1140 lines
40 KiB
R
1140 lines
40 KiB
R
# 80_WEEKLY_STATS_UTILS.R
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# ============================================================================
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# UTILITY FUNCTIONS FOR WEEKLY STATISTICS CALCULATION
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#
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# This file contains reusable functions for:
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# - Tile grid management
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# - Tile loading and merging
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# - Field-level statistics calculation from CI rasters
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# - Weekly stats caching (RDS/CSV export/import)
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# - KPI trend calculations
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# - Historical data loading and auto-generation from mosaics
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#
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# Used by: 80_calculate_kpis.R, run_full_pipeline.R, other reporting scripts
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# ============================================================================
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# ============================================================================
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# WEEK/YEAR CALCULATION HELPERS (Consistent across all scripts)
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# ============================================================================
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#' Calculate week and year for a given lookback offset
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#' This function handles ISO 8601 week numbering with proper year wrapping
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#' when crossing year boundaries (e.g., week 01/2026 -> week 52/2025)
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#'
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#' @param current_week ISO week number (1-53)
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#' @param current_year ISO week year (from format(..., "%G"))
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#' @param offset_weeks Number of weeks to go back (0 = current week, 1 = previous week, etc.)
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#'
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#' @return List with: week (ISO week number), year (ISO week year)
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#'
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#' @details
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#' This is the authoritative week/year calculation function.
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#' Used by:
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#' - load_historical_field_data() - to find RDS/CSV files for 4-week lookback
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#' - Script 80 main - to calculate previous week with year wrapping
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#' - Any other script needing to walk backwards through weeks
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#'
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#' Example: Week 01/2026, offset=1 -> returns list(week=52, year=2025)
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calculate_target_week_and_year <- function(current_week, current_year, offset_weeks = 0) {
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target_week <- current_week - offset_weeks
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target_year <- current_year
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# Handle wrapping: when going back from week 1, wrap to week 52 of previous year
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while (target_week < 1) {
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target_week <- target_week + 52
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target_year <- target_year - 1
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}
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return(list(week = target_week, year = target_year))
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}
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# ============================================================================
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# TILE-AWARE HELPER FUNCTIONS
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# ============================================================================
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get_tile_ids_for_field <- function(field_geom, tile_grid, field_id = NULL) {
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if (inherits(field_geom, "sf")) {
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field_bbox <- sf::st_bbox(field_geom)
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field_xmin <- field_bbox["xmin"]
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field_xmax <- field_bbox["xmax"]
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field_ymin <- field_bbox["ymin"]
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field_ymax <- field_bbox["ymax"]
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} else if (inherits(field_geom, "SpatVector")) {
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field_bbox <- terra::ext(field_geom)
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field_xmin <- field_bbox$xmin
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field_xmax <- field_bbox$xmax
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field_ymin <- field_bbox$ymin
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field_ymax <- field_bbox$ymax
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} else {
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stop("field_geom must be sf or terra::vect object")
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}
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intersecting_tiles <- tile_grid$id[
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!(tile_grid$xmax < field_xmin |
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tile_grid$xmin > field_xmax |
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tile_grid$ymax < field_ymin |
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tile_grid$ymin > field_ymax)
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]
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return(as.numeric(intersecting_tiles))
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}
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load_tiles_for_field <- function(field_geom, tile_ids, week_num, year, mosaic_dir) {
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if (length(tile_ids) == 0) {
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return(NULL)
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}
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tiles_list <- list()
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for (tile_id in sort(tile_ids)) {
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tile_filename <- sprintf("week_%02d_%d_%02d.tif", week_num, year, tile_id)
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tile_path <- file.path(mosaic_dir, tile_filename)
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if (file.exists(tile_path)) {
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tryCatch({
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tile_rast <- terra::rast(tile_path)
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ci_band <- terra::subset(tile_rast, 5)
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tiles_list[[length(tiles_list) + 1]] <- ci_band
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}, error = function(e) {
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message(paste(" Warning: Could not load tile", tile_id, ":", e$message))
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})
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}
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}
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if (length(tiles_list) == 0) {
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return(NULL)
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}
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if (length(tiles_list) == 1) {
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return(tiles_list[[1]])
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} else {
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tryCatch({
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rsrc <- terra::sprc(tiles_list)
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merged <- terra::mosaic(rsrc, fun = "max")
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return(merged)
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}, error = function(e) {
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message(paste(" Warning: Could not merge tiles:", e$message))
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return(tiles_list[[1]])
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})
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}
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}
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build_tile_grid <- function(mosaic_dir, week_num, year) {
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# Handle grid-size subdirectories (e.g., weekly_tile_max/5x5/)
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detected_grid_size <- NA
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if (dir.exists(mosaic_dir)) {
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subfolders <- list.dirs(mosaic_dir, full.names = FALSE, recursive = FALSE)
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grid_patterns <- grep("^\\d+x\\d+$", subfolders, value = TRUE)
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if (length(grid_patterns) > 0) {
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detected_grid_size <- grid_patterns[1]
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mosaic_dir <- file.path(mosaic_dir, detected_grid_size)
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message(paste(" Using grid-size subdirectory:", detected_grid_size))
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}
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}
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tile_pattern <- sprintf("week_%02d_%d_([0-9]{2})\\.tif", week_num, year)
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tile_files <- list.files(mosaic_dir, pattern = tile_pattern, full.names = TRUE)
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if (length(tile_files) == 0) {
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stop(paste("No tile files found for week", week_num, year, "in", mosaic_dir))
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}
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tile_grid <- data.frame(
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id = integer(),
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xmin = numeric(),
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xmax = numeric(),
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ymin = numeric(),
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ymax = numeric(),
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stringsAsFactors = FALSE
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)
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for (tile_file in tile_files) {
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tryCatch({
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matches <- regmatches(basename(tile_file), regexpr("_([0-9]{2})\\.tif$", basename(tile_file)))
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if (length(matches) > 0) {
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tile_id <- as.integer(sub("_|\\.tif", "", matches[1]))
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tile_rast <- terra::rast(tile_file)
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tile_ext <- terra::ext(tile_rast)
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tile_grid <- rbind(tile_grid, data.frame(
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id = tile_id,
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xmin = tile_ext$xmin,
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xmax = tile_ext$xmax,
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ymin = tile_ext$ymin,
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ymax = tile_ext$ymax,
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stringsAsFactors = FALSE
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))
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}
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}, error = function(e) {
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message(paste(" Warning: Could not process tile", basename(tile_file), ":", e$message))
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})
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}
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if (nrow(tile_grid) == 0) {
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stop("Could not extract extents from any tile files")
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}
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return(list(
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tile_grid = tile_grid,
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mosaic_dir = mosaic_dir,
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grid_size = detected_grid_size
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))
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}
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# ============================================================================
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# STATISTICAL CATEGORIZATION FUNCTIONS
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# ============================================================================
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categorize_four_week_trend <- function(ci_values_list) {
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if (is.null(ci_values_list) || length(ci_values_list) < 2) {
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return(NA_character_)
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}
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ci_values_list <- ci_values_list[!is.na(ci_values_list)]
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if (length(ci_values_list) < 2) {
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return(NA_character_)
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}
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weekly_changes <- diff(ci_values_list)
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avg_weekly_change <- mean(weekly_changes, na.rm = TRUE)
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if (avg_weekly_change >= FOUR_WEEK_TREND_STRONG_GROWTH_MIN) {
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return("strong growth")
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} else if (avg_weekly_change >= FOUR_WEEK_TREND_GROWTH_MIN &&
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avg_weekly_change < FOUR_WEEK_TREND_GROWTH_MAX) {
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return("growth")
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} else if (abs(avg_weekly_change) <= FOUR_WEEK_TREND_NO_GROWTH_RANGE) {
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return("no growth")
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} else if (avg_weekly_change <= FOUR_WEEK_TREND_DECLINE_MIN &&
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avg_weekly_change > FOUR_WEEK_TREND_STRONG_DECLINE_MAX) {
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return("decline")
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} else if (avg_weekly_change < FOUR_WEEK_TREND_STRONG_DECLINE_MAX) {
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return("strong decline")
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} else {
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return("no growth")
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}
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}
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round_cloud_to_intervals <- function(cloud_pct_clear) {
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if (is.na(cloud_pct_clear)) {
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return(NA_character_)
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}
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if (cloud_pct_clear < 10) return("0-10%")
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if (cloud_pct_clear < 20) return("10-20%")
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if (cloud_pct_clear < 30) return("20-30%")
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if (cloud_pct_clear < 40) return("30-40%")
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if (cloud_pct_clear < 50) return("40-50%")
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if (cloud_pct_clear < 60) return("50-60%")
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if (cloud_pct_clear < 70) return("60-70%")
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if (cloud_pct_clear < 80) return("70-80%")
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if (cloud_pct_clear < 90) return("80-90%")
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if (cloud_pct_clear < 95) return("90-95%")
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return("95-100%")
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}
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get_ci_percentiles <- function(ci_values) {
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if (is.null(ci_values) || length(ci_values) == 0) {
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return(NA_character_)
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}
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ci_values <- ci_values[!is.na(ci_values)]
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if (length(ci_values) == 0) {
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return(NA_character_)
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}
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p10 <- quantile(ci_values, CI_PERCENTILE_LOW, na.rm = TRUE)
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p90 <- quantile(ci_values, CI_PERCENTILE_HIGH, na.rm = TRUE)
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return(sprintf("%.1f-%.1f", p10, p90))
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}
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calculate_cv_trend <- function(cv_current, cv_previous) {
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if (is.na(cv_current) || is.na(cv_previous)) {
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return(NA_real_)
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}
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return(round(cv_current - cv_previous, 4))
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}
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calculate_four_week_trend <- function(mean_ci_values) {
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#' Calculate four-week CI trend from available weeks
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#' Uses whatever weeks are available (1-4 weeks) to estimate trend
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if (is.null(mean_ci_values) || length(mean_ci_values) == 0) {
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return(NA_real_)
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}
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ci_clean <- mean_ci_values[!is.na(mean_ci_values)]
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if (length(ci_clean) < 2) {
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return(NA_real_)
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}
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trend <- ci_clean[length(ci_clean)] - ci_clean[1]
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return(round(trend, 2))
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}
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categorize_cv_slope <- function(slope) {
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#' Categorize CV slope (8-week regression) into field uniformity interpretation
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if (is.na(slope)) {
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return(NA_character_)
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}
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if (slope <= CV_SLOPE_IMPROVEMENT_MIN) {
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return("Excellent uniformity")
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} else if (slope < CV_SLOPE_HOMOGENOUS_MIN) {
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return("Homogenous growth")
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} else if (slope <= CV_SLOPE_HOMOGENOUS_MAX) {
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return("Homogenous growth")
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} else if (slope <= CV_SLOPE_PATCHINESS_MAX) {
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return("Minor patchiness")
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} else {
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return("Severe fragmentation")
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}
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}
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calculate_cv_trend_long_term <- function(cv_values) {
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#' Calculate 8-week CV trend via linear regression slope
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if (is.null(cv_values) || length(cv_values) == 0) {
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return(NA_real_)
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}
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cv_clean <- cv_values[!is.na(cv_values)]
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if (length(cv_clean) < 2) {
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return(NA_real_)
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}
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weeks <- seq_along(cv_clean)
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tryCatch({
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lm_fit <- lm(cv_clean ~ weeks)
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slope <- coef(lm_fit)["weeks"]
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return(round(as.numeric(slope), 4))
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}, error = function(e) {
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return(NA_real_)
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})
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}
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# ============================================================================
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# HELPER FUNCTIONS
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# ============================================================================
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get_phase_by_age <- function(age_weeks) {
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if (is.na(age_weeks)) return(NA_character_)
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for (i in seq_len(nrow(PHASE_DEFINITIONS))) {
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if (age_weeks >= PHASE_DEFINITIONS$age_start[i] &&
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age_weeks <= PHASE_DEFINITIONS$age_end[i]) {
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return(PHASE_DEFINITIONS$phase[i])
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}
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}
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return("Unknown")
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}
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get_status_trigger <- function(ci_values, ci_change, age_weeks) {
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if (is.na(age_weeks) || length(ci_values) == 0) return(NA_character_)
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ci_values <- ci_values[!is.na(ci_values)]
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if (length(ci_values) == 0) return(NA_character_)
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pct_above_2 <- sum(ci_values > 2) / length(ci_values) * 100
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pct_at_or_above_2 <- sum(ci_values >= 2) / length(ci_values) * 100
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ci_cv <- if (mean(ci_values, na.rm = TRUE) > 0) sd(ci_values) / mean(ci_values, na.rm = TRUE) else 0
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mean_ci <- mean(ci_values, na.rm = TRUE)
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if (age_weeks >= 0 && age_weeks <= 6) {
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if (pct_at_or_above_2 >= 70) {
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return("germination_complete")
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} else if (pct_above_2 > 10) {
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return("germination_started")
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}
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}
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if (age_weeks >= 45) {
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return("harvest_ready")
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}
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if (age_weeks > 6 && !is.na(ci_change) && ci_change < -1.5 && ci_cv < 0.25) {
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return("stress_detected_whole_field")
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}
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if (age_weeks > 6 && !is.na(ci_change) && ci_change > 1.5) {
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return("strong_recovery")
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}
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if (age_weeks >= 4 && age_weeks < 39 && !is.na(ci_change) && ci_change > 0.2) {
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return("growth_on_track")
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}
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if (age_weeks >= 39 && age_weeks < 45 && mean_ci > 3.5) {
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return("maturation_progressing")
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}
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return(NA_character_)
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}
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extract_planting_dates <- function(harvesting_data, field_boundaries_sf = NULL) {
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# Extract planting dates from harvest.xlsx (season_start column)
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# Returns: data.frame with columns (field_id, planting_date)
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if (is.null(harvesting_data) || nrow(harvesting_data) == 0) {
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message("Warning: No harvesting data available - planting dates will be NA.")
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if (!is.null(field_boundaries_sf)) {
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return(data.frame(
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field_id = field_boundaries_sf$field,
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planting_date = rep(as.Date(NA), nrow(field_boundaries_sf)),
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stringsAsFactors = FALSE
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))
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}
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return(NULL)
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}
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tryCatch({
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planting_dates <- harvesting_data %>%
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arrange(field, desc(season_start)) %>%
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distinct(field, .keep_all = TRUE) %>%
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select(field, season_start) %>%
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rename(field_id = field, planting_date = season_start) %>%
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filter(!is.na(planting_date)) %>%
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as.data.frame()
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message(paste("Extracted planting dates for", nrow(planting_dates), "fields from harvest.xlsx"))
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return(planting_dates)
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}, error = function(e) {
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message(paste("Error extracting planting dates:", e$message))
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return(NULL)
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})
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}
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# ============================================================================
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# MODULAR STATISTICS CALCULATION
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# ============================================================================
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calculate_field_statistics <- function(field_boundaries_sf, week_num, year,
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mosaic_dir, report_date = Sys.Date()) {
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message(paste("Calculating statistics for all fields - Week", week_num, year))
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# Support both tile-based and single-file mosaics
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tile_pattern <- sprintf("week_%02d_%d_([0-9]{2})\\.tif", week_num, year)
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single_file_pattern <- sprintf("week_%02d_%d\\.tif", week_num, year)
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# Try tile-based first
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tile_files <- list.files(mosaic_dir, pattern = tile_pattern, full.names = TRUE)
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# If no tiles, try single-file
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if (length(tile_files) == 0) {
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single_file <- list.files(mosaic_dir, pattern = single_file_pattern, full.names = TRUE)
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if (length(single_file) > 0) {
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message(paste(" Using single-file mosaic for week", week_num))
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tile_files <- single_file[1] # Use first match as single "tile"
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} else {
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stop(paste("No mosaic files found for week", week_num, year, "in", mosaic_dir))
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}
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}
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message(paste(" Found", length(tile_files), "mosaic file(s) for week", week_num))
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results_list <- list()
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fields_processed <- 0
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for (tile_idx in seq_along(tile_files)) {
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tile_file <- tile_files[tile_idx]
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tryCatch({
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current_rast <- terra::rast(tile_file)
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ci_band <- current_rast[["CI"]]
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if (is.null(ci_band) || !inherits(ci_band, "SpatRaster")) {
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message(paste(" [SKIP] Tile", basename(tile_file), "- CI band not found"))
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return(NULL)
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}
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extracted <- terra::extract(ci_band, field_boundaries_sf, na.rm = FALSE)
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unique_field_ids <- unique(extracted$ID[!is.na(extracted$ID)])
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for (field_poly_idx in unique_field_ids) {
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field_id <- field_boundaries_sf$field[field_poly_idx]
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ci_vals <- extracted$CI[extracted$ID == field_poly_idx]
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ci_vals <- ci_vals[!is.na(ci_vals)]
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if (length(ci_vals) == 0) {
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next
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}
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mean_ci <- mean(ci_vals, na.rm = TRUE)
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ci_std <- sd(ci_vals, na.rm = TRUE)
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cv <- if (mean_ci > 0) ci_std / mean_ci else NA_real_
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range_min <- min(ci_vals, na.rm = TRUE)
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range_max <- max(ci_vals, na.rm = TRUE)
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range_str <- sprintf("%.1f-%.1f", range_min, range_max)
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ci_percentiles_str <- get_ci_percentiles(ci_vals)
|
|
|
|
# Count pixels with CI >= 2 (germination threshold)
|
|
GERMINATION_CI_THRESHOLD <- 2.0
|
|
num_pixels_gte_2 <- sum(ci_vals >= GERMINATION_CI_THRESHOLD, na.rm = TRUE)
|
|
num_pixels_total <- length(ci_vals)
|
|
pct_pixels_gte_2 <- if (num_pixels_total > 0) round((num_pixels_gte_2 / num_pixels_total) * 100, 1) else 0
|
|
|
|
field_rows <- extracted[extracted$ID == field_poly_idx, ]
|
|
num_total <- nrow(field_rows)
|
|
num_data <- sum(!is.na(field_rows$CI))
|
|
pct_clear <- if (num_total > 0) round((num_data / num_total) * 100, 1) else 0
|
|
cloud_cat <- if (num_data == 0) "No image available"
|
|
else if (pct_clear >= 95) "Clear view"
|
|
else "Partial coverage"
|
|
|
|
# Age_week and Phase are now calculated in main script using actual planting dates
|
|
# Germination_progress is calculated in main script after Age_week is known
|
|
|
|
existing_idx <- which(sapply(results_list, function(x) x$Field_id) == field_id)
|
|
|
|
if (length(existing_idx) > 0) {
|
|
next
|
|
}
|
|
|
|
results_list[[length(results_list) + 1]] <- data.frame(
|
|
Field_id = field_id,
|
|
Mean_CI = round(mean_ci, 2),
|
|
CV = round(cv * 100, 2),
|
|
CI_range = range_str,
|
|
CI_Percentiles = ci_percentiles_str,
|
|
Pct_pixels_CI_gte_2 = pct_pixels_gte_2,
|
|
Cloud_pct_clear = pct_clear,
|
|
Cloud_category = cloud_cat,
|
|
stringsAsFactors = FALSE
|
|
)
|
|
|
|
fields_processed <- fields_processed + 1
|
|
}
|
|
|
|
message(paste(" Tile", tile_idx, "of", length(tile_files), "processed"))
|
|
|
|
}, error = function(e) {
|
|
message(paste(" [ERROR] Tile", basename(tile_file), ":", e$message))
|
|
})
|
|
}
|
|
|
|
if (length(results_list) == 0) {
|
|
stop(paste("No fields processed successfully for week", week_num))
|
|
}
|
|
|
|
stats_df <- dplyr::bind_rows(results_list)
|
|
message(paste(" ✓ Successfully calculated statistics for", nrow(stats_df), "fields"))
|
|
|
|
return(stats_df)
|
|
}
|
|
|
|
# ============================================================================
|
|
# CALCULATE KPI TRENDS
|
|
# ============================================================================
|
|
|
|
calculate_kpi_trends <- function(current_stats, prev_stats = NULL,
|
|
project_dir = NULL, reports_dir = NULL,
|
|
current_week = NULL, year = NULL) {
|
|
|
|
message("Calculating KPI trends from current and previous week data")
|
|
|
|
current_stats$Weekly_ci_change <- NA_real_
|
|
current_stats$CV_Trend_Short_Term <- NA_real_
|
|
current_stats$Four_week_trend <- NA_real_
|
|
current_stats$CV_Trend_Long_Term <- NA_real_
|
|
current_stats$nmr_of_weeks_analysed <- 1L
|
|
|
|
if (is.null(prev_stats) || nrow(prev_stats) == 0) {
|
|
message(" No previous week data available - using defaults")
|
|
return(current_stats)
|
|
}
|
|
|
|
message(paste(" prev_stats has", nrow(prev_stats), "rows and", ncol(prev_stats), "columns"))
|
|
|
|
prev_lookup <- setNames(seq_len(nrow(prev_stats)), prev_stats$Field_id)
|
|
|
|
prev_field_analysis <- NULL
|
|
|
|
tryCatch({
|
|
analysis_dir <- file.path(reports_dir, "kpis", "field_analysis")
|
|
if (dir.exists(analysis_dir)) {
|
|
analysis_files <- list.files(analysis_dir, pattern = "_field_analysis_week.*\\.csv$", full.names = TRUE)
|
|
if (length(analysis_files) > 0) {
|
|
recent_file <- analysis_files[which.max(file.info(analysis_files)$mtime)]
|
|
prev_field_analysis <- readr::read_csv(recent_file, show_col_types = FALSE,
|
|
col_select = c(Field_id, nmr_of_weeks_analysed, Phase))
|
|
}
|
|
}
|
|
}, error = function(e) {
|
|
message(paste(" Note: Could not load previous field_analysis for nmr_weeks tracking:", e$message))
|
|
})
|
|
|
|
if (!is.null(prev_field_analysis) && nrow(prev_field_analysis) > 0) {
|
|
message(paste(" Using previous field_analysis to track nmr_of_weeks_analysed"))
|
|
}
|
|
|
|
historical_4weeks <- list()
|
|
historical_8weeks <- list()
|
|
|
|
if (!is.null(project_dir) && !is.null(reports_dir) && !is.null(current_week)) {
|
|
message(" Loading historical field_stats for 4-week and 8-week trends...")
|
|
|
|
for (lookback in 1:4) {
|
|
target_week <- current_week - lookback
|
|
target_year <- year
|
|
if (target_week < 1) {
|
|
target_week <- target_week + 52
|
|
target_year <- target_year - 1
|
|
}
|
|
|
|
rds_filename <- sprintf("%s_field_stats_week%02d_%d.rds", project_dir, target_week, target_year)
|
|
rds_path <- file.path(reports_dir, "kpis", "field_stats", rds_filename)
|
|
|
|
if (file.exists(rds_path)) {
|
|
tryCatch({
|
|
stats_data <- readRDS(rds_path)
|
|
historical_4weeks[[length(historical_4weeks) + 1]] <- list(
|
|
week = target_week,
|
|
stats = stats_data
|
|
)
|
|
}, error = function(e) {
|
|
message(paste(" Warning: Could not load week", target_week, ":", e$message))
|
|
})
|
|
}
|
|
}
|
|
|
|
for (lookback in 1:8) {
|
|
target_week <- current_week - lookback
|
|
target_year <- year
|
|
if (target_week < 1) {
|
|
target_week <- target_week + 52
|
|
target_year <- target_year - 1
|
|
}
|
|
|
|
rds_filename <- sprintf("%s_field_stats_week%02d_%d.rds", project_dir, target_week, target_year)
|
|
rds_path <- file.path(reports_dir, "kpis", "field_stats", rds_filename)
|
|
|
|
if (file.exists(rds_path)) {
|
|
tryCatch({
|
|
stats_data <- readRDS(rds_path)
|
|
historical_8weeks[[length(historical_8weeks) + 1]] <- list(
|
|
week = target_week,
|
|
stats = stats_data
|
|
)
|
|
}, error = function(e) {
|
|
# Silently skip
|
|
})
|
|
}
|
|
}
|
|
|
|
if (length(historical_4weeks) > 0) {
|
|
message(paste(" Loaded", length(historical_4weeks), "weeks for 4-week trend"))
|
|
}
|
|
if (length(historical_8weeks) > 0) {
|
|
message(paste(" Loaded", length(historical_8weeks), "weeks for 8-week CV trend"))
|
|
}
|
|
}
|
|
|
|
cv_trends_calculated <- 0
|
|
four_week_trends_calculated <- 0
|
|
cv_long_term_calculated <- 0
|
|
|
|
for (i in seq_len(nrow(current_stats))) {
|
|
field_id <- current_stats$Field_id[i]
|
|
prev_idx <- prev_lookup[field_id]
|
|
|
|
if (!is.na(prev_idx) && prev_idx > 0 && prev_idx <= nrow(prev_stats)) {
|
|
prev_row <- prev_stats[prev_idx, , drop = FALSE]
|
|
|
|
prev_ci <- prev_row$Mean_CI[1]
|
|
if (!is.na(prev_ci) && !is.na(current_stats$Mean_CI[i])) {
|
|
current_stats$Weekly_ci_change[i] <-
|
|
round(current_stats$Mean_CI[i] - prev_ci, 2)
|
|
}
|
|
|
|
prev_cv <- prev_row$CV[1]
|
|
if (!is.na(prev_cv) && !is.na(current_stats$CV[i])) {
|
|
current_stats$CV_Trend_Short_Term[i] <-
|
|
calculate_cv_trend(current_stats$CV[i], prev_cv)
|
|
cv_trends_calculated <- cv_trends_calculated + 1
|
|
}
|
|
|
|
if (length(historical_4weeks) > 0) {
|
|
ci_values_4week <- numeric()
|
|
|
|
for (hist_idx in rev(seq_along(historical_4weeks))) {
|
|
hist_data <- historical_4weeks[[hist_idx]]$stats
|
|
hist_field <- which(hist_data$Field_id == field_id)
|
|
if (length(hist_field) > 0 && !is.na(hist_data$Mean_CI[hist_field[1]])) {
|
|
ci_values_4week <- c(ci_values_4week, hist_data$Mean_CI[hist_field[1]])
|
|
}
|
|
}
|
|
|
|
ci_values_4week <- c(ci_values_4week, current_stats$Mean_CI[i])
|
|
|
|
if (length(ci_values_4week) >= 2) {
|
|
current_stats$Four_week_trend[i] <- calculate_four_week_trend(ci_values_4week)
|
|
four_week_trends_calculated <- four_week_trends_calculated + 1
|
|
}
|
|
}
|
|
|
|
if (length(historical_8weeks) > 0) {
|
|
cv_values_8week <- numeric()
|
|
|
|
for (hist_idx in rev(seq_along(historical_8weeks))) {
|
|
hist_data <- historical_8weeks[[hist_idx]]$stats
|
|
hist_field <- which(hist_data$Field_id == field_id)
|
|
if (length(hist_field) > 0 && !is.na(hist_data$CV[hist_field[1]])) {
|
|
cv_values_8week <- c(cv_values_8week, hist_data$CV[hist_field[1]])
|
|
}
|
|
}
|
|
|
|
cv_values_8week <- c(cv_values_8week, current_stats$CV[i])
|
|
|
|
if (length(cv_values_8week) >= 2) {
|
|
slope <- calculate_cv_trend_long_term(cv_values_8week)
|
|
current_stats$CV_Trend_Long_Term[i] <- slope
|
|
cv_long_term_calculated <- cv_long_term_calculated + 1
|
|
}
|
|
}
|
|
|
|
if (!is.null(prev_field_analysis) && nrow(prev_field_analysis) > 0) {
|
|
prev_analysis_row <- prev_field_analysis %>%
|
|
dplyr::filter(Field_id == field_id)
|
|
|
|
if (nrow(prev_analysis_row) > 0) {
|
|
prev_nmr_weeks_analysis <- prev_analysis_row$nmr_of_weeks_analysed[1]
|
|
|
|
# Only increment nmr_of_weeks_analysed if we have previous data
|
|
if (!is.na(prev_nmr_weeks_analysis)) {
|
|
current_stats$nmr_of_weeks_analysed[i] <- prev_nmr_weeks_analysis + 1L
|
|
} else {
|
|
current_stats$nmr_of_weeks_analysed[i] <- 1L
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
message(paste(" ✓ Calculated CV_Trend_Short_Term:", cv_trends_calculated, "fields"))
|
|
message(paste(" ✓ Calculated Four_week_trend:", four_week_trends_calculated, "fields"))
|
|
message(paste(" ✓ Calculated CV_Trend_Long_Term:", cv_long_term_calculated, "fields"))
|
|
return(current_stats)
|
|
}
|
|
|
|
# ============================================================================
|
|
# LOAD PER-FIELD DAILY RDS DATA (NEW ARCHITECTURE)
|
|
# ============================================================================
|
|
|
|
#' Load per-field daily CI data from daily_vals/ directory
|
|
#'
|
|
#' Reads per-field daily RDS files (output from Script 20):
|
|
#' daily_vals/{FIELD}/{YYYY-MM-DD}.rds
|
|
#'
|
|
#' Filters to dates matching the week specified, and returns combined data for all fields.
|
|
#'
|
|
#' @param week_num ISO week number (1-53)
|
|
#' @param year ISO week year
|
|
#' @param daily_vals_dir Directory containing daily_vals/{FIELD}/ structure
|
|
#' @param field_boundaries_sf Field boundaries (for validation)
|
|
#' @return Data frame with columns: field, sub_field, Date, ci_mean, ci_sd, ... (per-field daily data)
|
|
#'
|
|
load_per_field_daily_rds <- function(week_num, year, daily_vals_dir, field_boundaries_sf = NULL) {
|
|
|
|
if (!dir.exists(daily_vals_dir)) {
|
|
warning(paste("daily_vals directory not found:", daily_vals_dir))
|
|
return(NULL)
|
|
}
|
|
|
|
# Calculate week date range
|
|
# Create a date in the specified ISO week
|
|
jan_4 <- as.Date(paste0(year, "-01-04"))
|
|
week_start <- jan_4 - (as.numeric(format(jan_4, "%w")) - 2) * 86400 + (week_num - 1) * 7 * 86400
|
|
week_end <- week_start + 6
|
|
|
|
# List all field directories
|
|
field_dirs <- list.dirs(daily_vals_dir, full.names = FALSE, recursive = FALSE)
|
|
|
|
if (length(field_dirs) == 0) {
|
|
warning(paste("No field subdirectories found in:", daily_vals_dir))
|
|
return(NULL)
|
|
}
|
|
|
|
combined_data <- data.frame()
|
|
|
|
# Loop through each field and load matching RDS files for this week
|
|
for (field in field_dirs) {
|
|
field_path <- file.path(daily_vals_dir, field)
|
|
|
|
# Find all RDS files in this field directory
|
|
rds_files <- list.files(field_path, pattern = "\\.rds$", full.names = TRUE)
|
|
|
|
if (length(rds_files) == 0) {
|
|
next
|
|
}
|
|
|
|
# Filter to files within the week date range
|
|
for (rds_file in rds_files) {
|
|
# Extract date from filename: {FIELD}/{YYYY-MM-DD}.rds
|
|
date_str <- tools::file_path_sans_ext(basename(rds_file))
|
|
file_date <- tryCatch(as.Date(date_str), error = function(e) NA)
|
|
|
|
if (is.na(file_date) || file_date < week_start || file_date > week_end) {
|
|
next
|
|
}
|
|
|
|
# Load RDS file
|
|
tryCatch({
|
|
rds_data <- readRDS(rds_file)
|
|
rds_data$Date <- file_date
|
|
combined_data <- rbind(combined_data, rds_data)
|
|
}, error = function(e) {
|
|
warning(paste("Could not load RDS file:", rds_file, "-", e$message))
|
|
})
|
|
}
|
|
}
|
|
|
|
if (nrow(combined_data) == 0) {
|
|
warning(paste("No RDS data found for week", week_num, "in", daily_vals_dir))
|
|
return(NULL)
|
|
}
|
|
|
|
return(combined_data)
|
|
}
|
|
|
|
# ============================================================================
|
|
# LOAD OR CALCULATE WEEKLY STATISTICS
|
|
# ============================================================================
|
|
|
|
load_or_calculate_weekly_stats <- function(week_num, year, project_dir, field_boundaries_sf,
|
|
mosaic_dir, reports_dir, report_date = Sys.Date()) {
|
|
|
|
rds_filename <- sprintf("%s_field_stats_week%02d_%d.rds", project_dir, week_num, year)
|
|
rds_path <- file.path(reports_dir, "kpis", "field_stats", rds_filename)
|
|
|
|
if (file.exists(rds_path)) {
|
|
message(paste("Loading cached statistics from:", basename(rds_path)))
|
|
return(readRDS(rds_path))
|
|
}
|
|
|
|
message(paste("Cached RDS not found, calculating statistics from tiles for week", week_num))
|
|
stats_df <- calculate_field_statistics(field_boundaries_sf, week_num, year,
|
|
mosaic_dir, report_date)
|
|
|
|
output_dir <- file.path(reports_dir, "kpis", "field_stats")
|
|
if (!dir.exists(output_dir)) {
|
|
dir.create(output_dir, recursive = TRUE, showWarnings = FALSE)
|
|
}
|
|
|
|
saveRDS(stats_df, rds_path)
|
|
message(paste("Saved weekly statistics RDS:", basename(rds_path)))
|
|
|
|
csv_filename <- sprintf("%s_field_stats_week%02d_%d.csv", project_dir, week_num, year)
|
|
csv_path <- file.path(output_dir, csv_filename)
|
|
readr::write_csv(stats_df, csv_path)
|
|
message(paste("Saved weekly statistics CSV:", basename(csv_path)))
|
|
|
|
return(stats_df)
|
|
}
|
|
|
|
load_historical_field_data <- function(project_dir, current_week, current_year, reports_dir, num_weeks = 4, auto_generate = TRUE, field_boundaries_sf = NULL, daily_vals_dir = NULL) {
|
|
|
|
# NEW ARCHITECTURE: Try per-field daily RDS first
|
|
# If not available, fall back to consolidated RDS
|
|
|
|
# Determine daily_vals_dir if not provided
|
|
if (is.null(daily_vals_dir)) {
|
|
daily_vals_dir <- file.path("laravel_app", "storage", "app", project_dir, "Data", "extracted_ci", "daily_vals")
|
|
}
|
|
|
|
message(paste("Loading historical data from:", ifelse(dir.exists(daily_vals_dir), "per-field daily RDS", "consolidated RDS")))
|
|
|
|
historical_data <- list()
|
|
loaded_weeks <- c()
|
|
missing_weeks <- c()
|
|
|
|
# Try per-field daily RDS first
|
|
use_per_field <- dir.exists(daily_vals_dir)
|
|
|
|
if (use_per_field) {
|
|
message(paste(" Attempting to load from per-field RDS in:", daily_vals_dir))
|
|
|
|
for (lookback in 0:(num_weeks - 1)) {
|
|
target <- calculate_target_week_and_year(current_week, current_year, offset_weeks = lookback)
|
|
target_week <- target$week
|
|
target_year <- target$year
|
|
|
|
# Load from per-field daily RDS
|
|
per_field_data <- load_per_field_daily_rds(target_week, target_year, daily_vals_dir, field_boundaries_sf)
|
|
|
|
if (!is.null(per_field_data) && nrow(per_field_data) > 0) {
|
|
# Aggregate to field-week level
|
|
week_stats <- per_field_data %>%
|
|
dplyr::group_by(field) %>%
|
|
dplyr::summarise(
|
|
Field_id = dplyr::first(field),
|
|
Mean_CI = mean(ci_mean, na.rm = TRUE),
|
|
CI_SD = mean(ci_sd, na.rm = TRUE),
|
|
CV = mean(ci_sd / ci_mean, na.rm = TRUE),
|
|
.groups = "drop"
|
|
)
|
|
|
|
historical_data[[lookback + 1]] <- list(
|
|
week = target_week,
|
|
year = target_year,
|
|
data = week_stats
|
|
)
|
|
loaded_weeks <- c(loaded_weeks, paste0("week", sprintf("%02d_%d", target_week, target_year)))
|
|
} else {
|
|
missing_weeks <- c(missing_weeks, paste0("week", sprintf("%02d_%d", target_week, target_year)))
|
|
}
|
|
}
|
|
}
|
|
|
|
if (length(historical_data) == 0) {
|
|
message(paste("Error: No historical data found"))
|
|
return(NULL)
|
|
}
|
|
|
|
message(paste("✓ Loaded", length(historical_data), "weeks:", paste(loaded_weeks, collapse = ", ")))
|
|
|
|
return(historical_data)
|
|
}
|
|
|
|
#' [OLD CONSOLIDATED RDS FALLBACK - KEPT FOR REFERENCE]
|
|
#' This function is now replaced by per-field RDS loading above.
|
|
#' Keeping it as a comment for potential fallback logic.
|
|
|
|
load_historical_field_data_consolidated <- function(project_dir, current_week, current_year, reports_dir, num_weeks = 4, auto_generate = TRUE, field_boundaries_sf = NULL) {
|
|
historical_data <- list()
|
|
loaded_weeks <- c()
|
|
missing_weeks <- c()
|
|
|
|
for (lookback in 0:(num_weeks - 1)) {
|
|
# Calculate target week and year using authoritative helper (handles year boundaries)
|
|
target <- calculate_target_week_and_year(current_week, current_year, offset_weeks = lookback)
|
|
target_week <- target$week
|
|
target_year <- target$year
|
|
|
|
# Construct filename with BOTH week and year (proper ISO format)
|
|
csv_filename <- paste0(project_dir, "_field_analysis_week", sprintf("%02d_%d", target_week, target_year), ".csv")
|
|
csv_path <- file.path(reports_dir, "kpis", "field_analysis", csv_filename)
|
|
|
|
if (file.exists(csv_path)) {
|
|
tryCatch({
|
|
data <- read_csv(csv_path, show_col_types = FALSE)
|
|
historical_data[[lookback + 1]] <- list(
|
|
week = target_week,
|
|
year = target_year,
|
|
data = data
|
|
)
|
|
loaded_weeks <- c(loaded_weeks, paste0("week", sprintf("%02d_%d", target_week, target_year)))
|
|
}, error = function(e) {
|
|
message(paste(" Warning: Could not load week", target_week, "/", target_year, ":", e$message))
|
|
missing_weeks <<- c(missing_weeks, paste0("week", sprintf("%02d_%d", target_week, target_year)))
|
|
})
|
|
} else {
|
|
missing_weeks <- c(missing_weeks, paste0("week", sprintf("%02d_%d", target_week, target_year)))
|
|
}
|
|
}
|
|
|
|
if (length(missing_weeks) > 0 && auto_generate) {
|
|
message(paste("⚠ Missing weeks:", paste(missing_weeks, collapse = ", ")))
|
|
message("Scanning for ALL available weekly mosaics and calculating stats...\n")
|
|
|
|
if (is.null(field_boundaries_sf)) {
|
|
message(" Error: field_boundaries_sf not provided - cannot auto-generate")
|
|
return(historical_data)
|
|
}
|
|
|
|
if (!exists("weekly_tile_max")) {
|
|
message(" ✗ weekly_tile_max path not defined")
|
|
return(historical_data)
|
|
}
|
|
|
|
check_paths <- c(file.path(weekly_tile_max, "5x5"), weekly_tile_max)
|
|
mosaic_scan_dir <- NA
|
|
|
|
for (check_path in check_paths) {
|
|
if (dir.exists(check_path)) {
|
|
tif_files <- list.files(check_path, pattern = "week_.*\\.tif$", full.names = TRUE)
|
|
if (length(tif_files) > 0) {
|
|
mosaic_scan_dir <- check_path
|
|
break
|
|
}
|
|
}
|
|
}
|
|
|
|
if (is.na(mosaic_scan_dir)) {
|
|
message(" ✗ No mosaic files found in weekly_tile_max")
|
|
return(historical_data)
|
|
}
|
|
|
|
weeks_to_load <- 8
|
|
today <- Sys.Date()
|
|
target_dates <- today - (0:(weeks_to_load - 1)) * 7
|
|
|
|
expected_weeks <- data.frame(
|
|
date = target_dates,
|
|
week = as.numeric(format(target_dates, "%V")),
|
|
year = as.numeric(format(target_dates, "%G")),
|
|
stringsAsFactors = FALSE
|
|
)
|
|
expected_weeks <- unique(expected_weeks)
|
|
|
|
message(paste(" Expected weeks (last 8 from", format(today, "%Y-%m-%d"), "):"))
|
|
for (i in seq_len(nrow(expected_weeks))) {
|
|
message(paste(" Week", sprintf("%02d", expected_weeks$week[i]), expected_weeks$year[i]))
|
|
}
|
|
message("")
|
|
|
|
tif_files <- list.files(mosaic_scan_dir, pattern = "week_([0-9]{2})_([0-9]{4})_[0-9]{2}\\.tif$",
|
|
full.names = FALSE)
|
|
|
|
available_weeks <- data.frame()
|
|
for (filename in tif_files) {
|
|
matches <- regmatches(filename, gregexpr("week_([0-9]{2})_([0-9]{4})", filename))[[1]]
|
|
if (length(matches) > 0) {
|
|
week_year <- strsplit(matches[1], "_")[[1]]
|
|
if (length(week_year) == 3) {
|
|
week_num <- as.numeric(week_year[2])
|
|
year_num <- as.numeric(week_year[3])
|
|
|
|
if (week_num %in% expected_weeks$week && year_num %in% expected_weeks$year) {
|
|
available_weeks <- rbind(available_weeks,
|
|
data.frame(week = week_num, year = year_num))
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
available_weeks <- unique(available_weeks)
|
|
available_weeks <- merge(available_weeks, expected_weeks[, c("week", "year", "date")], by = c("week", "year"))
|
|
available_weeks <- available_weeks[order(available_weeks$date, decreasing = TRUE), ]
|
|
|
|
if (nrow(available_weeks) == 0) {
|
|
message(" ✗ No matching mosaic files found")
|
|
message(paste(" Scanned directory:", mosaic_scan_dir))
|
|
return(historical_data)
|
|
}
|
|
|
|
message(paste(" Found", nrow(available_weeks), "week(s) with available mosaics:"))
|
|
|
|
for (i in seq_len(nrow(available_weeks))) {
|
|
week_to_calc <- available_weeks$week[i]
|
|
year_to_calc <- available_weeks$year[i]
|
|
date_to_calc <- available_weeks$date[i]
|
|
|
|
tile_pattern <- sprintf("week_%02d_%d_([0-9]{2})\\.tif", week_to_calc, year_to_calc)
|
|
tile_files <- list.files(mosaic_scan_dir, pattern = tile_pattern, full.names = TRUE)
|
|
|
|
if (length(tile_files) == 0) {
|
|
message(paste(" ✗ Week", sprintf("%02d", week_to_calc), year_to_calc, "- no tiles found"))
|
|
next
|
|
}
|
|
|
|
message(paste(" ✓ Week", sprintf("%02d", week_to_calc), year_to_calc, "-", length(tile_files), "mosaics"))
|
|
|
|
tryCatch({
|
|
week_stats <- load_or_calculate_weekly_stats(
|
|
week_num = week_to_calc,
|
|
year = year_to_calc,
|
|
project_dir = project_dir,
|
|
field_boundaries_sf = field_boundaries_sf,
|
|
mosaic_dir = mosaic_scan_dir,
|
|
reports_dir = reports_dir,
|
|
report_date = date_to_calc
|
|
)
|
|
|
|
if (!is.null(week_stats) && nrow(week_stats) > 0) {
|
|
message(paste(" ✓ Calculated stats for", nrow(week_stats), "fields"))
|
|
|
|
historical_data[[length(historical_data) + 1]] <- list(
|
|
week = week_to_calc,
|
|
year = year_to_calc,
|
|
data = week_stats
|
|
)
|
|
loaded_weeks <- c(loaded_weeks, paste0(week_to_calc, "_", year_to_calc))
|
|
}
|
|
}, error = function(e) {
|
|
message(paste(" ✗ Error:", e$message))
|
|
})
|
|
}
|
|
}
|
|
|
|
if (length(historical_data) == 0) {
|
|
message(paste("Error: No historical field data found and could not auto-generate weeks"))
|
|
return(NULL)
|
|
}
|
|
|
|
message(paste("✓ Loaded", length(historical_data), "weeks of historical data:",
|
|
paste(loaded_weeks, collapse = ", ")))
|
|
|
|
return(historical_data)
|
|
}
|
|
|
|
# ============================================================================
|
|
# HELPER: Extract field-level statistics from CI raster
|
|
# ============================================================================
|
|
|
|
extract_field_statistics_from_ci <- function(ci_band, field_boundaries_sf) {
|
|
#' Extract CI statistics for all fields from a single CI raster band
|
|
|
|
extract_result <- terra::extract(ci_band, field_boundaries_sf)
|
|
|
|
stats_list <- list()
|
|
|
|
for (field_idx in seq_len(nrow(field_boundaries_sf))) {
|
|
field_pixels <- extract_result[extract_result$ID == field_idx, 2]
|
|
pixels <- as.numeric(field_pixels[!is.na(field_pixels)])
|
|
|
|
if (length(pixels) == 0) {
|
|
stats_list[[field_idx]] <- data.frame(
|
|
field_idx = field_idx,
|
|
mean_ci = NA_real_,
|
|
cv = NA_real_,
|
|
p10 = NA_real_,
|
|
p90 = NA_real_,
|
|
min_ci = NA_real_,
|
|
max_ci = NA_real_,
|
|
pixel_count_valid = 0,
|
|
pixel_count_total = 0,
|
|
stringsAsFactors = FALSE
|
|
)
|
|
next
|
|
}
|
|
|
|
mean_val <- mean(pixels, na.rm = TRUE)
|
|
cv_val <- if (mean_val > 0) sd(pixels, na.rm = TRUE) / mean_val else NA_real_
|
|
p10_val <- quantile(pixels, probs = CI_PERCENTILE_LOW, na.rm = TRUE)[[1]]
|
|
p90_val <- quantile(pixels, probs = CI_PERCENTILE_HIGH, na.rm = TRUE)[[1]]
|
|
min_val <- min(pixels, na.rm = TRUE)
|
|
max_val <- max(pixels, na.rm = TRUE)
|
|
|
|
stats_list[[field_idx]] <- data.frame(
|
|
field_idx = field_idx,
|
|
mean_ci = mean_val,
|
|
cv = cv_val,
|
|
p10 = p10_val,
|
|
p90 = p90_val,
|
|
min_ci = min_val,
|
|
max_ci = max_val,
|
|
pixel_count_valid = length(pixels),
|
|
pixel_count_total = nrow(extract_result[extract_result$ID == field_idx, ]),
|
|
stringsAsFactors = FALSE
|
|
)
|
|
}
|
|
|
|
return(dplyr::bind_rows(stats_list))
|
|
}
|
|
|
|
# ============================================================================
|
|
# COMMENTED OUT / UNUSED FUNCTIONS (kept for future use)
|
|
# ============================================================================
|
|
|
|
# analyze_single_field <- function(field_idx, field_boundaries_sf, tile_grid, week_num, year,
|
|
# mosaic_dir, historical_data = NULL, planting_dates = NULL,
|
|
# report_date = Sys.Date(), harvest_imminence_data = NULL,
|
|
# harvesting_data = NULL) {
|
|
# # [Function kept as reference for parallel field analysis]
|
|
# # Currently replaced by calculate_field_statistics() for efficiency
|
|
# }
|