# 06_CROP_MESSAGING.R
# ===================
# This script analyzes weekly CI mosaics to detect changes and generate automated messages
# about crop conditions. It compares two weeks of data to assess:
# - Field uniformity (high vs low variation)
# - CI change trends (increase, stable, decrease)
# - Generates contextual messages based on analysis
# - Outputs results in multiple formats: WhatsApp/Word text, CSV, and .docx
#
# Usage: Rscript 06_crop_messaging.R [current_week] [previous_week] [estate_name]
#   - current_week: Current week number (e.g., 30)
#   - previous_week: Previous week number (e.g., 29)
#   - estate_name: Estate name (e.g., "simba", "chemba")
#
# Examples:
#   Rscript 06_crop_messaging.R 32 31 simba
#   Rscript 06_crop_messaging.R 30 29 chemba
#
# The script automatically:
#   1. Loads the correct estate configuration
#   2. Analyzes weekly mosaics
#   3. Generates field-by-field analysis
#   4. Creates output files in multiple formats
#   5. Displays WhatsApp-ready text in console
#

# 1. Load required packages
# -----------------------
suppressPackageStartupMessages({
  library(sf)
  library(terra)
  library(tidyverse)
  library(lubridate)
  library(here)
  library(spdep)  # For spatial statistics
})

# 2. Main function to handle messaging workflow
# ---------------------------------------------
main <- function() {
  # Capture command line arguments
  args <- commandArgs(trailingOnly = TRUE)

  # Process arguments with defaults
  current_week <- if (length(args) >= 1 && !is.na(args[1])) {
    as.numeric(args[1])
  } else {
    39  # Default for proof of concept
  }

  previous_week <- if (length(args) >= 2 && !is.na(args[2])) {
    as.numeric(args[2])
  } else {
    38  # Default for proof of concept
  }

  estate_name <- if (length(args) >= 3 && !is.na(args[3])) {
    as.character(args[3])
  } else {
    "aura"  # Default estate
  }

  year <- 2025  # Current year - could be made dynamic

  # Make estate_name available globally so parameters_project.R can use it
  assign("project_dir", estate_name, envir = .GlobalEnv)

  # Initialize project configuration and load utility functions
  tryCatch({
    source("parameters_project.R")
    source("crop_messaging_utils.R")
  }, error = function(e) {
    warning("Default source files not found. Attempting to source from 'r_app' directory.")
    tryCatch({
      source(here::here("r_app", "parameters_project.R"))
      source(here::here("r_app", "crop_messaging_utils.R"))
      warning(paste("Successfully sourced files from 'r_app' directory."))

    }, error = function(e) {
      stop("Failed to source required files from both default and 'r_app' directories.")
    })
  })

  log_message("Starting crop messaging analysis")

  # Run the modular analysis
  analysis_results <- run_estate_analysis(estate_name, current_week, previous_week, year)
  field_results <- analysis_results$field_results

  # Display detailed field-by-field analysis
  cat("=== FIELD-BY-FIELD ANALYSIS ===\n\n")

  for (field_id in names(field_results)) {
    field_info <- field_results[[field_id]]
    current_field <- field_info$current_stats
    previous_field <- field_info$previous_stats
    ci_change <- field_info$ci_change
    change_category <- field_info$change_category
    change_percentages <- field_info$change_percentages
    uniformity_category <- field_info$uniformity_category
    message_result <- field_info$message_result

    # Print enhanced field analysis
    cat("FIELD:", current_field$field, "-", current_field$sub_field, "\n")
    cat("- Field size:", round(current_field$field_area_ha, 1), "hectares\n")
    cat("- Week", previous_week, "CI:", round(previous_field$mean_ci, 3), "\n")
    cat("- Week", current_week, "CI:", round(current_field$mean_ci, 3), "\n")
    cat("- Terra stats: Mean =", round(current_field$mean_ci, 3),
        ", CV =", round(current_field$cv, 3),
        ", Range = [", round(current_field$min_ci, 2), "-", round(current_field$max_ci, 2), "]\n")

    cat("- Within acceptable range (±25% of mean):", round(current_field$acceptable_pct, 1), "%\n")

    # Display primary uniformity metrics (CV and Entropy)
    cat("- Field uniformity: CV =", round(current_field$cv, 3))
    if (current_field$cv < 0.08) {
      cat(" (excellent)")
    } else if (current_field$cv < 0.15) {
      cat(" (good)")
    } else if (current_field$cv < 0.30) {
      cat(" (moderate)")
    } else if (current_field$cv < 0.50) {
      cat(" (high variation)")
    } else {
      cat(" (very high variation)")
    }

    # Add entropy information
    if (!is.na(current_field$entropy)) {
      cat(", Entropy =", round(current_field$entropy, 3))
      # Entropy interpretation (higher = more heterogeneous)
      # Adjusted thresholds to better match CV patterns
      if (current_field$entropy < 1.3) {
        cat(" (very uniform)")
      } else if (current_field$entropy < 1.5) {
        cat(" (uniform)")
      } else if (current_field$entropy < 1.7) {
        cat(" (moderate heterogeneity)")
      } else {
        cat(" (high heterogeneity)")
      }
    }
    cat("\n")

    cat("- Change: Mean =", round(ci_change, 3), "(", change_category, ")")
    if (!is.na(change_percentages$positive_pct)) {
      # Calculate hectares for this field using field area from geojson
      field_hectares <- current_field$field_area_ha
      improving_hectares <- (change_percentages$positive_pct / 100) * field_hectares
      declining_hectares <- (change_percentages$negative_pct / 100) * field_hectares

      cat(", Areas: ", round(change_percentages$positive_pct, 1), "% (", round(improving_hectares, 1), " ha) improving, ",
          round(change_percentages$negative_pct, 1), "% (", round(declining_hectares, 1), " ha) declining\n")
    } else {
      cat("\n")
    }
    cat("- Spatial Pattern:", uniformity_category, "\n")

    # Add spatial details if available
    if (!is.na(current_field$spatial_autocorr$morans_i)) {
      cat("- Moran's I:", round(current_field$spatial_autocorr$morans_i, 3),
          "(", current_field$spatial_autocorr$interpretation, ")")

      # Add agricultural context explanation for Moran's I
      moran_val <- current_field$spatial_autocorr$morans_i
      if (moran_val >= 0.7 && moran_val < 0.85) {
        cat(" - normal field continuity")
      } else if (moran_val >= 0.85 && moran_val < 0.95) {
        cat(" - strong spatial pattern")
      } else if (moran_val >= 0.95) {
        cat(" - very strong clustering, monitor for management issues")
      } else if (moran_val < 0.7 && moran_val > 0.3) {
        cat(" - moderate spatial pattern")
      } else {
        cat(" - unusual spatial pattern for crop field")
      }
      cat("\n")
    }

    if (!is.na(current_field$extreme_percentages$hotspot_pct)) {
      cat("- Extreme areas: ", round(current_field$extreme_percentages$hotspot_pct, 1),
          "% hotspots (high-performing), ", round(current_field$extreme_percentages$coldspot_pct, 1),
          "% coldspots (underperforming)")

      # Show method used for extreme detection
      if (!is.null(current_field$extreme_percentages$method)) {
        if (current_field$extreme_percentages$method == "getis_ord_gi_star") {
          cat(" [Getis-Ord Gi*]")
        } else if (current_field$extreme_percentages$method == "simple_sd") {
          cat(" [Simple SD]")
        }
      }
      cat("\n")
    }

    cat("- Message:", message_result$message, "\n")
    cat("- Alert needed:", if(message_result$worth_sending) "YES 🚨" else "NO", "\n\n")
  }

  # Summary of alerts
  alert_fields <- sapply(field_results, function(x) x$message_result$worth_sending)
  total_alerts <- sum(alert_fields)

  cat("=== SUMMARY ===\n")
  cat("Total fields analyzed:", length(field_results), "\n")
  cat("Fields requiring alerts:", total_alerts, "\n")

  if (total_alerts > 0) {
    cat("\nFields needing attention:\n")
    for (field_id in names(field_results)[alert_fields]) {
      field_info <- field_results[[field_id]]
      cat("-", field_info$current_stats$field, "-", field_info$current_stats$sub_field,
          ":", field_info$message_result$message, "\n")
    }
  }

  # Farm-wide analysis summary table
  cat("\n=== FARM-WIDE ANALYSIS SUMMARY ===\n")

  # Field uniformity statistics with detailed categories
  excellent_fields <- sapply(field_results, function(x) x$current_stats$cv <= 0.08)
  good_fields <- sapply(field_results, function(x) x$current_stats$cv > 0.08 & x$current_stats$cv <= 0.15)
  moderate_fields <- sapply(field_results, function(x) x$current_stats$cv > 0.15 & x$current_stats$cv <= 0.30)
  poor_fields <- sapply(field_results, function(x) x$current_stats$cv > 0.30)

  n_excellent <- sum(excellent_fields)
  n_good <- sum(good_fields)
  n_moderate <- sum(moderate_fields)
  n_poor <- sum(poor_fields)
  n_uniform_total <- n_excellent + n_good  # Total uniform fields (CV ≤ 0.20)

  # Calculate farm-wide area statistics
  total_hectares <- sum(sapply(field_results, function(x) x$current_stats$field_area_ha), na.rm = TRUE)
  total_improving_hectares <- sum(sapply(field_results, function(x) {
    if (!is.na(x$change_percentages$positive_pct)) {
      (x$change_percentages$positive_pct / 100) * x$current_stats$field_area_ha
    } else 0
  }), na.rm = TRUE)

  total_declining_hectares <- sum(sapply(field_results, function(x) {
    if (!is.na(x$change_percentages$negative_pct)) {
      (x$change_percentages$negative_pct / 100) * x$current_stats$field_area_ha
    } else 0
  }), na.rm = TRUE)

  # Calculate farm-wide percentages
  farm_improving_pct <- (total_improving_hectares / total_hectares) * 100
  farm_declining_pct <- (total_declining_hectares / total_hectares) * 100

  # Display summary table
  cat("\nFIELD UNIFORMITY SUMMARY:\n")
  cat("│ Uniformity Level    │ Count │ Percent │\n")
  cat(sprintf("│ Excellent (CV≤0.08) │ %5d │ %6.1f%% │\n", n_excellent, (n_excellent/length(field_results))*100))
  cat(sprintf("│ Good (CV 0.08-0.15) │ %5d │ %6.1f%% │\n", n_good, (n_good/length(field_results))*100))
  cat(sprintf("│ Moderate (CV 0.15-0.30) │ %5d │ %6.1f%% │\n", n_moderate, (n_moderate/length(field_results))*100))
  cat(sprintf("│ Poor (CV>0.30)      │ %5d │ %6.1f%% │\n", n_poor, (n_poor/length(field_results))*100))
  cat(sprintf("│ Total fields        │ %5d │ %6.1f%% │\n", length(field_results), 100.0))

  cat("\nFARM-WIDE AREA CHANGE SUMMARY:\n")
  cat("│ Change Type         │ Hectares│ Percent │\n")
  cat(sprintf("│ Improving areas     │ %7.1f │ %6.1f%% │\n", total_improving_hectares, farm_improving_pct))
  cat(sprintf("│ Declining areas     │ %7.1f │ %6.1f%% │\n", total_declining_hectares, farm_declining_pct))
  cat(sprintf("│ Total area          │ %7.1f │ %6.1f%% │\n", total_hectares, 100.0))

  # Additional insights
  cat("\nKEY INSIGHTS:\n")
  cat(sprintf("• %d%% of fields have good uniformity (CV ≤ 0.15)\n", round((n_uniform_total/length(field_results))*100)))
  cat(sprintf("• %d%% of fields have excellent uniformity (CV ≤ 0.08)\n", round((n_excellent/length(field_results))*100)))
  cat(sprintf("• %.1f hectares (%.1f%%) of farm area is improving week-over-week\n", total_improving_hectares, farm_improving_pct))
  cat(sprintf("• %.1f hectares (%.1f%%) of farm area is declining week-over-week\n", total_declining_hectares, farm_declining_pct))
  cat(sprintf("• Total farm area analyzed: %.1f hectares\n", total_hectares))
  if (farm_improving_pct > farm_declining_pct) {
    cat(sprintf("• Overall trend: POSITIVE (%.1f%% more area improving than declining)\n", farm_improving_pct - farm_declining_pct))
  } else if (farm_declining_pct > farm_improving_pct) {
    cat(sprintf("• Overall trend: NEGATIVE (%.1f%% more area declining than improving)\n", farm_declining_pct - farm_improving_pct))
  } else {
    cat("• Overall trend: BALANCED (equal improvement and decline)\n")
  }

  # Generate and save multiple output formats
  saved_files <- save_analysis_outputs(analysis_results)

  # Analysis complete
  cat("\n=== ANALYSIS COMPLETE ===\n")
  cat("All field analysis results, farm-wide summary, and output files created.\n")

  # Return results for potential further processing
  invisible(analysis_results)
}

if (sys.nframe() == 0) {
  main()
}