# ============================================================================
# OPERATIONAL HARVEST ALERT SYSTEM
# Two-stage detection optimized for daily factory operations
# ============================================================================
# STAGE 1: Advance Warning (2-3 weeks ahead)
#   - 7-day rolling avg CI < 2.5 for 5+ consecutive days
#   - Alerts factory to monitor field closely
#   - Escalates over time: WATCH → PREPARE → IMMINENT
#
# STAGE 2: Harvest Confirmation (day after harvest)
#   - Sharp drop (≥1.0) within 3-7 days AND CI stays below 2.0
#   - Confirms harvest occurred
#   - Prioritizes Stage 1 alerted fields
# ============================================================================

suppressPackageStartupMessages({
  library(readxl)
  library(dplyr)
  library(tidyr)
  library(lubridate)
  library(here)
  library(zoo)  # For rolling averages
})

# Set project directory
project_dir <- "esa"
assign("project_dir", project_dir, envir = .GlobalEnv)

if (basename(getwd()) == "harvest_prediction") {
  setwd("../../..")
}

source(here("r_app", "parameters_project.R"))

# ============================================================================
# CONFIGURATION
# ============================================================================

CONFIG <- list(
  # STAGE 1: Advance warning thresholds
  rolling_window_days = 7,           # Rolling average window
  ci_threshold_rolling = 2.5,        # 7-day avg below this
  sustained_days = 5,                # Consecutive days below threshold
  min_field_age_days = 240,          # 8 months minimum
  
  # Alert escalation timing (days since first Stage 1 alert)
  watch_days = 0,        # 0-7 days: WATCH
  prepare_days = 7,      # 7-14 days: PREPARE  
  imminent_days = 14,    # 14+ days: IMMINENT
  
  # STAGE 2: Harvest confirmation thresholds
  sharp_drop_threshold = 1.0,        # CI drop within window
  sharp_drop_window = 7,             # Days to measure drop
  post_harvest_ci = 2.0,             # CI stays below this after harvest
  confirmation_days = 2,             # Days to confirm stable low CI
  
  # Validation settings
  test_window_days = 21
)

cat("============================================================================\n")
cat("OPERATIONAL HARVEST ALERT SYSTEM\n")
cat("Optimized for daily factory operations\n")
cat("============================================================================\n\n")

cat("STAGE 1 - ADVANCE WARNING:\n")
cat("  - 7-day rolling avg CI <", CONFIG$ci_threshold_rolling, "for", CONFIG$sustained_days, "consecutive days\n")
cat("  - Provides 2-3 weeks advance notice\n")
cat("  - Escalates: WATCH → PREPARE → IMMINENT\n\n")

cat("STAGE 2 - HARVEST CONFIRMATION:\n")
cat("  - Sharp drop (≥", CONFIG$sharp_drop_threshold, ") within", CONFIG$sharp_drop_window, "days\n")
cat("  - AND CI stays below", CONFIG$post_harvest_ci, "for", CONFIG$confirmation_days, "days\n")
cat("  - Detects day after harvest (better confidence)\n\n")

# ============================================================================
# LOAD DATA
# ============================================================================

cat("=== LOADING DATA ===\n\n")

ci_rds_file <- here("laravel_app/storage/app", project_dir, "Data/extracted_ci/cumulative_vals/All_pivots_Cumulative_CI_quadrant_year_v2.rds")
ci_data_raw <- readRDS(ci_rds_file) %>% ungroup()

time_series_daily <- ci_data_raw %>%
  mutate(date = as.Date(Date)) %>%
  select(field_id = field, date, mean_ci = FitData) %>%
  filter(!is.na(mean_ci), !is.na(date), !is.na(field_id)) %>%
  arrange(field_id, date)

harvest_data <- read_excel('laravel_app/storage/app/esa/Data/harvest.xlsx') %>%
  mutate(
    season_start = as.Date(season_start),
    season_end = as.Date(season_end)
  ) %>%
  filter(!is.na(season_end))

fields_with_ci <- unique(time_series_daily$field_id)
harvest_data_filtered <- harvest_data %>%
  filter(field %in% fields_with_ci) %>%
  arrange(field, season_end)

cat("Fields:", length(fields_with_ci), "\n")
cat("Harvest events:", nrow(harvest_data_filtered), "\n\n")

# ============================================================================
# CALCULATE ROLLING AVERAGES
# ============================================================================

cat("=== CALCULATING 7-DAY ROLLING AVERAGES ===\n\n")

time_series_with_rolling <- time_series_daily %>%
  group_by(field_id) %>%
  arrange(date) %>%
  mutate(
    ci_rolling_7d = rollapply(mean_ci, width = CONFIG$rolling_window_days, 
                               FUN = mean, align = "right", fill = NA, na.rm = TRUE)
  ) %>%
  ungroup()

cat("Rolling averages calculated\n\n")

# ============================================================================
# STAGE 1: ADVANCE WARNING DETECTION
# ============================================================================

detect_stage1_alert <- function(field_ts, check_date, last_harvest_date, 
                                first_alert_date = NULL, config = CONFIG) {
  # Check field age
  if (is.null(last_harvest_date) || is.na(last_harvest_date)) {
    earliest_date <- min(field_ts$date, na.rm = TRUE)
    field_age <- as.numeric(check_date - earliest_date)
  } else {
    field_age <- as.numeric(check_date - last_harvest_date)
  }
  
  if (field_age < config$min_field_age_days) {
    return(list(
      stage1_alert = FALSE,
      stage1_level = "too_young",
      consecutive_days = 0,
      rolling_ci = NA,
      first_alert_date = NA
    ))
  }
  
  # Get rolling average on check date
  current_rolling <- field_ts %>%
    filter(date == check_date) %>%
    pull(ci_rolling_7d)
  
  if (length(current_rolling) == 0 || is.na(current_rolling[1])) {
    return(list(
      stage1_alert = FALSE,
      stage1_level = "no_data",
      consecutive_days = 0,
      rolling_ci = NA,
      first_alert_date = NA
    ))
  }
  
  current_rolling <- current_rolling[1]
  
  # Count consecutive days with rolling avg below threshold
  recent_data <- field_ts %>%
    filter(date <= check_date, date >= check_date - 30) %>%
    arrange(desc(date))
  
  consecutive_days <- 0
  for (i in 1:nrow(recent_data)) {
    if (!is.na(recent_data$ci_rolling_7d[i]) && 
        recent_data$ci_rolling_7d[i] <= config$ci_threshold_rolling) {
      consecutive_days <- consecutive_days + 1
    } else {
      break
    }
  }
  
  # Determine alert status and level
  stage1_alert <- FALSE
  stage1_level <- "none"
  new_first_alert_date <- first_alert_date
  
  if (consecutive_days >= config$sustained_days) {
    stage1_alert <- TRUE
    
    # Track when alert first triggered
    if (is.null(first_alert_date) || is.na(first_alert_date)) {
      new_first_alert_date <- check_date
    }
    
    # Escalate alert level based on days since first alert
    if (!is.null(new_first_alert_date) && !is.na(new_first_alert_date)) {
      days_since_first_alert <- as.numeric(check_date - new_first_alert_date)
      
      if (days_since_first_alert >= config$imminent_days) {
        stage1_level <- "IMMINENT"  # 14+ days: harvest very soon
      } else if (days_since_first_alert >= config$prepare_days) {
        stage1_level <- "PREPARE"   # 7-14 days: get ready
      } else {
        stage1_level <- "WATCH"     # 0-7 days: monitor closely
      }
    } else {
      stage1_level <- "WATCH"
    }
  }
  
  return(list(
    stage1_alert = stage1_alert,
    stage1_level = stage1_level,
    consecutive_days = consecutive_days,
    rolling_ci = current_rolling,
    first_alert_date = new_first_alert_date
  ))
}

# ============================================================================
# STAGE 2: HARVEST CONFIRMATION DETECTION
# ============================================================================

detect_stage2_alert <- function(field_ts, check_date, config = CONFIG) {
  # Get current CI
  current_ci <- field_ts %>%
    filter(date == check_date) %>%
    pull(mean_ci)
  
  if (length(current_ci) == 0 || is.na(current_ci[1])) {
    return(list(
      stage2_alert = FALSE,
      stage2_level = "no_data",
      ci_drop = NA,
      current_ci = NA
    ))
  }
  
  current_ci <- current_ci[1]
  
  # Get CI from 7 days ago
  baseline_ci <- field_ts %>%
    filter(date >= check_date - config$sharp_drop_window - 3,
           date <= check_date - config$sharp_drop_window + 3) %>%
    summarise(mean_ci = mean(mean_ci, na.rm = TRUE)) %>%
    pull(mean_ci)
  
  if (length(baseline_ci) == 0 || is.na(baseline_ci)) {
    return(list(
      stage2_alert = FALSE,
      stage2_level = "no_baseline",
      ci_drop = NA,
      current_ci = current_ci
    ))
  }
  
  # Calculate drop
  ci_drop <- baseline_ci - current_ci
  
  # Check for sharp drop AND sustained low CI
  stage2_alert <- FALSE
  stage2_level <- "none"
  
  if (ci_drop >= config$sharp_drop_threshold && 
      current_ci <= config$post_harvest_ci) {
    
    # Confirm CI stays low for multiple days
    recent_low_days <- field_ts %>%
      filter(date <= check_date, date >= check_date - config$confirmation_days) %>%
      filter(mean_ci <= config$post_harvest_ci) %>%
      nrow()
    
    if (recent_low_days >= config$confirmation_days) {
      stage2_alert <- TRUE
      stage2_level <- "CONFIRMED"
    } else {
      stage2_alert <- TRUE
      stage2_level <- "POSSIBLE"
    }
  }
  
  return(list(
    stage2_alert = stage2_alert,
    stage2_level = stage2_level,
    ci_drop = ci_drop,
    current_ci = current_ci,
    baseline_ci = baseline_ci
  ))
}

# ============================================================================
# VALIDATION FUNCTION
# ============================================================================

validate_operational_system <- function(field_id) {
  field_ts <- time_series_with_rolling %>%
    filter(field_id == !!field_id) %>%
    arrange(date)
  
  field_harvests <- harvest_data_filtered %>%
    filter(field == field_id) %>%
    arrange(season_end)
  
  if (nrow(field_harvests) == 0) return(NULL)
  
  all_results <- data.frame()
  
  for (h in 1:nrow(field_harvests)) {
    harvest_date <- field_harvests$season_end[h]
    last_harvest <- if (h == 1) NA else field_harvests$season_end[h - 1]
    
    test_dates_seq <- seq.Date(
      from = harvest_date - CONFIG$test_window_days,
      to = harvest_date + 14,
      by = "1 day"
    )
    
    first_alert_date_tracked <- NA
    
    for (i in 1:length(test_dates_seq)) {
      test_date <- test_dates_seq[i]
      days_from_harvest <- as.numeric(test_date - harvest_date)
      
      # Stage 1 with alert escalation
      stage1 <- detect_stage1_alert(field_ts, test_date, last_harvest, 
                                     first_alert_date_tracked, CONFIG)
      
      # Update tracked first alert date
      if (stage1$stage1_alert && !is.na(stage1$first_alert_date)) {
        first_alert_date_tracked <- stage1$first_alert_date
      }
      
      # Stage 2
      stage2 <- detect_stage2_alert(field_ts, test_date, CONFIG)
      
      if (length(stage1$rolling_ci) > 0 && !is.na(stage1$rolling_ci)) {
        all_results <- bind_rows(all_results, data.frame(
          field = field_id,
          harvest_event = h,
          harvest_date = harvest_date,
          test_date = test_date,
          days_from_harvest = days_from_harvest,
          stage1_alert = stage1$stage1_alert,
          stage1_level = stage1$stage1_level,
          stage2_alert = stage2$stage2_alert,
          stage2_level = stage2$stage2_level,
          rolling_ci = stage1$rolling_ci,
          consecutive_days = stage1$consecutive_days,
          ci_drop = ifelse(is.null(stage2$ci_drop), NA, stage2$ci_drop)
        ))
      }
    }
  }
  
  return(all_results)
}

# ============================================================================
# RUN FULL VALIDATION
# ============================================================================

cat("============================================================================\n")
cat("VALIDATING ON FULL DATASET\n")
cat("============================================================================\n\n")

all_results <- data.frame()
summary_stats <- data.frame()

fields_to_test <- unique(harvest_data_filtered$field)
total_fields <- length(fields_to_test)

cat("Testing", total_fields, "fields...\n\n")
pb <- txtProgressBar(min = 0, max = total_fields, style = 3)

for (f in 1:total_fields) {
  field_id <- fields_to_test[f]
  
  field_results <- validate_operational_system(field_id)
  
  if (!is.null(field_results) && nrow(field_results) > 0) {
    all_results <- bind_rows(all_results, field_results)
    
    # Calculate success rates
    field_harvests_count <- length(unique(field_results$harvest_event))
    
    # Stage 1: Any alert in 7-21 days before harvest
    stage1_success <- field_results %>%
      filter(stage1_alert == TRUE, 
             days_from_harvest >= -21, 
             days_from_harvest <= -7) %>%
      distinct(harvest_event) %>%
      nrow()
    
    # Stage 2: Detection within 1-3 days after harvest
    stage2_success <- field_results %>%
      filter(stage2_alert == TRUE, 
             stage2_level == "CONFIRMED",
             days_from_harvest >= 0, 
             days_from_harvest <= 3) %>%
      distinct(harvest_event) %>%
      nrow()
    
    summary_stats <- bind_rows(summary_stats, data.frame(
      field = field_id,
      total_harvests = field_harvests_count,
      stage1_success = stage1_success,
      stage2_success = stage2_success,
      stage1_rate = round(100 * stage1_success / field_harvests_count, 1),
      stage2_rate = round(100 * stage2_success / field_harvests_count, 1)
    ))
  }
  
  setTxtProgressBar(pb, f)
}

close(pb)

# ============================================================================
# RESULTS
# ============================================================================

cat("\n\n============================================================================\n")
cat("RESULTS BY FIELD\n")
cat("============================================================================\n\n")

print(summary_stats, row.names = FALSE)

cat("\n============================================================================\n")
cat("OVERALL PERFORMANCE\n")
cat("============================================================================\n\n")

total_harvests <- sum(summary_stats$total_harvests)
total_stage1 <- sum(summary_stats$stage1_success)
total_stage2 <- sum(summary_stats$stage2_success)

cat("Total harvest events:", total_harvests, "\n\n")

cat("STAGE 1 - ADVANCE WARNING (7-21 days ahead):\n")
cat("  Success:", total_stage1, "/", total_harvests, 
    "(", round(100 * total_stage1 / total_harvests, 1), "% )\n")
cat("  Fields with >50% success:", sum(summary_stats$stage1_rate > 50), "/", total_fields, "\n\n")

cat("STAGE 2 - HARVEST CONFIRMATION (0-3 days after):\n")
cat("  Success:", total_stage2, "/", total_harvests,
    "(", round(100 * total_stage2 / total_harvests, 1), "% )\n")
cat("  Fields with >50% success:", sum(summary_stats$stage2_rate > 50), "/", total_fields, "\n\n")

# Alert escalation analysis
if (nrow(all_results) > 0) {
  cat("STAGE 1 ALERT ESCALATION BREAKDOWN:\n")
  escalation_breakdown <- all_results %>%
    filter(stage1_alert == TRUE, days_from_harvest < 0) %>%
    group_by(stage1_level) %>%
    summarise(count = n()) %>%
    arrange(match(stage1_level, c("WATCH", "PREPARE", "IMMINENT")))
  
  print(escalation_breakdown, row.names = FALSE)
  cat("\n")
}

cat("============================================================================\n")
cat("TOP PERFORMING FIELDS\n")
cat("============================================================================\n\n")

cat("STAGE 1 (Advance Warning):\n")
top_stage1 <- summary_stats %>% arrange(desc(stage1_rate)) %>% head(5)
print(top_stage1, row.names = FALSE)

cat("\n\nSTAGE 2 (Harvest Confirmation):\n")
top_stage2 <- summary_stats %>% arrange(desc(stage2_rate)) %>% head(5)
print(top_stage2, row.names = FALSE)

cat("\n============================================================================\n")
cat("OPERATIONAL IMPLEMENTATION\n")
cat("============================================================================\n\n")

cat("🏭 DAILY WORKFLOW:\n\n")
cat("  1. Run this script each morning\n")
cat("  2. Review ALL ACTIVE ALERTS (status report for all fields)\n\n")

cat("  STAGE 1 ESCALATION:\n")
cat("    - WATCH: Field entered harvest window, monitor closely\n")
cat("    - PREPARE: 1 week in alert, prepare logistics (7-14 days total)\n")
cat("    - IMMINENT: 2+ weeks in alert, harvest very soon (14+ days total)\n\n")

cat("  STAGE 2 CONFIRMATION:\n")
cat("    - POSSIBLE: Sharp CI drop detected, likely harvested\n")
cat("    - CONFIRMED: Sustained low CI for 2+ days, harvest confirmed\n\n")

cat("  Priority: Stage 1 alerted fields get Stage 2 monitoring\n")
cat("  Detection: Day after harvest (better satellite coverage = higher confidence)\n\n")

# Save results
output_file <- here("r_app/experiments/harvest_prediction/operational_validation_results.rds")
saveRDS(list(
  all_results = all_results,
  summary = summary_stats,
  config = CONFIG
), output_file)

cat("============================================================================\n")
cat("Results saved to:", output_file, "\n")
cat("============================================================================\n")