat' & length(dat) > 1):
# put under $dat1, $dat2, .... Put it in picked_vars list
#TODO: The current (2) uses the inefficient method. Should define the list structure first
# then fill the list, rather than expand it in the for loop.
if (any(metadata_dims == pattern_dims) & length_dat > 1) { # (2)
for (kk in 1:length(return_metadata)) {
sublist_names <- lapply(return_metadata, names)[[kk]]
if (!is.null(sublist_names)) {
for (jj in 1:length(sublist_names)) {
picked_vars[[kk]][[sublist_names[jj]]] <- return_metadata[[kk]][[jj]]
}
}
}
Variables_list <- c(list(common = picked_common_vars), picked_vars)
} else { #(1)
len <- unlist(lapply(return_metadata, length))
len <- sum(len) + length(which(len == 0)) #0 means NULL
name_list <- lapply(return_metadata, names)
new_list <- vector('list', length = len)
count <- 1
for (kk in 1:length(return_metadata)) {
if (length(return_metadata[[kk]]) == 0) { #NULL
count <- count + 1
} else {
for (jj in 1:length(return_metadata[[kk]])) {
new_list[[count]] <- return_metadata[[kk]][[jj]]
names(new_list)[count] <- name_list[[kk]][jj]
count <- count + 1
}
}
}
Variables_list <- c(list(common = c(picked_common_vars, new_list)), picked_vars)
}
return(Variables_list)
}
1260
1261
1262
1263
1264
1265
1266
1267
1268
1269
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284
1285
1286
1287
1288
1289
1290
1291
1292
1293
1294
1295
1296
1297
1298
1299
1300
1301
1302
1303
1304
1305
1306
1307
1308
1309
1310
1311
1312
1313
1314
1315
1316
1317
1318
# This function generates a list of 3, containing picked(_common)_vars,
# picked(_common)_vars_ordered, and picked(_common)_vars_unorder_indices for the 'var_to_read'
# of this dataset (i) and file (j).
generate_picked_var_of_read <- function(var_to_read, var_to_check, array_of_files_to_load,
var_dims, array_of_var_files, file_var_reader,
file_object, synonims, associated_dim_name,
dim_reorder_params, aiat, current_indices, var_params,
either_picked_vars,
either_picked_vars_ordered,
either_picked_vars_unorder_indices) {
var_file_dims <- NULL
if (any(names(dim(array_of_files_to_load)) %in% var_to_check)) {
var_file_dims <- dim(array_of_files_to_load)[which(names(dim(array_of_files_to_load)) %in%
var_to_check)]
}
if (is.null(either_picked_vars)) {
if (any(names(var_file_dims) %in% names(var_dims))) {
stop("Found a requested var in 'return_var' requested for a ",
"file dimension which also appears in the dimensions of ",
"the variable inside the file.\n", array_of_var_files)
}
first_sample <- file_var_reader(NULL, file_object, NULL,
var_to_read, synonims)
if (any(class(first_sample) %in% names(time_special_types()))) {
array_size <- prod(c(var_file_dims, var_dims))
new_array <- rep(time_special_types()[[class(first_sample)[1]]](NA), array_size)
dim(new_array) <- c(var_file_dims, var_dims)
} else {
new_array <- array(dim = c(var_file_dims, var_dims))
}
attr(new_array, 'variables') <- attr(first_sample, 'variables')
either_picked_vars <- new_array
pick_ordered <- FALSE
if (var_to_read %in% unlist(var_params)) {
if (associated_dim_name %in% names(dim_reorder_params) && !aiat) {
either_picked_vars_ordered <- new_array
pick_ordered <- TRUE
}
}
if (!pick_ordered) {
either_picked_vars_ordered <- NULL
}
} else {
array_var_dims <- dim(either_picked_vars)
full_array_var_dims <- array_var_dims
if (any(names(array_var_dims) %in% names(var_file_dims))) {
array_var_dims <- array_var_dims[-which(names(array_var_dims) %in% names(var_file_dims))]
}
if (names(array_var_dims) != names(var_dims)) {
stop("Error while reading the variable '", var_to_read, "' from ",
"the file. Dimensions do not match.\nExpected ",
paste(paste0("'", names(array_var_dims), "'"), collapse = ', '),
" but found ",
paste(paste0("'", names(var_dims), "'"), collapse = ', '),
1320
1321
1322
1323
1324
1325
1326
1327
1328
1329
1330
1331
1332
1333
1334
1335
1336
1337
1338
1339
1340
}
if (any(var_dims > array_var_dims)) {
longer_dims <- which(var_dims > array_var_dims)
if (length(longer_dims) == 1) {
longer_dims_in_full_array <- longer_dims
if (any(names(full_array_var_dims) %in% names(var_file_dims))) {
candidates <- (1:length(full_array_var_dims))[-which(names(full_array_var_dims) %in% names(var_file_dims))]
longer_dims_in_full_array <- candidates[longer_dims]
}
padding_dims <- full_array_var_dims
padding_dims[longer_dims_in_full_array] <-
var_dims[longer_dims] - array_var_dims[longer_dims]
var_class <- class(either_picked_vars)
if (any(var_class %in% names(time_special_types()))) {
padding_size <- prod(padding_dims)
padding <- rep(time_special_types()[[var_class[1]]](NA), padding_size)
dim(padding) <- padding_dims
} else {
padding <- array(dim = padding_dims)
}
tmp_attr <- attributes(either_picked_vars)$variables
either_picked_vars <- .abind2(either_picked_vars, padding,
names(full_array_var_dims)[longer_dims_in_full_array])
attr(either_picked_vars, 'variables') <- tmp_attr
1346
1347
1348
1349
1350
1351
1352
1353
1354
1355
1356
1357
1358
1359
1360
1361
1362
1363
1364
1365
1366
1367
1368
1369
1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
1382
1383
1384
1385
1386
1387
1388
1389
1390
1391
1392
1393
1394
1395
1396
1397
1398
1399
1400
1401
1402
1403
1404
1405
1406
1407
1408
1409
1410
1411
1412
1413
1414
1415
1416
1417
} else {
stop("Error while reading the variable '", var_to_read, "' from ",
"the file. Found size (", paste(var_dims, collapse = ' x '),
") is greater than expected maximum size (", array_var_dims, ").")
}
}
}
var_store_indices <- c(as.list(current_indices[names(var_file_dims)]),
lapply(var_dims, function(x) 1:x))
var_values <- file_var_reader(NULL, file_object, NULL, var_to_read, synonims)
if (var_to_read %in% unlist(var_params)) {
if ((associated_dim_name %in% names(dim_reorder_params)) && !aiat) {
## Is this check really needed?
if (length(dim(var_values)) > 1) {
stop("Requested a '", associated_dim_name, "_reorder' for a dimension ",
"whose coordinate variable that has more than 1 dimension. This is ",
"not supported.")
}
ordered_var_values <- dim_reorder_params[[associated_dim_name]](var_values)
attr(ordered_var_values$x, 'variables') <- attr(var_values, 'variables')
if (!all(c('x', 'ix') %in% names(ordered_var_values))) {
stop("All the dimension reorder functions must return a list with the components 'x' and 'ix'.")
}
# Save the indices to reorder the ordered variable values back to original order.
# 'unorder' refers to the indices of 'ordered_var_values' if it is unordered.
# This will be used to define the first round indices.
unorder <- sort(ordered_var_values$ix, index.return = TRUE)$ix
either_picked_vars_ordered <- do.call('[<-',
c(list(x = either_picked_vars_ordered),
var_store_indices,
list(value = ordered_var_values$x)))
either_picked_vars_unorder_indices <- do.call('[<-',
c(list(x = either_picked_vars_unorder_indices),
var_store_indices,
list(value = unorder)))
}
}
either_picked_vars <- do.call('[<-',
c(list(x = either_picked_vars),
var_store_indices,
list(value = var_values)))
# Turn time zone back to UTC if this var_to_read is 'time'
if (all(class(either_picked_vars) == names(time_special_types))) {
attr(either_picked_vars, "tzone") <- 'UTC'
}
return(list(either_picked_vars = either_picked_vars,
either_picked_vars_ordered = either_picked_vars_ordered,
either_picked_vars_unorder_indices = either_picked_vars_unorder_indices))
}
# Trnasforms a vector of indices v expressed in a world of
# length N from 1 to N, into a world of length M, from
# 1 to M. Repeated adjacent indices are collapsed.
transform_indices <- function(v, n, m) {
#unique2 turns e.g. 1 1 2 2 2 3 3 1 1 1 into 1 2 3 1
unique2 <- function(v) {
if (length(v) < 2) {
v
} else {
v[c(1, v[2:length(v)] - v[1:(length(v) - 1)]) != 0]
}
}
unique2(round(((v - 1) / (n - 1)) * (m - 1))) + 1 # this rounding may generate 0s. what then?
}
replace_character_with_indices <- function(selectors, data_dims, chunk_amount) {
} else if (selectors == 'first') {
selectors <- indices(1)
} else if (selectors == 'last') {