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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,206 @@ | ||
| .reduce_tile <- function(tile, | ||
| block, | ||
| expr, | ||
| out_band, | ||
| in_bands, | ||
| output_dir, | ||
| progress) { | ||
|
|
||
| # Output file | ||
| out_file <- .file_eo_name( | ||
| tile = tile, band = out_band, | ||
| date = .tile_start_date(tile), | ||
| output_dir = output_dir | ||
| ) | ||
| # Resume feature | ||
| if (.raster_is_valid(out_file, output_dir = output_dir)) { | ||
| # recovery message | ||
| .check_recovery(out_file) | ||
|
|
||
| # Create tile based on template | ||
| tile <- .tile_eo_from_files( | ||
| files = out_file, fid = .fi_fid(.fi(tile)), | ||
| bands = out_band, date = .tile_start_date(tile), | ||
| base_tile = tile, update_bbox = FALSE | ||
| ) | ||
| return(tile) | ||
| } | ||
| # Remove remaining incomplete fractions files | ||
| unlink(out_file) | ||
| # Create chunks as jobs | ||
| chunks <- .tile_chunks_create( | ||
| tile = tile, overlap = 0, block = block | ||
| ) | ||
| # Filter tile bands | ||
| if (.band_cloud() %in% .tile_bands(tile)) { | ||
| in_bands <- c(in_bands, .band_cloud()) | ||
| } | ||
| tile <- .tile_filter_bands(tile, in_bands) | ||
| # Process jobs in parallel | ||
| block_files <- .jobs_map_parallel_chr(chunks, function(chunk) { | ||
| # Get job block | ||
| block <- .block(chunk) | ||
| # Block file name | ||
| block_file <- .file_block_name( | ||
| pattern = .file_pattern(out_file), | ||
| block = block, | ||
| output_dir = output_dir | ||
| ) | ||
| # Resume processing in case of failure | ||
| if (.raster_is_valid(block_file)) { | ||
| return(block_file) | ||
| } | ||
| # Read and preprocess values | ||
| values <- .classify_data_read( | ||
| tile = tile, | ||
| block = block, | ||
| bands = in_bands, | ||
| ml_model = NULL, | ||
| filter_fn = NULL | ||
| ) | ||
| # Convert to named list | ||
| values <- list(values) | ||
| names(values) <- in_bands | ||
| # Evaluate expression here | ||
| # Band and kernel evaluation | ||
| values <- eval( | ||
| expr = expr[[out_band]], | ||
| envir = values, | ||
| enclos = .temp_functions() | ||
| ) | ||
| # Prepare fractions to be saved | ||
| band_conf <- .tile_band_conf(tile = tile, band = out_band) | ||
| offset <- .offset(band_conf) | ||
| if (.has(offset) && offset != 0) { | ||
| values <- values - offset | ||
| } | ||
| scale <- .scale(band_conf) | ||
| if (.has(scale) && scale != 1) { | ||
| values <- values / scale | ||
| } | ||
| # Job crop block | ||
| crop_block <- .block(.chunks_no_overlap(chunk)) | ||
| # Prepare and save results as raster | ||
| .raster_write_block( | ||
| files = block_file, block = block, bbox = .bbox(chunk), | ||
| values = values, data_type = .data_type(band_conf), | ||
| missing_value = .miss_value(band_conf), | ||
| crop_block = crop_block | ||
| ) | ||
| # Free memory | ||
| gc() | ||
| # Returned block files for each fraction | ||
| block_file | ||
|
|
||
| }, progress = progress) | ||
| # Merge blocks into a new eo_cube tile | ||
| band_tile <- .tile_eo_merge_blocks( | ||
| files = out_file, | ||
| bands = out_band, | ||
| base_tile = tile, | ||
| block_files = block_files, | ||
| multicores = 1, | ||
| update_bbox = FALSE | ||
| ) | ||
| # Return a reduced tile | ||
| band_tile | ||
| } | ||
|
|
||
| .reduce_samples <- function(data, expr, in_band, out_band) { | ||
| col <- "time_series" | ||
| min_date <- min(.samples_timeline(data)) | ||
| # Pre-condition | ||
| .check_chr_within( | ||
| col, | ||
| within = names(data), | ||
| msg = "invalid column name" | ||
| ) | ||
| # Select data do unpack | ||
| x <- data[col] | ||
| # Prepare to unpack | ||
| x[["#.."]] <- as.factor(seq_len(nrow(data))) | ||
| # Unpack | ||
| x <- tidyr::unnest(x, cols = dplyr::all_of(col)) | ||
| # Apply the temporal operation | ||
| x <- by(x, x[, "#.."], function(y) { | ||
| y <- list(t(y[[in_band]])) | ||
| names(y) <- in_band | ||
| eval( | ||
| expr = expr[[out_band]], | ||
| envir = y, | ||
| enclos = .temp_functions() | ||
| ) | ||
| }) | ||
| # Unlist results | ||
| x <- unlist(x) | ||
| # Create a data frame | ||
| x <- tibble::tibble(x) | ||
| colnames(x) <- out_band | ||
| x[["#.."]] <- as.factor(seq_len(nrow(x))) | ||
| # Add index column | ||
| x <- tibble::add_column(x, Index = min_date, .before = out_band) | ||
| # pack | ||
| x <- tidyr::nest(x, `..unnest_col` = -"#..") | ||
| # remove garbage | ||
| x[["#.."]] <- NULL | ||
| names(x) <- col | ||
| # prepare result | ||
| data[[col]] <- x[[col]] | ||
| return(data) | ||
| } | ||
|
|
||
| #' @title Temporal functions for reduce operations | ||
| #' @name .temp_functions | ||
| #' @noRd | ||
| #' @return operations on reduce function | ||
| .temp_functions <- function() { | ||
| result_env <- list2env(list( | ||
| t_max = function(m) { | ||
| C_temp_max(mtx = as.matrix(m)) | ||
| }, | ||
| t_min = function(m) { | ||
| C_temp_min(mtx = as.matrix(m)) | ||
| }, | ||
| t_mean = function(m) { | ||
| C_temp_mean(mtx = as.matrix(m)) | ||
| }, | ||
| t_median = function(m) { | ||
| C_temp_median(mtx = as.matrix(m)) | ||
| }, | ||
| t_sum = function(m) { | ||
| C_temp_sum(mtx = as.matrix(m)) | ||
| }, | ||
| t_std = function(m) { | ||
| C_temp_std(mtx = as.matrix(m)) | ||
| }, | ||
| t_skewness = function(m) { | ||
| C_temp_skew(mtx = as.matrix(m)) | ||
| }, | ||
| t_kurtosis = function(m) { | ||
| C_temp_kurt(mtx = as.matrix(m)) | ||
| }, | ||
| t_amplitude = function(m) { | ||
| C_temp_amplitude(mtx = as.matrix(m)) | ||
| }, | ||
| t_fslope = function(m) { | ||
| C_temp_fslope(mtx = as.matrix(m)) | ||
| }, | ||
| t_mse = function(m) { | ||
| C_temp_mse(mtx = as.matrix(m)) | ||
| }, | ||
| t_fqr = function(m) { | ||
| C_temp_fqr(mtx = as.matrix(m)) | ||
| }, | ||
| t_sqr = function(m) { | ||
| C_temp_sqr(mtx = as.matrix(m)) | ||
| }, | ||
| t_tqr = function(m) { | ||
| C_temp_tqr(mtx = as.matrix(m)) | ||
| }, | ||
| t_iqr = function(m) { | ||
| C_temp_iqr(mtx = as.matrix(m)) | ||
| } | ||
| ), parent = parent.env(environment()), hash = TRUE) | ||
|
|
||
| return(result_env) | ||
| } |
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