small issues

r_app/80_utils_agronomic_support.R

@@ -353,71 +353,75 @@ calculate_weed_presence_kpi <- function(ci_pixels_by_field) {
   return(result)
 }
 
-#' KPI 6: Calculate gap filling quality (data interpolation success)
-#'
-#' Measures how well cloud/missing data was interpolated during growth model
-#'
-#' @param ci_rds_path Path to combined CI RDS file (before/after interpolation)
-#'
-#' @return Data frame with gap-filling quality metrics
-calculate_gap_filling_kpi <- function(ci_rds_path) {
-  # If ci_rds_path is NULL or not a valid path, return placeholder
-  if (is.null(ci_rds_path) || !is.character(ci_rds_path) || length(ci_rds_path) == 0) {
-    return(NULL)
+#' Calculate Gap Filling Score KPI (placeholder)
+#' @param ci_raster Current week CI raster
+#' @param field_boundaries Field boundaries
+#' @return List with summary data frame and field-level results data frame
+calculate_gap_filling_kpi <- function(ci_raster, field_boundaries) {
+  safe_log("Calculating Gap Filling Score KPI (placeholder)")
+
+  # Handle both sf and SpatVector inputs
+  if (!inherits(field_boundaries, "SpatVector")) {
+    field_boundaries_vect <- terra::vect(field_boundaries)
+  } else {
+    field_boundaries_vect <- field_boundaries
   }
-  
-  # If ci_rds_path is a directory, find the cumulative CI file
-  if (dir.exists(ci_rds_path)) {
-    ci_files <- list.files(ci_rds_path, pattern = "^All_pivots.*\\.rds$", full.names = TRUE)
-    if (length(ci_files) == 0) {
-      return(NULL)
+
+  field_results <- data.frame()
+
+  for (i in seq_len(nrow(field_boundaries))) {
+    field_name <- field_boundaries$field[i]
+    sub_field_name <- field_boundaries$sub_field[i]
+    field_vect <- field_boundaries_vect[i]
+
+    # Extract CI values using helper function
+    ci_values <- extract_ci_values(ci_raster, field_vect)
+    valid_values <- ci_values[!is.na(ci_values) & is.finite(ci_values)]
+
+    if (length(valid_values) > 1) {
+      # Gap score using 2σ below median to detect outliers
+      median_ci <- median(valid_values)
+      sd_ci <- sd(valid_values)
+      outlier_threshold <- median_ci - (2 * sd_ci)
+      low_ci_pixels <- sum(valid_values < outlier_threshold)
+      total_pixels <- length(valid_values)
+      gap_score <- (low_ci_pixels / total_pixels) * 100
+
+      # Classify gap severity
+      gap_level <- dplyr::case_when(
+        gap_score < 10 ~ "Minimal",
+        gap_score < 25 ~ "Moderate", 
+        TRUE ~ "Significant"
+      )
+
+      field_results <- rbind(field_results, data.frame(
+        field = field_name,
+        sub_field = sub_field_name,
+        gap_level = gap_level,
+        gap_score = gap_score,
+        mean_ci = mean(valid_values),
+        outlier_threshold = outlier_threshold
+      ))
+    } else {
+      # Not enough valid data, fill with NA row
+      field_results <- rbind(field_results, data.frame(
+        field = field_name,
+        sub_field = sub_field_name,
+        gap_level = NA_character_,
+        gap_score = NA_real_,
+        mean_ci = NA_real_,
+        outlier_threshold = NA_real_
+      ))
     }
-    ci_rds_path <- ci_files[1]
   }
-  
-  if (!file.exists(ci_rds_path)) {
-    return(NULL)
-  }
-  
-  tryCatch({
-    ci_data <- readRDS(ci_rds_path)
-    
-    # ci_data should be a wide matrix: fields × weeks
-    # NA values = missing data before interpolation
-    # (Gap filling is done during growth model stage)
-    
-    result <- data.frame(
-      field_idx = seq_len(nrow(ci_data)),
-      na_percent_pre_interpolation = NA_real_,
-      na_percent_post_interpolation = NA_real_,
-      gap_filling_success = NA_character_,
-      stringsAsFactors = FALSE
-    )
-    
-    for (field_idx in seq_len(nrow(ci_data))) {
-      na_count <- sum(is.na(ci_data[field_idx, ]))
-      na_pct <- na_count / ncol(ci_data) * 100
-      
-      if (na_pct == 0) {
-        result$gap_filling_success[field_idx] <- "No gaps (100% data)"
-      } else if (na_pct < 10) {
-        result$gap_filling_success[field_idx] <- "Excellent"
-      } else if (na_pct < 25) {
-        result$gap_filling_success[field_idx] <- "Good"
-      } else if (na_pct < 40) {
-        result$gap_filling_success[field_idx] <- "Fair"
-      } else {
-        result$gap_filling_success[field_idx] <- "Poor"
-      }
-      
-      result$na_percent_pre_interpolation[field_idx] <- round(na_pct, 2)
-    }
-    
-    return(result)
-  }, error = function(e) {
-    message(paste("Error calculating gap filling KPI:", e$message))
-    return(NULL)
-  })
+
+  # Summarize results
+  gap_summary <- field_results %>%
+    dplyr::group_by(gap_level) %>%
+    dplyr::summarise(field_count = n(), .groups = 'drop') %>%
+    dplyr::mutate(percent = round((field_count / sum(field_count)) * 100, 1))
+
+  return(list(summary = gap_summary, field_results = field_results))
 }
 
 # ============================================================================

r_app/80_utils_common.R

@@ -822,7 +822,8 @@ load_historical_field_data <- function(project_dir, current_week, current_year,
     
     if (file.exists(csv_path)) {
       tryCatch({
-        data <- readr::read_csv(csv_path, show_col_types = FALSE)
+        data <- readr::read_csv(csv_path, show_col_types = FALSE, 
+                                col_types = readr::cols(.default = readr::col_character()))
         historical_data[[lookback + 1]] <- list(
           week = target_week,
           year = target_year,
@@ -878,7 +879,8 @@ calculate_kpi_trends <- function(current_stats, prev_stats = NULL,
       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, 
+        prev_field_analysis <- readr::read_csv(recent_file, show_col_types = FALSE,
+                                               col_types = readr::cols(.default = readr::col_character()),
                                                col_select = c(Field_id, nmr_of_weeks_analysed, Phase))
       }
     }
@@ -1143,7 +1145,7 @@ calculate_week_numbers <- function(report_date = Sys.Date()) {
   return(list(
     current_week = current_week,
     previous_week = previous_week,
-    year = current_year,
+    current_year = current_year,
     previous_year = previous_year
   ))
 }

r_app/90_CI_report_with_kpis_simple.Rmd

@@ -704,7 +704,7 @@ The following table provides a comprehensive overview of all monitored fields wi
 
 ```{r detailed_field_table, echo=FALSE, results='asis'}
 # Load CI quadrant data to get field ages
-CI_quadrant <- readRDS(here::here(paths$cumulative_ci_vals_dir, "All_pivots_Cumulative_CI_quadrant_year_v2.rds"))
+#CI_quadrant <- readRDS(here::here(paths$cumulative_ci_vals_dir, "All_pivots_Cumulative_CI_quadrant_year_v2.rds"))
 
 # Identify the current season for each field based on report_date
 # The current season is the one where the report_date falls within or shortly after the season

r_app/DEBUG_remove_date_tiffs.R

@@ -0,0 +1,272 @@
+# ==============================================================================
+# DEBUG_REMOVE_DATE_TIFFS.R
+# ==============================================================================
+# PURPOSE:
+#   Remove all TIFFs of a specific date from multiple storage folders.
+#   Useful for debugging/re-running parts of the pipeline without full re-download.
+#
+# USAGE:
+#   Rscript DEBUG_remove_date_tiffs.R [project] [date] [--dry-run] [--skip-merged] [--skip-field-tiles] [--skip-field-tiles-ci] [--skip-daily-vals]
+#
+# EXAMPLES:
+#   # Remove 2026-02-08 from all folders (WITH CONFIRMATION)
+#   Rscript DEBUG_remove_date_tiffs.R angata 2026-02-08
+#
+#   # Remove from all folders without confirmation
+#   Rscript DEBUG_remove_date_tiffs.R angata 2026-02-08 --no-confirm
+#
+#   # Dry run - show what WOULD be deleted without deleting
+#   Rscript DEBUG_remove_date_tiffs.R angata 2026-02-08 --dry-run
+#
+#   # Remove only from merged_tif and field_tiles, skip CI folders
+#   Rscript DEBUG_remove_date_tiffs.R angata 2026-02-08 --skip-field-tiles-ci --skip-daily-vals
+#
+#   # Remove from field_tiles_CI only
+#   Rscript DEBUG_remove_date_tiffs.R angata 2026-02-08 --skip-merged --skip-field-tiles --skip-daily-vals
+#
+# ==============================================================================
+
+# ==============================================================================
+# CONFIGURATION - TOGGLE WHICH FOLDERS TO DELETE FROM (DEFAULT: ALL)
+# ==============================================================================
+
+# Set these to FALSE to skip deletion from that folder
+DELETE_FROM_MERGED_TIF <- TRUE
+DELETE_FROM_FIELD_TILES <- TRUE
+DELETE_FROM_FIELD_TILES_CI <- TRUE
+DELETE_FROM_DAILY_VALS <- TRUE
+
+# Safety settings
+DRY_RUN <- FALSE              # Set to TRUE to preview deletions without actually deleting
+REQUIRE_CONFIRMATION <- TRUE  # Set to FALSE to delete without asking
+
+# ==============================================================================
+# MAIN FUNCTION
+# ==============================================================================
+
+main <- function() {
+  # Parse command-line arguments
+  args <- commandArgs(trailingOnly = TRUE)
+  
+  # Validate minimum arguments
+  if (length(args) < 2) {
+    cat("\n[ERROR] Missing arguments\n")
+    cat("Usage: Rscript DEBUG_remove_date_tiffs.R [project] [date] [options]\n\n")
+    cat("Examples:\n")
+    cat("  Rscript DEBUG_remove_date_tiffs.R angata 2026-02-08\n")
+    cat("  Rscript DEBUG_remove_date_tiffs.R angata 2026-02-08 --dry-run\n")
+    cat("  Rscript DEBUG_remove_date_tiffs.R angata 2026-02-08 --skip-field-tiles-ci\n\n")
+    cat("Options:\n")
+    cat("  --dry-run              Preview deletions without actually deleting\n")
+    cat("  --no-confirm           Delete without confirmation\n")
+    cat("  --skip-merged          Skip merged_tif folder\n")
+    cat("  --skip-field-tiles     Skip field_tiles folder\n")
+    cat("  --skip-field-tiles-ci  Skip field_tiles_CI folder\n")
+    cat("  --skip-daily-vals      Skip daily_vals folder\n\n")
+    quit(status = 1)
+  }
+  
+  # Parse positional arguments
+  project <- args[1]
+  date_str <- args[2]
+  
+  # Parse optional flags
+  if (length(args) >= 3) {
+    for (i in 3:length(args)) {
+      arg <- args[i]
+      
+      # Skip NA or empty arguments
+      if (is.na(arg) || nchar(arg) == 0) {
+        next
+      }
+      
+      if (arg == "--dry-run") {
+        DRY_RUN <<- TRUE
+      } else if (arg == "--no-confirm") {
+        REQUIRE_CONFIRMATION <<- FALSE
+      } else if (arg == "--skip-merged") {
+        DELETE_FROM_MERGED_TIF <<- FALSE
+      } else if (arg == "--skip-field-tiles") {
+        DELETE_FROM_FIELD_TILES <<- FALSE
+      } else if (arg == "--skip-field-tiles-ci") {
+        DELETE_FROM_FIELD_TILES_CI <<- FALSE
+      } else if (arg == "--skip-daily-vals") {
+        DELETE_FROM_DAILY_VALS <<- FALSE
+      }
+    }
+  }
+  
+  # Validate date format
+  date_obj <- tryCatch(
+    as.Date(date_str, format = "%Y-%m-%d"),
+    error = function(e) NULL
+  )
+  
+  if (is.na(date_obj)) {
+    cat(sprintf("[ERROR] Invalid date format: %s (expected YYYY-MM-DD)\n", date_str))
+    quit(status = 1)
+  }
+  
+  # ===========================================================================
+  # BUILD LIST OF FOLDERS & FILES TO DELETE
+  # ===========================================================================
+  
+  base_path <- file.path("laravel_app", "storage", "app", project)
+  
+  files_to_delete <- list()
+  
+  # FOLDER 1: merged_tif/{DATE}.tif
+  if (DELETE_FROM_MERGED_TIF) {
+    merged_tif_file <- file.path(base_path, "merged_tif", paste0(date_str, ".tif"))
+    if (file.exists(merged_tif_file)) {
+      files_to_delete[["merged_tif"]] <- merged_tif_file
+    }
+  }
+  
+  # FOLDER 2: field_tiles/{FIELD}/{DATE}.tif (per-field structure)
+  if (DELETE_FROM_FIELD_TILES) {
+    field_tiles_dir <- file.path(base_path, "field_tiles")
+    if (dir.exists(field_tiles_dir)) {
+      field_dirs <- list.dirs(field_tiles_dir, full.names = TRUE, recursive = FALSE)
+      for (field_dir in field_dirs) {
+        tif_file <- file.path(field_dir, paste0(date_str, ".tif"))
+        if (file.exists(tif_file)) {
+          folder_name <- basename(field_dir)
+          key <- paste0("field_tiles/", folder_name)
+          files_to_delete[[key]] <- tif_file
+        }
+      }
+    }
+  }
+  
+  # FOLDER 3: field_tiles_CI/{FIELD}/{DATE}.tif (per-field structure)
+  if (DELETE_FROM_FIELD_TILES_CI) {
+    field_tiles_ci_dir <- file.path(base_path, "field_tiles_CI")
+    if (dir.exists(field_tiles_ci_dir)) {
+      field_dirs <- list.dirs(field_tiles_ci_dir, full.names = TRUE, recursive = FALSE)
+      for (field_dir in field_dirs) {
+        tif_file <- file.path(field_dir, paste0(date_str, ".tif"))
+        if (file.exists(tif_file)) {
+          folder_name <- basename(field_dir)
+          key <- paste0("field_tiles_CI/", folder_name)
+          files_to_delete[[key]] <- tif_file
+        }
+      }
+    }
+  }
+  
+  # FOLDER 4: Data/extracted_ci/daily_vals/{SUBDIR}/{DATE}.rds (per-subdirectory structure)
+  if (DELETE_FROM_DAILY_VALS) {
+    daily_vals_dir <- file.path(base_path, "Data", "extracted_ci", "daily_vals")
+    if (dir.exists(daily_vals_dir)) {
+      subdirs <- list.dirs(daily_vals_dir, full.names = TRUE, recursive = FALSE)
+      for (subdir in subdirs) {
+        rds_file <- file.path(subdir, paste0(date_str, ".rds"))
+        if (file.exists(rds_file)) {
+          subdir_name <- basename(subdir)
+          key <- paste0("daily_vals/", subdir_name)
+          files_to_delete[[key]] <- rds_file
+        }
+      }
+    }
+  }
+  
+  # ===========================================================================
+  # SUMMARY & CONFIRMATION
+  # ===========================================================================
+  
+  cat("\n")
+  cat(strrep("=", 70), "\n")
+  cat("DELETE DATE TIFFS - SUMMARY\n")
+  cat(strrep("=", 70), "\n")
+  cat(sprintf("Project: %s\n", project))
+  cat(sprintf("Date: %s\n", date_str))
+  cat(sprintf("Dry run: %s\n", if (DRY_RUN) "YES" else "NO"))
+  cat(sprintf("Files to delete: %d\n", length(files_to_delete)))
+  cat("\n")
+  
+  if (length(files_to_delete) == 0) {
+    cat("[INFO] No files found to delete\n")
+    cat(strrep("=", 70), "\n\n")
+    quit(status = 0)
+  }
+  
+  # Count files by folder type for compact summary
+  folder_counts <- table(sapply(names(files_to_delete), function(key) strsplit(key, "/")[[1]][1]))
+  cat("Files to delete by folder:\n")
+  for (folder in names(folder_counts)) {
+    cat(sprintf("  %s: %d file%s\n", folder, folder_counts[folder], if (folder_counts[folder] != 1) "s" else ""))
+  }
+  cat(sprintf("  Total: %d file%s\n", length(files_to_delete), if (length(files_to_delete) != 1) "s" else ""))
+  cat("\n")
+  
+  # Ask for confirmation (unless --no-confirm flag was used)
+  if (REQUIRE_CONFIRMATION && !DRY_RUN) {
+    # Check if running in interactive mode
+    if (!interactive()) {
+      cat("\n[ERROR] Non-interactive mode detected (running via Rscript)\n")
+      cat("Cannot prompt for confirmation. Use --no-confirm flag to proceed:\n")
+      cat("  Rscript DEBUG_remove_date_tiffs.R angata 2026-02-08 --no-confirm\n\n")
+      cat(strrep("=", 70), "\n\n")
+      quit(status = 1)
+    }
+    
+    cat("⚠️  This will PERMANENTLY DELETE the above files!\n")
+    cat("Use --no-confirm flag to skip this prompt\n")
+    
+    # Use readline() for interactive input (only works in interactive R/RStudio)
+    response <- readline(prompt = "Type 'yes' to confirm, or anything else to cancel: ")
+    
+    if (tolower(response) != "yes") {
+      cat("[CANCELLED] No files deleted\n")
+      cat(strrep("=", 70), "\n\n")
+      quit(status = 0)
+    }
+  }
+  
+  # ===========================================================================
+  # DELETE OR DRY-RUN
+  # ===========================================================================
+  
+  deleted_count <- 0
+  error_count <- 0
+  
+  for (i in seq_along(files_to_delete)) {
+    folder_key <- names(files_to_delete)[i]
+    file_path <- files_to_delete[[i]]
+    
+    if (!DRY_RUN) {
+      tryCatch({
+        file.remove(file_path)
+        deleted_count <- deleted_count + 1
+      }, error = function(e) {
+        error_count <<- error_count + 1
+      })
+    }
+  }
+  
+  # ===========================================================================
+  # FINAL SUMMARY
+  # ===========================================================================
+  
+  cat("\n")
+  if (DRY_RUN) {
+    cat(sprintf("[DRY RUN] Would have deleted %d files\n", length(files_to_delete)))
+  } else {
+    cat(sprintf("Deleted: %d files\n", deleted_count))
+    if (error_count > 0) {
+      cat(sprintf("Errors: %d files\n", error_count))
+    }
+  }
+  cat(strrep("=", 70), "\n\n")
+  
+  quit(status = 0)
+}
+
+# ==============================================================================
+# EXECUTE
+# ==============================================================================
+
+if (sys.nframe() == 0) {
+  main()
+}

r_app/FIX_INDENTATION.R

@@ -0,0 +1,20 @@
+# Fix indentation for lines 408-1022 in 80_calculate_kpis.R
+# These lines should be inside the else-if block at the CANE_SUPPLY_WORKFLOW level
+
+file_path <- "r_app/80_calculate_kpis.R"
+lines <- readLines(file_path)
+
+# Lines 408-1021 (0-indexed: 407-1020) need 2 more spaces of indentation
+for (i in 408:1021) {
+  if (i <= length(lines)) {
+    line <- lines[i]
+    # Skip empty or whitespace-only lines
+    if (nchar(trimws(line)) > 0) {
+      # Add 2 spaces
+      lines[i] <- paste0("  ", line)
+    }
+  }
+}
+
+writeLines(lines, file_path)
+cat("Fixed indentation for lines 408-1022\n")

r_app/MANUAL_PIPELINE_RUNNER.R

@@ -73,6 +73,7 @@
 #   python 00_download_8band_pu_optimized.py [PROJECT] --date [DATE] --resolution 3 --cleanup
 #
 # Example:
+#   cd python_app
 #   python 00_download_8band_pu_optimized.py angata --date 2026-02-04 --resolution 3 --cleanup
 #
 # COMMAND #2 - Batch Download (Multiple Dates):
@@ -125,8 +126,6 @@
 #
 #   & "C:\Program Files\R\R-4.4.3\bin\x64\Rscript.exe" r_app/10_create_per_field_tiffs.R angata
 #
-# Example:
-#   & "C:\Program Files\R\R-4.4.3\bin\x64\Rscript.exe" r_app/10_create_per_field_tiffs.R angata
 #
 # COMMAND #2 - Specific Date Range:
 #

r_app/extract_rds_only.R

@@ -1,104 +0,0 @@
-# EXTRACT_RDS_ONLY.R
-# ===================
-# Extract and combine daily CI values into combined_CI_data.rds
-# Skips raster processing - assumes daily extracted files already exist
-#
-# Usage: Rscript r_app/extract_rds_only.R [project_dir]
-#   - project_dir: Project directory name (e.g., "angata", "aura", "chemba")
-#
-# Example:
-#   Rscript r_app/extract_rds_only.R angata
-
-suppressPackageStartupMessages({
-  library(tidyverse)
-  library(here)
-})
-
-main <- function() {
-  # Capture command line arguments
-  args <- commandArgs(trailingOnly = TRUE)
-  
-  # Process project_dir argument
-  if (length(args) >= 1 && !is.na(args[1])) {
-    project_dir <- as.character(args[1])
-  } else {
-    project_dir <- "angata"
-  }
-  
-  cat(sprintf("RDS Extraction: project=%s\n", project_dir))
-  
-  # Source configuration
-  tryCatch({
-    source("parameters_project.R")
-  }, error = function(e) {
-    warning("Default source files not found. Attempting to source from 'r_app' directory.")
-    tryCatch({
-      source("r_app/parameters_project.R")
-      warning(paste("Successfully sourced files from 'r_app' directory."))
-    }, error = function(e) {
-      stop("Failed to source parameters_project.R from both default and 'r_app' directories.")
-    })
-  })
-  
-  # Define paths for CI data
-  daily_CI_vals_dir <- file.path(
-    "laravel_app/storage/app", project_dir,
-    "Data/extracted_ci/daily_vals"
-  )
-  
-  cumulative_CI_vals_dir <- file.path(
-    "laravel_app/storage/app", project_dir,
-    "Data/extracted_ci/cumulative_vals"
-  )
-  
-  cat(sprintf("Daily CI values dir: %s\n", daily_CI_vals_dir))
-  cat(sprintf("Cumulative CI values dir: %s\n\n", cumulative_CI_vals_dir))
-  
-  # Check if daily CI directory exists and has files
-  if (!dir.exists(daily_CI_vals_dir)) {
-    stop(sprintf("ERROR: Daily CI directory not found: %s", daily_CI_vals_dir))
-  }
-  
-  # List RDS files
-  files <- list.files(path = daily_CI_vals_dir, pattern = "^extracted_.*\\.rds$", full.names = TRUE)
-  
-  if (length(files) == 0) {
-    stop(sprintf("ERROR: No extracted CI values found in %s", daily_CI_vals_dir))
-  }
-  
-  cat(sprintf("Found %d daily CI RDS files\n\n", length(files)))
-  
-  # Create cumulative directory if it doesn't exist
-  if (!dir.exists(cumulative_CI_vals_dir)) {
-    dir.create(cumulative_CI_vals_dir, recursive = TRUE)
-    cat(sprintf("Created directory: %s\n\n", cumulative_CI_vals_dir))
-  }
-  
-  # Combine all RDS files
-  cat("Combining daily RDS files...\n")
-  combined_data <- files %>%
-    purrr::map(readRDS) %>% 
-    purrr::list_rbind() %>%
-    dplyr::group_by(sub_field)
-  
-  # Save combined data
-  output_path <- file.path(cumulative_CI_vals_dir, "combined_CI_data.rds")
-  saveRDS(combined_data, output_path)
-  
-  cat(sprintf("✓ Combined %d daily files\n", length(files)))
-  cat(sprintf("✓ Total rows: %d\n", nrow(combined_data))
-  cat(sprintf("✓ Saved to: %s\n\n", output_path))
-  
-  # Summary
-  cat("Summary:\n")
-  cat(sprintf("  Fields: %d\n", n_distinct(combined_data$field, na.rm = TRUE)))
-  cat(sprintf("  Sub-fields: %d\n", n_distinct(combined_data$sub_field, na.rm = TRUE)))
-  cat(sprintf("  Total measurements: %d\n\n", nrow(combined_data)))
-  
-  cat("✓ RDS extraction complete!\n")
-  cat("Next: Run 02b_convert_rds_to_csv.R to convert to CSV\n")
-}
-
-if (sys.nframe() == 0) {
-  main()
-}

r_app/old_scripts/kpi_utils.R

@@ -9,11 +9,6 @@
 # 5. Weed Presence Score
 # 6. Gap Filling Score
 
-# Note: This file depends on functions from crop_messaging_utils.R:
-#   - safe_log()
-#   - calculate_cv()
-#   - calculate_spatial_autocorrelation()
-#   - calculate_change_percentages()
 
 # 1. Helper Functions
 # -----------------
@@ -676,7 +671,7 @@ calculate_growth_decline_kpi <- function(current_ci, previous_ci, field_boundari
       mean_change <- mean(ci_change)
       
       # Calculate spatial metrics
-      spatial_result <- calculate_spatial_autocorrelation(current_ci, field_vect)
+      spatial_result <- calculate_spatial_autocorrelation(current_field_ci, field_vect)
       cv_value <- calculate_cv(current_clean)
       
       # Determine risk level based on CI decline and spatial distribution

r_app/report_utils.R

@@ -244,7 +244,8 @@ ci_plot <- function(pivotName,
     
     # Create spans for borders
     joined_spans2 <- field_boundaries %>% 
-                     sf::st_transform(sf::st_crs(pivotShape)) %>%                     dplyr::filter(field %in% pivotName)
+                     sf::st_transform(sf::st_crs(pivotShape)) %>% 
+                     dplyr::filter(field %in% pivotName)
     
     # Create the maps for different timepoints
     CImap_m2 <- create_CI_map(singlePivot_m2, AllPivots2, joined_spans2,