Start.R

                        
                        if (min(gap_width, beta) != beta) {
                          .warning(paste0("Adding parameter transform_extra_cells =  ",
                                          transform_extra_cells, " to the transformed index excesses ",
                                          "the border. The border index is used for transformation."))
                        }
                      }
                      
                    } else {
                      #NOTE: This if seems redundant.
                      if (is.list(sub_array_of_indices)) {
                        sub_array_of_indices <- sub_array_of_indices[[1]]:sub_array_of_indices[[2]]
                      }
                      first_index <- min(unlist(sub_array_of_indices))
                      last_index <- max(unlist(sub_array_of_indices))
                      
                      start_padding <- min(beta, first_index - 1)
                      end_padding <- min(beta, n - last_index)
                      
                      if (exists("is_circular_dim")) {
                        if (!is_circular_dim) {  #latitude
                          sub_array_of_fri <- (first_index - start_padding):(last_index + end_padding)
                          if (start_padding != beta | end_padding != beta) {
                            .warning(paste0("Adding parameter transform_extra_cells =  ",
                                            transform_extra_cells, " to the transformed index excesses ",
                                            "the border. The border index is used for transformation."))
                          }
                        } else {  #longitude
                          if ((last_index - first_index + 1 + beta * 2) >= n) {
                            sub_array_of_fri <- 1:n
                          } else if (start_padding < beta) {  # left side too close to border, need to go to right side
                            sub_array_of_fri <- c((first_index - start_padding):(last_index + end_padding), (n - (beta - start_padding - 1)):n)
                          } else if (end_padding < beta) { # right side too close to border, need to go to left side
                            sub_array_of_fri <- c(1: (beta - end_padding), (first_index - start_padding):(last_index + end_padding))
                          } else {  #normal
                            sub_array_of_fri <- (first_index - start_padding):(last_index + end_padding)
                          }
                        }
                      } else {   # when <var>_reorder is not used
                        sub_array_of_fri <- (first_index - start_padding):(last_index + end_padding)
                        if (start_padding != beta | end_padding != beta) {
                          .warning(paste0("Adding parameter transform_extra_cells =  ",
                                          transform_extra_cells, " to the transformed index excesses ",
                                          "the border. The border index is used for transformation."))
                        }
                      }
                      
                    }
                    subset_vars_to_transform <- vars_to_transform
                    if (!is.null(var_ordered)) {
                      
                      ##NOTE: if var_ordered is common_vars, it doesn't have attributes and it is a vector.
                      ## Turn it into array and add dimension name.
                      if (!is.array(var_ordered)) {
                        var_ordered <- as.array(var_ordered)
                        names(dim(var_ordered)) <- inner_dim
                      }
                      
                      subset_vars_to_transform[[var_with_selectors_name]] <- Subset(var_ordered, inner_dim, sub_array_of_fri)
                    } else {
                      ##NOTE: It should be redundant because without reordering the var should remain array
                      ## But just stay same with above...
                      if (!is.array(sub_array_of_values)) {
                        sub_array_of_values <- as.array(sub_array_of_values)
                        names(dim(sub_array_of_values)) <- inner_dim
                      }
                      
                      subset_vars_to_transform[[var_with_selectors_name]] <- Subset(sub_array_of_values, inner_dim, sub_array_of_fri)
                    }
                    
                    # Change the order of longitude crop if no reorder + from big to small.
                    # cdo -sellonlatbox, the lon is west, east (while lat can be north 
                    # to south or opposite)
                    
                    # Before changing crop, first we need to find the name of longitude. 
                    # NOTE: The potential bug here (also the bug for CDORemapper): the lon name
                    #       is limited (only the ones listed in .KnownLonNames() are available.
                    known_lon_names <- .KnownLonNames()
                    lon_name <- names(subset_vars_to_transform)[which(names(subset_vars_to_transform) %in% known_lon_names)[1]]
                    
                    # NOTE: The cases not considered: (1) if lon reorder(decreasing = T)
                    #       It doesn't make sense, but if someone uses it, here should
                    #       occur error. (2) crop = TRUE/FALSE
                    if ('crop' %in% names(transform_params) & var_with_selectors_name == lon_name & is.null(dim_reorder_params[[inner_dim]])) {
                      if (is.numeric(class(transform_params$crop))) {
                        if (transform_params$crop[1] > transform_params$crop[2]) {
                          tmp <- transform_params$crop[1]
                          transform_params$crop[1] <- transform_params$crop[2]
                          transform_params$crop[2] <- tmp
                        }
                      }
                    }
                    
                    transformed_subset_var <- do.call(transform, c(list(data_array = NULL,
                                                                        variables = subset_vars_to_transform,
                                                                        file_selectors = selectors_of_first_files_with_data[[i]]),
                                                                   transform_params))$variables[[var_with_selectors_name]]
                    # Sorting the transformed variable and working out the indices again after transform.
                    if (!is.null(dim_reorder_params[[inner_dim]])) {
                      transformed_subset_var_reorder <- dim_reorder_params[[inner_dim]](transformed_subset_var)
                      transformed_subset_var <- transformed_subset_var_reorder$x
                      #NOTE: The fix here solves the mis-ordered lon when across_meridian. 
                      transformed_subset_var_unorder <- transformed_subset_var_reorder$ix
                      #                      transformed_subset_var_unorder <- sort(transformed_subset_var_reorder$ix, index.return = TRUE)$ix
                    } else {
                      transformed_subset_var_unorder <- 1:length(transformed_subset_var)
                    }
                    sub_array_of_sri <- selector_checker(sub_array_of_selectors, transformed_subset_var,
                                                         tolerance = if (aiat) {
                                                           tolerance_params[[inner_dim]]
                                                         } else {
                                                           NULL
                                                         })
                    
                    # Check if selectors fall out of the range of the transform grid
                    # It may happen when original lon is [-180, 180] while want to regrid to
                    # [0, 360], and lon selector = [-20, -10].
                    if (any(is.na(sub_array_of_sri))) {
                      stop(paste0("The selectors of ",
                                  inner_dim, " are out of range of transform grid '",
                                  transform_params$grid, "'. Use parameter '",
                                  inner_dim, "_reorder' or change ", inner_dim,
                                  " selectors."))
                    }
                    
                    if (goes_across_prime_meridian) {
                      
                      if (sub_array_of_sri[[1]] == sub_array_of_sri[[2]]) {
                        # global longitude
                        sub_array_of_sri <- c(1:length(transformed_subset_var))
                      } else {
                        # the common case, i.e., non-global
                        # NOTE: Because sub_array_of_sri order is exchanged due to 
                        # previous development, here [[1]] and [[2]] should exchange
                        sub_array_of_sri <- c(1:sub_array_of_sri[[1]],
                                              sub_array_of_sri[[2]]:length(transformed_subset_var))
                      }
                      
                    } else if (is.list(sub_array_of_sri)) {
                      sub_array_of_sri <- sub_array_of_sri[[1]]:sub_array_of_sri[[2]]
                    }
                    ordered_sri <- sub_array_of_sri
                    sub_array_of_sri <- transformed_subset_var_unorder[sub_array_of_sri]
                    # In this case, the tvi are not defined and the 'transformed_subset_var'
                    # will be taken instead of the var transformed before in the code.
                    if (debug) {
                      if (inner_dim %in% dims_to_check) {
                        print("-> FIRST INDEX:")
                        print(first_index)
                        print("-> LAST INDEX:")
                        print(last_index)
                        print("-> STRUCTURE OF FIRST ROUND INDICES:")
                        print(str(sub_array_of_fri))
                        print("-> STRUCTURE OF SECOND ROUND INDICES:")
                        print(str(sub_array_of_sri))
                        print("-> STRUCTURE OF TRANSFORMED VARIABLE INDICES:")
                        print(str(tvi))
                      }
                    }
                    ###                    # If the selectors are expressed after transformation
                    ###                    } else {
                    ###if (debug) {
                    ###if (inner_dim %in% dims_to_check) {
                    ###print("-> SELECTORS REQUESTED AFTER TRANSFORM.")
                    ###}
                    ###}
                    ###                      if (goes_across_prime_meridian) {
                    ###                        sub_array_of_indices <- c(sub_array_of_indices[[1]]:m,
                    ###                                                    1:sub_array_of_indices[[2]])
                    ###                      }
                    ###                      first_index <- min(unlist(sub_array_of_indices))
                    ###                      last_index <- max(unlist(sub_array_of_indices))
                    ###                      first_index_before_transform <- max(transform_indices(first_index, m, n) - beta, 1)
                    ###                      last_index_before_transform <- min(transform_indices(last_index, m, n) + beta, n)
                    ###                      sub_array_of_fri <- first_index_before_transform:last_index_before_transform
                    ###                      n_of_extra_cells <- round(beta / n * m)
                    ###                      if (is.list(sub_array_of_indices) && (length(sub_array_of_indices) > 1)) {
                    ###                        sub_array_of_sri <- 1:(last_index - first_index + 1) 
                    ###                        if (is.null(tvi)) {
                    ###                          tvi <- sub_array_of_sri + first_index - 1
                    ###                        }
                    ###                      } else {
                    ###                        sub_array_of_sri <- sub_array_of_indices - first_index + 1
                    ###                        if (is.null(tvi)) {
                    ###                          tvi <- sub_array_of_indices
                    ###                        }
                    ###                      }
                    ###                      sub_array_of_sri <- sub_array_of_sri + n_of_extra_cells
                    sri <- do.call('[[<-', c(list(x = sri), as.list(selector_store_position),
                                             list(value = sub_array_of_sri)))
                  } else {
                    if (goes_across_prime_meridian) {
                      #NOTE: The potential problem here is, if it is global longitude,
                      #      and the indices overlap (e.g., lon = [0, 359.723] and 
                      #      CircularSort(-180, 180), then sub_array_of_indices = list(649, 649)). 
                      #      Therefore, sub_array_of_fri will be c(1:649, 649:1296). We'll
                      #      get two 649.
                      #      The fix below may not be the best solution, but it works for 
                      #      the example above.
                      
                      if (sub_array_of_indices[[1]] == sub_array_of_indices[[2]]) {
                        # global longitude
                        sub_array_of_fri <- c(1:n)
                      } else {
                        # the common case, i.e., non-global
                        sub_array_of_fri <- c(1:min(unlist(sub_array_of_indices)),
                                              max(unlist(sub_array_of_indices)):n)
                      }
                      
                    } else if (is.list(sub_array_of_indices)) {
                      sub_array_of_fri <- sub_array_of_indices[[1]]:sub_array_of_indices[[2]]
                    } else {
                      sub_array_of_fri <- sub_array_of_indices
                    }
                  }
                  if (!is.null(var_unorder_indices)) {
                    if (is.null(ordered_fri)) {
                      ordered_fri <- sub_array_of_fri
                    }
                    sub_array_of_fri <- var_unorder_indices[sub_array_of_fri]
                  }
                  fri <- do.call('[[<-', c(list(x = fri), as.list(selector_store_position),
                                           list(value = sub_array_of_fri)))
                  if (!is.null(file_dim)) {
                    taken_chunks[selector_store_position[[file_dim]]] <- TRUE
                  } else {
                    taken_chunks <- TRUE
                  }
                }
              } else {
                if (debug) {
                  if (inner_dim %in% dims_to_check) {
                    print("-> THE INNER DIMENSION GOES ACROSS A FILE DIMENSION.")
                  }
                }
                if (inner_dim %in% names(dim(sub_array_of_selectors))) {
                  if (is.null(var_with_selectors_name)) {
                    if (any(na.omit(unlist(sub_array_of_selectors)) < 1) ||
                        any(na.omit(unlist(sub_array_of_selectors)) > data_dims[inner_dim] * chunk_amount)) {
                      stop("Provided indices out of range for dimension '", inner_dim, "' ", 
                           "for dataset '", dat[[i]][['name']], "' (accepted range: 1 to ", 
                           data_dims[inner_dim] * chunk_amount, ").")
                    }
                  } else {
                    if (inner_dim %in% names(dim(sub_array_of_values))) {
                      # NOTE: Put across-inner-dim at the 1st position.
                      # POSSIBLE PROB!! Only organize inner dim, the rest dims may not in the same order as sub_array_of_selectors below.
                      inner_dim_pos_in_sub_array <- which(names(dim(sub_array_of_values)) == inner_dim)
                      if (inner_dim_pos_in_sub_array != 1) {
                        new_sub_array_order <- (1:length(dim(sub_array_of_values)))[-inner_dim_pos_in_sub_array]
                        new_sub_array_order <- c(inner_dim_pos_in_sub_array, new_sub_array_order)
                        sub_array_of_values <- .aperm2(sub_array_of_values, new_sub_array_order)
                      }
                    }
                  }
                  
                  # NOTE: Put across-inner-dim at the 1st position.
                  # POSSIBLE PROB!! Only organize inner dim, the rest dims may not in the same order as sub_array_of_values above.
                  inner_dim_pos_in_sub_array <- which(names(dim(sub_array_of_selectors)) == inner_dim)
                  if (inner_dim_pos_in_sub_array != 1) {
                    new_sub_array_order <- (1:length(dim(sub_array_of_selectors)))[-inner_dim_pos_in_sub_array]
                    new_sub_array_order <- c(inner_dim_pos_in_sub_array, new_sub_array_order)
                    sub_array_of_selectors <- .aperm2(sub_array_of_selectors, new_sub_array_order)
                  }
                  sub_array_of_indices <- selector_checker(sub_array_of_selectors, sub_array_of_values,
                                                           tolerance = tolerance_params[[inner_dim]])
                  # It is needed to expand the indices here, otherwise for 
                  # values(list(date1, date2)) only 2 values are picked.
                  if (is.list(sub_array_of_indices)) {
                    sub_array_of_indices <- sub_array_of_indices[[1]]:sub_array_of_indices[[2]]
                  }
                  sub_array_of_indices <- sub_array_of_indices[chunk_indices(length(sub_array_of_indices),
                                                                             chunks[[inner_dim]]['chunk'],
                                                                             chunks[[inner_dim]]['n_chunks'],
                                                                             inner_dim)]
                  sub_array_is_list <- FALSE
                  if (is.list(sub_array_of_indices)) {
                    sub_array_is_list <- TRUE
                    sub_array_of_indices <- unlist(sub_array_of_indices)
                  }
                  if (is.null(var_with_selectors_name)) {
                    indices_chunk <- floor((sub_array_of_indices - 1) / data_dims[inner_dim]) + 1
                    transformed_indices <- ((sub_array_of_indices - 1) %% data_dims[inner_dim]) + 1
                  } else {
                    indices_chunk <- floor((sub_array_of_indices - 1) / var_full_dims[inner_dim]) + 1
                    transformed_indices <- ((sub_array_of_indices - 1) %% var_full_dims[inner_dim]) + 1
                  }
                  if (sub_array_is_list) {
                    sub_array_of_indices <- as.list(sub_array_of_indices)
                  }
                  if (debug) {
                    if (inner_dim %in% dims_to_check) {
                      print("-> GOING TO ITERATE ALONG CHUNKS.")
                    }
                  }
                  for (chunk in 1:chunk_amount) {
                    if (!is.null(names(selector_store_position))) {
                      selector_store_position[file_dim] <- chunk
                    } else {
                      selector_store_position <- chunk
                    }
                    chunk_selectors <- transformed_indices[which(indices_chunk == chunk)]
                    sub_array_of_indices <- chunk_selectors
                    if (with_transform) {
                      # If the provided selectors are expressed in the world
                      # before transformation
                      if (!aiat) {
                        first_index <- min(unlist(sub_array_of_indices))
                        last_index <- max(unlist(sub_array_of_indices))
                        sub_array_of_fri <- max(c(first_index - beta, 1)):min(c(last_index + beta, n))
                        sub_array_of_sri <- transform_indices(unlist(sub_array_of_indices) - first_index + 1, n, m)
                        if (is.list(sub_array_of_indices)) {
                          if (length(sub_array_of_sri) > 1) {
                            sub_array_of_sri <- sub_array_of_sri[[1]]:sub_array_of_sri[[2]]
                          }
                        }
                        ##TODO: TRANSFORM SUBSET VARIABLE AS ABOVE, TO COMPUTE SRI
                        # If the selectors are expressed after transformation
                      } else {
                        first_index <- min(unlist(sub_array_of_indices))
                        last_index <- max(unlist(sub_array_of_indices))
                        first_index_before_transform <- max(transform_indices(first_index, m, n) - beta, 1)
                        last_index_before_transform <- min(transform_indices(last_index, m, n) + beta, n)
                        sub_array_of_fri <- first_index_before_transform:last_index_before_transform
                        if (is.list(sub_array_of_indices) && (length(sub_array_of_indices) > 1)) {
                          sub_array_of_sri <- 1:(last_index - first_index + 1) + 
                            round(beta / n * m) 
                        } else {
                          sub_array_of_sri <- sub_array_of_indices - first_index + 1 +
                            round(beta / n * m)
                        }
                        ##TODO: FILL IN TVI
                      }
                      sri <- do.call('[[<-', c(list(x = sri), as.list(selector_store_position),
                                               list(value = sub_array_of_sri)))
                      if (length(sub_array_of_sri) > 0) {
                        taken_chunks[chunk] <- TRUE
                      }
                    } else {
                      sub_array_of_fri <- sub_array_of_indices
                      if (length(sub_array_of_fri) > 0) {
                        taken_chunks[chunk] <- TRUE
                      }
                    }
                    if (!is.null(var_unorder_indices)) {
                      ordered_fri <- sub_array_of_fri
                      sub_array_of_fri <- var_unorder_indices[sub_array_of_fri]
                    }
                    fri <- do.call('[[<-', c(list(x = fri), as.list(selector_store_position),
                                             list(value = sub_array_of_fri)))
                  }
                  if (debug) {
                    if (inner_dim %in% dims_to_check) {
                      print("-> FINISHED ITERATING ALONG CHUNKS")
                    }
                  }
                } else {
                  stop("Provided array of indices for dimension '", inner_dim, "', ",
                       "which goes across the file dimension '", file_dim, "', but ",
                       "the provided array does not have the dimension '", inner_dim, 
                       "', which is mandatory.")
                }
              }
            }
          }
          if (debug) {
            if (inner_dim %in% dims_to_check) {
              print("-> PROCEEDING TO CROP VARIABLES")
            }
          }
          #if ((length(selector_array) == 1) && (selector_array %in% c('all', 'first', 'last'))) {
          #if (!is.null(var_with_selectors_name) || (is.null(var_with_selectors_name) && is.character(selector_array) &&
          #    (length(selector_array) == 1) && (selector_array %in% c('all', 'first', 'last')))) {
          empty_chunks <- which(!taken_chunks)
          if (length(empty_chunks) >= length(taken_chunks)) {
            stop("Selectors do not match any of the possible values for the dimension '", inner_dim, "'.")
          }
          if (length(empty_chunks) > 0) {
            #                # Get the first group of chunks to remove, and remove them. 
            #                # E.g., from c(1, 2, 4, 5, 6, 8, 9) remove only 1 and 2
            #                dist <- abs(rev(empty_chunks) - c(rev(empty_chunks)[1] - 1, head(rev(empty_chunks), length(rev(empty_chunks)) - 1)))
            #                if (all(dist == 1)) {
            #                  start_chunks_to_remove <- NULL
            #                } else {
            #                  first_chunk_to_remove <- tail(which(dist > 1), 1)
            #                  start_chunks_to_remove <- rev(rev(empty_chunks)[first_chunk_to_remove:length(empty_chunks)])
            #                }
            #                # Get the last group of chunks to remove, and remove them. 
            #                # E.g., from c(1, 2, 4, 5, 6, 8, 9) remove only 8 and 9
            #                dist <- abs(empty_chunks - c(empty_chunks[1] - 1, head(empty_chunks, length(empty_chunks) - 1)))
            #                if (all(dist == 1)) {
            #                  first_chunk_to_remove <- 1
            #                } else {
            #                  first_chunk_to_remove <- tail(which(dist > 1), 1)
            #                }
            #                end_chunks_to_remove <- empty_chunks[first_chunk_to_remove:length(empty_chunks)]
            #                chunks_to_keep <- which(!((1:length(taken_chunks)) %in% c(start_chunks_to_remove, end_chunks_to_remove)))
            chunks_to_keep <- which(taken_chunks)
            dims_to_crop[[file_dim]] <- c(dims_to_crop[[file_dim]], list(chunks_to_keep))
            #                found_indices <- Subset(found_indices, file_dim, chunks_to_keep)
            #                # Crop dataset variables file dims.
            #                for (picked_var in names(picked_vars[[i]])) {
            #                  if (file_dim %in% names(dim(picked_vars[[i]][[picked_var]]))) {
            #                    picked_vars[[i]][[picked_var]] <- Subset(picked_vars[[i]][[picked_var]], file_dim, chunks_to_keep)
            #                  }
            #                }
          }
          #}
          dat[[i]][['selectors']][[inner_dim]] <- list(fri = fri, sri = sri)
          # Crop dataset variables inner dims.
          # Crop common variables inner dims.
          types_of_var_to_crop <- 'picked'
          if (with_transform) {
            types_of_var_to_crop <- c(types_of_var_to_crop, 'transformed')
          }
          if (!is.null(dim_reorder_params[[inner_dim]])) {
            types_of_var_to_crop <- c(types_of_var_to_crop, 'reordered')
          }
          for (type_of_var_to_crop in types_of_var_to_crop) {
            if (type_of_var_to_crop == 'transformed') {
              if (is.null(tvi)) {
                if (!is.null(dim_reorder_params[[inner_dim]])) {
                  crop_indices <- unique(unlist(ordered_sri))
                } else {
                  crop_indices <- unique(unlist(sri))
                }
              } else {
                crop_indices <- unique(unlist(tvi))
              }
              vars_to_crop <- transformed_vars[[i]]
              common_vars_to_crop <- transformed_common_vars
            } else if (type_of_var_to_crop == 'reordered') {
              crop_indices <- unique(unlist(ordered_fri))
              vars_to_crop <- picked_vars_ordered[[i]]
              common_vars_to_crop <- picked_common_vars_ordered
            } else {
              crop_indices <- unique(unlist(fri))
              vars_to_crop <- picked_vars[[i]]
              common_vars_to_crop <- picked_common_vars
            }
            for (var_to_crop in names(vars_to_crop)) {
              if (inner_dim %in% names(dim(vars_to_crop[[var_to_crop]]))) {
                if (!is.null(crop_indices)) {
                  if (type_of_var_to_crop == 'transformed') {
                    if (!aiat) {
                      vars_to_crop[[var_to_crop]] <- Subset(transformed_subset_var, inner_dim, crop_indices)
                    } else {
                      vars_to_crop[[var_to_crop]] <- Subset(vars_to_crop[[var_to_crop]], inner_dim, crop_indices)
                    }
                  } else {
                    vars_to_crop[[var_to_crop]] <- Subset(vars_to_crop[[var_to_crop]], inner_dim, crop_indices)
                  }
                }
              }
            }
            if (i == length(dat)) {
              for (common_var_to_crop in names(common_vars_to_crop)) {
                if (inner_dim %in% names(dim(common_vars_to_crop[[common_var_to_crop]]))) {

                  if (type_of_var_to_crop == 'transformed' & !aiat) {
                  common_vars_to_crop[[common_var_to_crop]] <- Subset(transformed_subset_var, inner_dim, crop_indices)
                  } else {  #old code
                  common_vars_to_crop[[common_var_to_crop]] <- Subset(common_vars_to_crop[[common_var_to_crop]], inner_dim, crop_indices)
                  }

                }
              }
            }
            if (type_of_var_to_crop == 'transformed') {
              if (!is.null(vars_to_crop)) {
                transformed_vars[[i]] <- vars_to_crop
              }
              if (i == length(dat)) {
                transformed_common_vars <- common_vars_to_crop
              }
            } else if (type_of_var_to_crop == 'reordered') {
              if (!is.null(vars_to_crop)) {
                picked_vars_ordered[[i]] <- vars_to_crop
              }
              if (i == length(dat)) {
                picked_common_vars_ordered <- common_vars_to_crop
              }
            } else {
              if (!is.null(vars_to_crop)) {
                picked_vars[[i]] <- vars_to_crop
              }
              if (i == length(dat)) {
                picked_common_vars <- common_vars_to_crop
              }
            }
          }
          #}
        }
        # After the selectors have been picked (using the original variables), 
        # the variables are transformed. At that point, the original selectors
        # for the transformed variables are also kept in the variable original_selectors.
        #print("L")
      }
    }
  }
  #  if (!is.null(transformed_common_vars)) {
  #    picked_common_vars[names(transformed_common_vars)] <- transformed_common_vars
  #  }
  # Remove the trailing chunks, if any.
  for (file_dim in names(dims_to_crop)) {
    #    indices_to_keep <- min(sapply(dims_to_crop[[file_dim]], min)):max(sapply(dims_to_crop[[file_dim]], max))
    ## TODO: Merge indices in dims_to_crop with some advanced mechanism?
    indices_to_keep <- unique(unlist(dims_to_crop[[file_dim]]))
    array_of_files_to_load <- Subset(array_of_files_to_load, file_dim, indices_to_keep)
    array_of_not_found_files <- Subset(array_of_not_found_files, file_dim, indices_to_keep)
    for (i in 1:length(dat)) {
      # Crop selectors
      for (selector_dim in names(dat[[i]][['selectors']])) {
        if (selector_dim == file_dim) {
          for (j in 1:length(dat[[i]][['selectors']][[selector_dim]][['fri']])) {
            dat[[i]][['selectors']][[selector_dim]][['fri']][[j]] <- dat[[i]][['selectors']][[selector_dim]][['fri']][[j]][indices_to_keep]
          }
          for (j in 1:length(dat[[i]][['selectors']][[selector_dim]][['sri']])) {
            dat[[i]][['selectors']][[selector_dim]][['sri']][[j]] <- dat[[i]][['selectors']][[selector_dim]][['sri']][[j]][indices_to_keep]
          }
        }
        if (file_dim %in% names(dim(dat[[i]][['selectors']][[selector_dim]][['fri']]))) {
          dat[[i]][['selectors']][[selector_dim]][['fri']] <- Subset(dat[[i]][['selectors']][[selector_dim]][['fri']], file_dim, indices_to_keep)
          dat[[i]][['selectors']][[selector_dim]][['sri']] <- Subset(dat[[i]][['selectors']][[selector_dim]][['sri']], file_dim, indices_to_keep)
        }
      }
      # Crop dataset variables file dims.
      for (picked_var in names(picked_vars[[i]])) {
        if (file_dim %in% names(dim(picked_vars[[i]][[picked_var]]))) {
          picked_vars[[i]][[picked_var]] <- Subset(picked_vars[[i]][[picked_var]], file_dim, indices_to_keep)
        }
      }
      for (transformed_var in names(transformed_vars[[i]])) {
        if (file_dim %in% names(dim(transformed_vars[[i]][[transformed_var]]))) {
          transformed_vars[[i]][[transformed_var]] <- Subset(transformed_vars[[i]][[transformed_var]], file_dim, indices_to_keep)
        }
      }
    }
    # Crop common variables file dims.
    for (picked_common_var in names(picked_common_vars)) {
      if (file_dim %in% names(dim(picked_common_vars[[picked_common_var]]))) {
        picked_common_vars[[picked_common_var]] <- Subset(picked_common_vars[[picked_common_var]], file_dim, indices_to_keep)
      }
    }
    for (transformed_common_var in names(transformed_common_vars)) {
      if (file_dim %in% names(dim(transformed_common_vars[[transformed_common_var]]))) {
        transformed_common_vars[[transformed_common_var]] <- Subset(transformed_common_vars[[transformed_common_var]], file_dim, indices_to_keep)
      }
    }
  }
  # Calculate the size of the final array.
  total_inner_dims <- NULL
  for (i in 1:length(dat)) {
    if (dataset_has_files[i]) {
      inner_dims <- expected_inner_dims[[i]]
      inner_dims <- sapply(inner_dims, 
                           function(x) {
                             if (!all(sapply(dat[[i]][['selectors']][[x]][['sri']], is.null))) {
                               max(sapply(dat[[i]][['selectors']][[x]][['sri']], length))
                             } else {
                               if (length(var_params[[x]]) > 0) {
                                 if (var_params[[x]] %in% names(transformed_vars[[i]])) {
                                   length(transformed_vars[[i]][[var_params[[x]]]])
                                 } else if (var_params[[x]] %in% names(transformed_common_vars)) {
                                   length(transformed_common_vars[[var_params[[x]]]])
                                 } else {
                                   max(sapply(dat[[i]][['selectors']][[x]][['fri']], length))
                                 }
                               } else {
                                 max(sapply(dat[[i]][['selectors']][[x]][['fri']], length))
                               }
                             }
                           })
      names(inner_dims) <- expected_inner_dims[[i]]
      if (is.null(total_inner_dims)) {
        total_inner_dims <- inner_dims
      } else {
        new_dims <- .MergeArrayDims(total_inner_dims, inner_dims)
        total_inner_dims <- new_dims[[3]]
      }
    }
  }
  new_dims <- .MergeArrayDims(dim(array_of_files_to_load), total_inner_dims)
  final_dims <- new_dims[[3]][dim_names]
  # final_dims_fake is the vector of final dimensions after having merged the 
  # 'across' file dimensions with the respective 'across' inner dimensions, and
  # after having broken into multiple dimensions those dimensions for which 
  # multidimensional selectors have been provided.
  # final_dims will be used for collocation of data, whereas final_dims_fake 
  # will be used for shaping the final array to be returned to the user.
  final_dims_fake <- final_dims
  if (merge_across_dims) {
    if (!is.null(inner_dims_across_files)) {
      for (file_dim_across in names(inner_dims_across_files)) {
        inner_dim_pos <- which(names(final_dims_fake) == inner_dims_across_files[[file_dim_across]])
        new_dims <- c()
        if (inner_dim_pos > 1) {
          new_dims <- c(new_dims, final_dims_fake[1:(inner_dim_pos - 1)])
        }
        new_dims <- c(new_dims, setNames(prod(final_dims_fake[c(inner_dim_pos, inner_dim_pos + 1)]), 
                                         inner_dims_across_files[[file_dim_across]]))
        if (inner_dim_pos + 1 < length(final_dims_fake)) {
          new_dims <- c(new_dims, final_dims_fake[(inner_dim_pos + 2):length(final_dims_fake)])
        }
        final_dims_fake <- new_dims
      }
    }
  }
  all_split_dims <- NULL
  if (split_multiselected_dims) {
    for (dim_param in 1:length(dim_params)) {
      if (!is.null(dim(dim_params[[dim_param]]))) {
        if (length(dim(dim_params[[dim_param]])) > 1) {
          split_dims <- dim(dim_params[[dim_param]])
          all_split_dims <- c(all_split_dims, setNames(list(split_dims), 
                                                       names(dim_params)[dim_param]))
          if (is.null(names(split_dims))) {
            names(split_dims) <- paste0(names(dim_params)[dim_param], 
                                        1:length(split_dims))
          }
          old_dim_pos <- which(names(final_dims_fake) == names(dim_params)[dim_param])
          
          # NOTE: Three steps to create new dims.
          # 1st: Put in the dims before split_dim.
          # 2nd: Replace the old_dim with split_dims.
          # 3rd: Put in the dims after split_dim.
          new_dims <- c()
          if (old_dim_pos > 1) {
            new_dims <- c(new_dims, final_dims_fake[1:(old_dim_pos - 1)])
          }
          new_dims <- c(new_dims, split_dims)
          if (old_dim_pos < length(final_dims_fake)) {
            new_dims <- c(new_dims, final_dims_fake[(old_dim_pos + 1):length(final_dims_fake)])
          }
          final_dims_fake <- new_dims
        }
      }
    }
  }
  if (merge_across_dims_narm) {
    # only merge_across_dims -> the 'time' dim length needs to be adjusted
    across_inner_dim <- inner_dims_across_files[[1]]  #TODO: more than one?
    across_file_dim <- names(inner_dims_across_files)  #TODO: more than one?
    # Get the length of each inner_dim ('time') along each file_dim ('file_date')  
    length_inner_across_dim <- lapply(dat[[i]][['selectors']][[across_inner_dim]][['fri']], length)
    
    if (!split_multiselected_dims) {
      final_dims_fake_name <- names(final_dims_fake)
      pos_across_inner_dim <- which(final_dims_fake_name == across_inner_dim)
      new_length_inner_dim <- sum(unlist(length_inner_across_dim))
      if (pos_across_inner_dim != length(final_dims_fake)) {
        final_dims_fake <- c(final_dims_fake[1:(pos_across_inner_dim - 1)],
                             new_length_inner_dim,
                             final_dims_fake[(pos_across_inner_dim + 1):length(final_dims_fake)])
      } else {
        final_dims_fake <- c(final_dims_fake[1:(pos_across_inner_dim - 1)],
                             new_length_inner_dim)
      }
      names(final_dims_fake) <- final_dims_fake_name
    }
  }
  
  if (!silent) {
    .message("Detected dimension sizes:")
    longest_dim_len <- max(sapply(names(final_dims_fake), nchar))
    longest_size_len <- max(sapply(paste0(final_dims_fake, ''), nchar))
    sapply(names(final_dims_fake), 
           function(x) {
             message(paste0("*   ", paste(rep(' ', longest_dim_len - nchar(x)), collapse = ''), 
                            x, ": ", paste(rep(' ', longest_size_len - nchar(paste0(final_dims_fake[x], ''))), collapse = ''), 
                            final_dims_fake[x]))
           })
    bytes <- prod(c(final_dims_fake, 8))
    dim_sizes <- paste(final_dims_fake, collapse = ' x ')
    if (retrieve) {
      .message(paste("Total size of requested data:"))
    } else {
      .message(paste("Total size of involved data:"))
    }
    .message(paste(dim_sizes, " x 8 bytes =", 
                   format(structure(bytes, class = "object_size"), units = "auto")), 
             indent = 2)
  }
  
  # NOTE: If split_multiselected_dims + merge_across_dims, the dim order may need to be changed.
  #       The inner_dim needs to be the first dim among split dims.
  #       Cannot control the rest dims are in the same order or not...
  #       Suppose users put the same order of across inner and file dims.
  if (split_multiselected_dims & merge_across_dims) {
    # TODO: More than one split?
    inner_dim_pos_in_split_dims <- which(names(all_split_dims[[1]]) == inner_dims_across_files)  
    # if inner_dim is not the first, change!
    if (inner_dim_pos_in_split_dims != 1) {
      split_dims <- c(split_dims[inner_dim_pos_in_split_dims],
                      split_dims[1:length(split_dims)][-inner_dim_pos_in_split_dims])
      split_dims_pos <- which(!is.na(match(names(final_dims_fake), names(split_dims))))
      # Save the current final_dims_fake for later reorder back
      final_dims_fake_output <- final_dims_fake
      new_dims <- c()
      if (split_dims_pos[1] != 1) {
        new_dims <- c(new_dims, final_dims_fake[1:(split_dims_pos[1] - 1)])
      }
      new_dims <- c(new_dims, split_dims)
      if (split_dims_pos[length(split_dims_pos)] < length(final_dims_fake)) {
        new_dims <- c(new_dims, final_dims_fake[(split_dims_pos[length(split_dims_pos)] + 1):length(final_dims_fake)])
      }
      final_dims_fake <- new_dims
    }
  }
  
  # The following several lines will only be run if retrieve = TRUE
  if (retrieve) {
    
    ########## CREATING THE SHARED MATRIX AND DISPATCHING WORK PIECES ###########
    # TODO: try performance of storing all in cols instead of rows
    # Create the shared memory array, and a pointer to it, to be sent
    # to the work pieces.
    data_array <- bigmemory::big.matrix(nrow = prod(final_dims), ncol = 1)
    shared_matrix_pointer <- bigmemory::describe(data_array)
    if (is.null(num_procs)) {
      num_procs <- future::availableCores()
    }
    # Creating a shared tmp folder to store metadata from each chunk
    array_of_metadata_flags <- array(FALSE, dim = dim(array_of_files_to_load))
    if (!is.null(metadata_dims)) {
      metadata_indices_to_load <- as.list(rep(1, length(dim(array_of_files_to_load))))
      names(metadata_indices_to_load) <- names(dim(array_of_files_to_load))
      metadata_indices_to_load[metadata_dims] <- as.list(rep(TRUE, length(metadata_dims)))
      array_of_metadata_flags <- do.call('[<-', c(list(array_of_metadata_flags),  metadata_indices_to_load,
                                                  list(value = rep(TRUE, prod(dim(array_of_files_to_load)[metadata_dims])))))
    }
    metadata_file_counter <- 0
    metadata_folder <- tempfile('metadata')
    dir.create(metadata_folder)
    # Build the work pieces, each with:
    # - file path
    # - total size (dims) of store array
    # - start position in store array
    # - file selectors (to provide extra info. useful e.g. to select variable)
    # - indices to take from file
    work_pieces <- list()
    for (i in 1:length(dat)) {
      if (dataset_has_files[i]) {
        selectors <- dat[[i]][['selectors']]
        file_dims <- found_file_dims[[i]]
        inner_dims <- expected_inner_dims[[i]]
        sub_array_dims <- final_dims[file_dims]
        sub_array_dims[found_pattern_dim] <- 1
        sub_array_of_files_to_load <- array(1:prod(sub_array_dims), 
                                            dim = sub_array_dims)
        names(dim(sub_array_of_files_to_load)) <- names(sub_array_dims)
        # Detect which of the dimensions of the dataset go across files.
        file_dim_across_files <- lapply(inner_dims, 
                                        function(x) {
                                          dim_across <- sapply(inner_dims_across_files, function(y) x %in% y)
                                          if (any(dim_across)) {
                                            names(inner_dims_across_files)[which(dim_across)[1]]
                                          } else {
                                            NULL
                                          }
                                        })
        names(file_dim_across_files) <- inner_dims
        j <- 1
        while (j <= prod(sub_array_dims)) {
          # Work out file path.
          file_to_load_sub_indices <- which(sub_array_of_files_to_load == j, arr.ind = TRUE)[1, ]
          names(file_to_load_sub_indices) <- names(sub_array_dims)
          file_to_load_sub_indices[found_pattern_dim] <- i
          big_dims <- rep(1, length(dim(array_of_files_to_load)))
          names(big_dims) <- names(dim(array_of_files_to_load))
          file_to_load_indices <- .MergeArrayDims(file_to_load_sub_indices, big_dims)[[1]]
          file_to_load <- do.call('[[', c(list(array_of_files_to_load), 
                                          as.list(file_to_load_indices)))
          not_found_file <- do.call('[[', c(list(array_of_not_found_files),
                                            as.list(file_to_load_indices)))
          load_file_metadata <- do.call('[', c(list(array_of_metadata_flags), 
                                               as.list(file_to_load_indices)))
          if (load_file_metadata) {
            metadata_file_counter <- metadata_file_counter + 1
          }
          if (!is.na(file_to_load) && !not_found_file) {
            # Work out indices to take
            first_round_indices <- lapply(inner_dims, 
                                          function (x) {
                                            if (is.null(file_dim_across_files[[x]])) {
                                              selectors[[x]][['fri']][[1]]
                                            } else {
                                              which_chunk <- file_to_load_sub_indices[file_dim_across_files[[x]]] 
                                              selectors[[x]][['fri']][[which_chunk]]
                                            }
                                          })
            names(first_round_indices) <- inner_dims
            second_round_indices <- lapply(inner_dims, 
                                           function (x) {
                                             if (is.null(file_dim_across_files[[x]])) {
                                               selectors[[x]][['sri']][[1]]
                                             } else {
                                               which_chunk <- file_to_load_sub_indices[file_dim_across_files[[x]]]
                                               selectors[[x]][['sri']][[which_chunk]]
                                             }
                                           })
            if (debug) {
              print("-> BUILDING A WORK PIECE")
              #print(str(selectors))
            }
            names(second_round_indices) <- inner_dims
            if (!any(sapply(first_round_indices, length) == 0)) {
              work_piece <- list()
              work_piece[['first_round_indices']] <- first_round_indices
              work_piece[['second_round_indices']] <- second_round_indices
              work_piece[['file_indices_in_array_of_files']] <- file_to_load_indices
              work_piece[['file_path']] <- file_to_load
              work_piece[['store_dims']] <- final_dims
              # Work out store position
              store_position <- final_dims
              store_position[names(file_to_load_indices)] <- file_to_load_indices
              store_position[inner_dims] <- rep(1, length(inner_dims))
              work_piece[['store_position']] <- store_position
              # Work out file selectors
              file_selectors <- sapply(file_dims, 
                                       function (x) {
                                         vector_to_pick <- 1
                                         if (x %in% names(depending_file_dims)) {
                                           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)
              }
              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 != '') {
        .message(progress_message, appendLF = FALSE)
      }
    }

# NOTE: In .LoadDataFile(), metadata is saved in metadata_folder/1 or /2 etc. Before here,
#       the path name is created in work_pieces but the path hasn't been built yet.
    if (num_procs == 1) {
      found_files <- lapply(work_pieces, .LoadDataFile, 
                            shared_matrix_pointer = shared_matrix_pointer,
                            file_data_reader = file_data_reader, 
                            synonims = synonims,
                            transform = transform, 
                            transform_params = transform_params,
                            silent = silent, debug = debug)
    } else {
      cluster <- parallel::makeCluster(num_procs, outfile = "")
      # Send the heavy work to the workers
      work_errors <- try({
        found_files <- parallel::clusterApplyLB(cluster, work_pieces, .LoadDataFile, 
                                                shared_matrix_pointer = shared_matrix_pointer,
                                                file_data_reader = file_data_reader,
                                                synonims = synonims,
                                                transform = transform, 
                                                transform_params = transform_params,
                                                silent = silent, debug = debug)
      })
      parallel::stopCluster(cluster)
    }
    
    if (!silent) {
      if (progress_message != '') {
        .message("\n", tag = '')
      }
    }
    #print("P")
    
    # NOTE: If merge_across_dims = TRUE, there might be additional NAs due to
    #       unequal inner_dim ('time') length across file_dim ('file_date').
    #       If merge_across_dims_narm = TRUE, add additional lines to remove these NAs.
    # TODO: Now it assumes that only one '_across'. Add a for loop for more-than-one case. 
    if (merge_across_dims_narm) {
      
      # Get the length of these two dimensions in final_dims
      length_inner_across_store_dims <- final_dims[across_inner_dim]
      length_file_across_store_dims <- final_dims[across_file_dim]
      
      # Create a logical array for merge_across_dims
      logi_array <- array(rep(FALSE,
                              length_file_across_store_dims * length_inner_across_store_dims),
                          dim = c(length_inner_across_store_dims, length_file_across_store_dims))
      for (i in 1:length_file_across_store_dims) {  #1:4
        logi_array[1:length_inner_across_dim[[i]], i] <- TRUE
      }
      
      # First, get the data array with final_dims dimension
      data_array_final_dims <- array(bigmemory::as.matrix(data_array), dim = final_dims)
      
      # Change the NA derived from additional spaces to -9999, then remove these -9999
      func_remove_blank <- function(data_array, logi_array) {
        # dim(data_array) = [time, file_date]
        # dim(logi_array) = [time, file_date]
        # Change the blank spaces from NA to -9999
        data_array[which(!logi_array)] <- -9999
        return(data_array)
      }
      data_array_final_dims <- multiApply::Apply(data_array_final_dims,
                                                 target_dims = c(across_inner_dim, across_file_dim),  #c('time', 'file_date')
                                                 output_dims = c(across_inner_dim, across_file_dim),
                                                 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