# 80_UTILS_CANE_SUPPLY.R
# ============================================================================
# CANE SUPPLY CLIENT-SPECIFIC UTILITIES (SCRIPT 80 - CLIENT TYPE: cane_supply)
#
# Contains ANGATA and other cane supply-specific KPI and reporting functions.
#
# Currently, CANE_SUPPLY clients use the common utilities from 80_utils_common.R:
#   - Weekly statistics (calculate_field_statistics, calculate_kpi_trends)
#   - Field analysis reporting (generate_field_analysis_summary)
#   - Excel export (export_field_analysis_excel)
#
# This file is structured to accommodate future ANGATA-specific functionality such as:
#   - Custom yield models
#   - Harvest readiness criteria
#   - Supply chain integration hooks
#   - ANGATA-specific alerting and messaging
#
# Orchestrator: (Placeholder - uses common functions)
# Dependencies: 00_common_utils.R, 80_utils_common.R
# Used by: 80_calculate_kpis.R (when client_type == "cane_supply")
# ============================================================================

library(terra)
library(sf)
library(dplyr)
library(tidyr)
library(readxl)
library(writexl)

# ============================================================================
# ANGATA-SPECIFIC HELPER FUNCTIONS (Placeholder Section)
# ============================================================================

#' Placeholder: ANGATA harvest readiness assessment
#'
#' Future implementation will integrate ANGATA-specific harvest readiness criteria:
#'   - Maturation phase detection (CI threshold-based)
#'   - Field age tracking (days since planting)
#'   - Weather-based ripeness indicators (if available)
#'   - Historical yield correlations
#'
#' @param field_ci CI values for the field
#' @param field_age_days Days since planting
#'
#' @return Character string with harvest readiness assessment
assess_harvest_readiness <- function(field_ci, field_age_days = NULL) {
  # Placeholder implementation
  # Real version would check:
  # - Mean CI > 3.5 (maturation threshold)
  # - Age > 350 days
  # - Weekly growth rate < threshold (mature plateau)
  
  if (is.null(field_ci) || all(is.na(field_ci))) {
    return("No data available")
  }
  
  mean_ci <- mean(field_ci, na.rm = TRUE)
  
  if (mean_ci > 3.5) {
    return("Ready for harvest")
  } else if (mean_ci > 2.5) {
    return("Approaching harvest readiness")
  } else {
    return("Not ready - continue monitoring")
  }
}

#' Placeholder: ANGATA supply chain status flags
#'
#' Future implementation will add supply chain-specific status indicators:
#'   - Harvest scheduling readiness
#'   - Equipment availability impact
#'   - Transportation/logistics flags
#'   - Quality parameter flags
#'
#' @param field_analysis Data frame with field analysis results
#'
#' @return Data frame with supply chain status columns
assess_supply_chain_status <- function(field_analysis) {
  # Placeholder: return field analysis as-is
  # Real version would add columns for:
  # - schedule_ready (bool)
  # - harvest_window_days (numeric)
  # - transportation_priority (char)
  # - quality_flags (char)
  
  return(field_analysis)
}

# ============================================================================
# ORCHESTRATOR FOR CANE_SUPPLY WORKFLOWS
# ============================================================================

#' Orchestrate ANGATA weekly field analysis and reporting
#'
#' Main entry point for CANE_SUPPLY (ANGATA, etc.) workflows.
#' Currently uses common utilities; future versions will add client-specific logic.
#'
#' @param field_boundaries_sf SF object with field geometries
#' @param current_week ISO week number (1-53)
#' @param current_year ISO week year
#' @param mosaic_dir Directory containing weekly mosaics
#' @param field_boundaries_path Path to field GeoJSON
#' @param harvesting_data Data frame with harvest data (optional)
#' @param output_dir Directory for exports
#' @param data_dir Base data directory
#'
#' @return List with field analysis results
#'
#' @details
#' This function:
#' 1. Loads weekly mosaic and extracts field statistics
#' 2. Calculates field statistics (using common utilities)
#' 3. Prepares field analysis summary
#' 4. Exports to Excel/CSV/RDS
#' 5. (Future) Applies ANGATA-specific assessments
#'
calculate_field_analysis_cane_supply <- function(
    field_boundaries_sf,
    current_week,
    current_year,
    mosaic_dir,
    field_boundaries_path = NULL,
    harvesting_data = NULL,
    output_dir = file.path(PROJECT_DIR, "output"),
    data_dir = NULL
) {
  
  message("\n============ CANE SUPPLY FIELD ANALYSIS (ANGATA, etc.) ============")
  
  # Load current week mosaic
  message("Loading current week mosaic...")
  current_mosaic <- load_weekly_ci_mosaic(mosaic_dir, current_week, current_year)
  
  if (is.null(current_mosaic)) {
    warning(paste("Could not load current week mosaic for week", current_week, current_year))
    return(NULL)
  }
  
  # Extract field statistics
  message("Extracting field statistics from current mosaic...")
  field_stats <- extract_field_statistics_from_ci(current_mosaic, field_boundaries_sf)
  
  # Load previous week stats for comparison
  message("Loading historical data for trends...")
  target_prev <- calculate_target_week_and_year(current_week, current_year, offset_weeks = 1)
  previous_stats <- NULL
  
  previous_mosaic <- load_weekly_ci_mosaic(mosaic_dir, target_prev$week, target_prev$year)
  if (!is.null(previous_mosaic)) {
    previous_stats <- extract_field_statistics_from_ci(previous_mosaic, field_boundaries_sf)
  }
  
  # Calculate 4-week historical trend
  message("Calculating field trends...")
  ci_rds_path <- file.path(data_dir, "combined_CI", "combined_CI_data.rds")
  
  field_analysis <- calculate_field_statistics(
    field_stats = field_stats,
    previous_stats = previous_stats,
    week_num = current_week,
    year = current_year,
    ci_rds_path = ci_rds_path,
    field_boundaries_sf = field_boundaries_sf,
    harvesting_data = harvesting_data
  )
  
  if (is.null(field_analysis)) {
    message("Could not generate field analysis")
    return(NULL)
  }
  
  # Generate summary
  message("Generating summary statistics...")
  summary_df <- generate_field_analysis_summary(field_analysis)
  
  # Export
  message("Exporting field analysis...")
  export_paths <- export_field_analysis_excel(
    field_analysis,
    summary_df,
    PROJECT_DIR,
    current_week,
    current_year,
    output_dir
  )
  
  message(paste("\n✓ CANE_SUPPLY field analysis complete. Week", current_week, current_year, "\n"))
  
  result <- list(
    field_analysis = field_analysis,
    summary = summary_df,
    exports = export_paths
  )
  
  return(result)
}

# ============================================================================
# FUTURE EXTENSION POINTS
# ============================================================================

# Placeholder for ANGATA-specific utilities that may be added in future:
# - Custom yield models based on ANGATA historical data
# - Field condition thresholds specific to ANGATA growing practices
# - Integration with ANGATA harvest scheduling system
# - WhatsApp messaging templates for ANGATA supply chain stakeholders
# - Cost/benefit analysis for ANGATA operational decisions

# These functions can be added here as ANGATA requirements evolve.