# Analyze timing between CI threshold crossings and actual harvest dates
# Goal: Determine how soon after CI drops below threshold the harvest actually occurs
suppressPackageStartupMessages({
  library(readxl)
  library(dplyr)
  library(tidyr)
  library(lubridate)
  library(here)
  library(ggplot2)
})

# Set project directory
project_dir <- "esa"
assign("project_dir", project_dir, envir = .GlobalEnv)
source(here("r_app", "parameters_project.R"))

# Read daily CI data
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, ci = FitData) %>%
  arrange(field_id, date)

# Read actual harvest data
harvest_actual <- 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))

cat("=== ANALYZING CI THRESHOLD CROSSING TIMING ===\n\n")

# For each actual harvest, find when CI first dropped below various thresholds
thresholds <- c(3.0, 2.5, 2.0, 1.8)

results <- list()

for (i in 1:nrow(harvest_actual)) {
  harvest <- harvest_actual[i, ]
  field <- harvest$field
  harvest_date <- harvest$season_end
  
  # Get CI data for this field in the year before harvest
  field_data <- time_series_daily %>%
    filter(field_id == field,
           date >= (harvest_date - 365),
           date <= harvest_date) %>%
    arrange(date)
  
  if (nrow(field_data) == 0) next
  
  # For each threshold, find LAST crossing date (working backward from harvest)
  # This finds the mature→harvest transition, not the previous cycle's harvest
  threshold_crossings <- sapply(thresholds, function(threshold) {
    # Find LAST period where CI was high (>3.5), then dropped below threshold
    # Work backward from harvest date
    last_mature_idx <- NA
    for (j in nrow(field_data):1) {
      if (!is.na(field_data$ci[j]) && field_data$ci[j] > 3.5) {
        last_mature_idx <- j
        break
      }
    }
    
    # If no mature period found, skip
    if (is.na(last_mature_idx)) return(NA)
    
    # Now find first crossing below threshold AFTER the mature period
    for (j in last_mature_idx:(nrow(field_data) - 2)) {
      if (!is.na(field_data$ci[j]) && !is.na(field_data$ci[j+1]) && !is.na(field_data$ci[j+2]) &&
          field_data$ci[j] < threshold &&
          field_data$ci[j+1] < threshold &&
          field_data$ci[j+2] < threshold) {
        return(as.character(field_data$date[j]))
      }
    }
    return(NA)
  })
  
  result_row <- data.frame(
    field = field,
    harvest_date = harvest_date,
    ci_at_harvest = field_data$ci[nrow(field_data)]
  )
  
  for (k in 1:length(thresholds)) {
    threshold <- thresholds[k]
    crossing_date <- as.Date(threshold_crossings[k])
    
    if (!is.na(crossing_date)) {
      days_before_harvest <- as.numeric(harvest_date - crossing_date)
      result_row[[paste0("first_below_", threshold)]] <- as.character(crossing_date)
      result_row[[paste0("days_before_", threshold)]] <- days_before_harvest
    } else {
      result_row[[paste0("first_below_", threshold)]] <- NA
      result_row[[paste0("days_before_", threshold)]] <- NA
    }
  }
  
  results[[i]] <- result_row
}

timing_analysis <- bind_rows(results)

# Print summary statistics
cat("\n=== TIMING STATISTICS: Days from threshold crossing to actual harvest ===\n\n")

for (threshold in thresholds) {
  days_col <- paste0("days_before_", threshold)
  days_before <- timing_analysis[[days_col]]
  days_before <- days_before[!is.na(days_before)]
  
  if (length(days_before) > 0) {
    cat(sprintf("CI < %.1f threshold:\n", threshold))
    cat(sprintf("  Valid cases: %d/%d (%.1f%%)\n", 
                length(days_before), nrow(timing_analysis),
                100 * length(days_before) / nrow(timing_analysis)))
    cat(sprintf("  Mean:   %.1f days before harvest\n", mean(days_before)))
    cat(sprintf("  Median: %.1f days before harvest\n", median(days_before)))
    cat(sprintf("  Range:  %.1f to %.1f days\n", min(days_before), max(days_before)))
    cat(sprintf("  Q1-Q3:  %.1f to %.1f days\n", quantile(days_before, 0.25), quantile(days_before, 0.75)))
    
    # Count how many harvests occur within specific time windows after crossing
    within_7d <- sum(days_before >= 0 & days_before <= 7)
    within_14d <- sum(days_before >= 0 & days_before <= 14)
    within_21d <- sum(days_before >= 0 & days_before <= 21)
    within_30d <- sum(days_before >= 0 & days_before <= 30)
    
    cat(sprintf("  Harvest timing after crossing:\n"))
    cat(sprintf("    0-7 days:   %d (%.1f%%)\n", within_7d, 100*within_7d/length(days_before)))
    cat(sprintf("    0-14 days:  %d (%.1f%%)\n", within_14d, 100*within_14d/length(days_before)))
    cat(sprintf("    0-21 days:  %d (%.1f%%)\n", within_21d, 100*within_21d/length(days_before)))
    cat(sprintf("    0-30 days:  %d (%.1f%%)\n", within_30d, 100*within_30d/length(days_before)))
    cat("\n")
  } else {
    cat(sprintf("CI < %.1f threshold: No valid crossings found\n\n", threshold))
  }
}

# Show detailed table for fields with mismatches
cat("\n=== DETAILED TIMING BY FIELD ===\n")

# Get column names dynamically
days_cols <- grep("days_before_", names(timing_analysis), value = TRUE)
select_cols <- c("field", "harvest_date", "ci_at_harvest", days_cols[1:min(2, length(days_cols))])

print(timing_analysis %>%
        select(all_of(select_cols)) %>%
        arrange(field, harvest_date), n = 100)

# Create visualization
cat("\n=== Creating timing distribution plot ===\n")
timing_long <- timing_analysis %>%
  select(field, harvest_date, starts_with("days_before_")) %>%
  pivot_longer(cols = starts_with("days_before_"),
               names_to = "threshold",
               values_to = "days_before") %>%
  filter(!is.na(days_before)) %>%
  mutate(threshold = gsub("days_before_", "CI < ", threshold))

png("timing_threshold_to_harvest.png", width = 1200, height = 800, res = 120)
ggplot(timing_long, aes(x = days_before, fill = threshold)) +
  geom_histogram(binwidth = 7, alpha = 0.7, position = "identity") +
  facet_wrap(~threshold, ncol = 1) +
  geom_vline(xintercept = c(7, 14, 21), linetype = "dashed", color = "red", alpha = 0.5) +
  labs(
    title = "Time from CI Threshold Crossing to Actual Harvest",
    subtitle = "How many days AFTER CI drops below threshold does harvest actually occur?",
    x = "Days from threshold crossing to harvest",
    y = "Count of harvest events",
    caption = "Dashed lines at 7, 14, 21 days"
  ) +
  theme_minimal() +
  theme(legend.position = "none")
dev.off()

cat("\nPlot saved to: timing_threshold_to_harvest.png\n")