# EXECUTIVE REPORT UTILITIES
# This file contains functions for creating advanced visualizations for the executive summary report

#' Create a velocity and acceleration indicator for CI change
#'
#' @param health_data Current farm health data
#' @param ci_current Current CI raster
#' @param ci_prev1 CI raster from 1 week ago
#' @param ci_prev2 CI raster from 2 weeks ago
#' @param ci_prev3 CI raster from 3 weeks ago
#' @param field_boundaries Field boundaries spatial data (sf object)
#' @return A ggplot2 object with velocity and acceleration gauges
#'
create_velocity_acceleration_indicator <- function(health_data, ci_current, ci_prev1, ci_prev2, ci_prev3, field_boundaries) {
  tryCatch({
    # Calculate farm-wide metrics for multiple weeks
    mean_ci_current <- mean(health_data$mean_ci, na.rm = TRUE)
    
    # Calculate previous week metrics
    # Extract CI values for previous weeks
    field_ci_metrics <- data.frame(field = character(), 
                                 week_current = numeric(),
                                 week_minus_1 = numeric(),
                                 week_minus_2 = numeric(),
                                 week_minus_3 = numeric(),
                                 stringsAsFactors = FALSE)
    
    # Process each field
    fields <- unique(field_boundaries$field)
    for (field_name in fields) {
      tryCatch({
        # Get field boundary
        field_shape <- field_boundaries %>% dplyr::filter(field == field_name)
        if (nrow(field_shape) == 0) next
        
        # Extract CI values for all weeks
        ci_curr_values <- terra::extract(ci_current, field_shape)
        ci_prev1_values <- terra::extract(ci_prev1, field_shape)
        ci_prev2_values <- terra::extract(ci_prev2, field_shape)
        ci_prev3_values <- terra::extract(ci_prev3, field_shape)
        
        # Calculate mean CI for each week
        mean_ci_curr <- mean(ci_curr_values$CI, na.rm = TRUE)
        mean_ci_prev1 <- mean(ci_prev1_values$CI, na.rm = TRUE)
        mean_ci_prev2 <- mean(ci_prev2_values$CI, na.rm = TRUE)
        mean_ci_prev3 <- mean(ci_prev3_values$CI, na.rm = TRUE)
        
        # Add to metrics table
        field_ci_metrics <- rbind(field_ci_metrics, data.frame(
          field = field_name,
          week_current = mean_ci_curr,
          week_minus_1 = mean_ci_prev1,
          week_minus_2 = mean_ci_prev2,
          week_minus_3 = mean_ci_prev3,
          stringsAsFactors = FALSE
        ))
      }, error = function(e) {
        message(paste("Error processing field", field_name, "for velocity indicator:", e$message))
      })
    }
    
    # Calculate farm-wide averages
    farm_avg <- colMeans(field_ci_metrics[, c("week_current", "week_minus_1", "week_minus_2", "week_minus_3")], na.rm = TRUE)
    
    # Calculate velocity (rate of change) - current week compared to last week
    velocity <- farm_avg["week_current"] - farm_avg["week_minus_1"]
    
    # Calculate previous velocity (last week compared to two weeks ago)
    prev_velocity <- farm_avg["week_minus_1"] - farm_avg["week_minus_2"]
    
    # Calculate acceleration (change in velocity)
    acceleration <- velocity - prev_velocity
    
    # Prepare data for velocity gauge
    velocity_data <- data.frame(
      label = "Weekly CI Change",
      value = velocity
    )
    
    # Prepare data for acceleration gauge
    acceleration_data <- data.frame(
      label = "Change Acceleration",
      value = acceleration
    )
    
    # Create velocity trend data
    trend_data <- data.frame(
      week = c(-3, -2, -1, 0),
      ci_value = c(farm_avg["week_minus_3"], farm_avg["week_minus_2"], 
                  farm_avg["week_minus_1"], farm_avg["week_current"])
    )
    
    # Create layout grid for the visualizations
    layout_matrix <- matrix(c(1, 1, 2, 2, 3, 3), nrow = 2, byrow = TRUE)
    
    # Create velocity gauge
    velocity_gauge <- ggplot2::ggplot(velocity_data, ggplot2::aes(x = 0, y = 0)) +
      ggplot2::geom_arc_bar(ggplot2::aes(
        x0 = 0, y0 = 0, 
        r0 = 0.5, r = 1,
        start = -pi/2, end = pi/2,
        fill = "background"
      ), fill = "#f0f0f0") +
      ggplot2::geom_arc_bar(ggplot2::aes(
        x0 = 0, y0 = 0, 
        r0 = 0.5, r = 1,
        start = -pi/2,
        end = -pi/2 + (pi * (0.5 + (velocity / 2))), # Scale to range -1 to +1
        fill = "velocity"
      ), fill = ifelse(velocity >= 0, "#1a9850", "#d73027")) +
      ggplot2::geom_text(ggplot2::aes(label = sprintf("%.2f", velocity)), 
                       size = 8, fontface = "bold") +
      ggplot2::geom_text(ggplot2::aes(label = "Velocity"), y = -0.3, size = 4) +
      ggplot2::coord_fixed() +
      ggplot2::theme_void() +
      ggplot2::scale_fill_manual(values = c("background" = "#f0f0f0", "velocity" = "steelblue"),
                               guide = "none") +
      ggplot2::annotate("text", x = -0.85, y = 0, label = "Declining", 
                      angle = 90, size = 3.5) +
      ggplot2::annotate("text", x = 0.85, y = 0, label = "Improving", 
                      angle = -90, size = 3.5) +
      ggplot2::labs(title = "Farm Health Velocity", 
                  subtitle = "CI change per week") +
      ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5, size = 14, face = "bold"),
                   plot.subtitle = ggplot2::element_text(hjust = 0.5, size = 12))

    # Create acceleration gauge
    acceleration_gauge <- ggplot2::ggplot(acceleration_data, ggplot2::aes(x = 0, y = 0)) +
      ggplot2::geom_arc_bar(ggplot2::aes(
        x0 = 0, y0 = 0, 
        r0 = 0.5, r = 1,
        start = -pi/2, end = pi/2,
        fill = "background"
      ), fill = "#f0f0f0") +
      ggplot2::geom_arc_bar(ggplot2::aes(
        x0 = 0, y0 = 0, 
        r0 = 0.5, r = 1,
        start = -pi/2,
        end = -pi/2 + (pi * (0.5 + (acceleration / 1))), # Scale to range -0.5 to +0.5
        fill = "acceleration"
      ), fill = ifelse(acceleration >= 0, "#1a9850", "#d73027")) +
      ggplot2::geom_text(ggplot2::aes(label = sprintf("%.2f", acceleration)), 
                       size = 8, fontface = "bold") +
      ggplot2::geom_text(ggplot2::aes(label = "Acceleration"), y = -0.3, size = 4) +
      ggplot2::coord_fixed() +
      ggplot2::theme_void() +
      ggplot2::scale_fill_manual(values = c("background" = "#f0f0f0", "acceleration" = "steelblue"),
                               guide = "none") +
      ggplot2::annotate("text", x = -0.85, y = 0, label = "Slowing", 
                      angle = 90, size = 3.5) +
      ggplot2::annotate("text", x = 0.85, y = 0, label = "Accelerating", 
                      angle = -90, size = 3.5) +
      ggplot2::labs(title = "Change Acceleration", 
                  subtitle = "Increasing or decreasing trend") +
      ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5, size = 14, face = "bold"),
                   plot.subtitle = ggplot2::element_text(hjust = 0.5, size = 12))
    
    # Create trend chart
    trend_chart <- ggplot2::ggplot(trend_data, ggplot2::aes(x = week, y = ci_value)) +
      ggplot2::geom_line(size = 1.5, color = "steelblue") +
      ggplot2::geom_point(size = 3, color = "steelblue") +
      ggplot2::geom_hline(yintercept = trend_data$ci_value[1], linetype = "dashed", color = "gray50") +
      ggplot2::labs(
        title = "4-Week CI Trend",
        x = "Weeks from current",
        y = "Average CI Value"
      ) +
      ggplot2::theme_minimal() +
      ggplot2::scale_x_continuous(breaks = c(-3, -2, -1, 0))

    # Create table of top velocity changes
    field_ci_metrics$velocity <- field_ci_metrics$week_current - field_ci_metrics$week_minus_1
    top_velocity_fields <- field_ci_metrics %>%
      dplyr::arrange(desc(abs(velocity))) %>%
      dplyr::slice_head(n = 5) %>%
      dplyr::select(field, velocity) %>%
      dplyr::mutate(direction = ifelse(velocity >= 0, "Improving", "Declining"))

    # Combine into multi-panel figure
    main_plot <- gridExtra::grid.arrange(
      gridExtra::grid.arrange(velocity_gauge, acceleration_gauge, ncol = 2),
      trend_chart,
      heights = c(1.5, 1),
      nrow = 2
    )
    
    return(main_plot)
    
  }, error = function(e) {
    message(paste("Error in create_velocity_acceleration_indicator:", e$message))
    return(ggplot2::ggplot() + 
             ggplot2::annotate("text", x = 0, y = 0, label = paste("Error creating velocity indicator:", e$message)) +
             ggplot2::theme_void())
  })
}

#' Generate a field health score based on CI values and trends
#'
#' @param ci_current Current CI raster
#' @param ci_change CI change raster
#' @param field_age_weeks Field age in weeks
#' @return List containing score, status, and component scores
#'
generate_field_health_score <- function(ci_current, ci_change, field_age_weeks) {
  # Get mean CI value for the field
  mean_ci <- terra::global(ci_current, "mean", na.rm=TRUE)[[1]]
  
  # Get mean CI change
  mean_change <- terra::global(ci_change, "mean", na.rm=TRUE)[[1]]
  
  # Get CI uniformity (coefficient of variation)
  ci_sd <- terra::global(ci_current, "sd", na.rm=TRUE)[[1]]
  ci_uniformity <- ifelse(mean_ci > 0, ci_sd / mean_ci, 1)
  
  # Calculate base score from current CI (scale 0-5)
  # Adjusted for crop age - expectations increase with age
  expected_ci <- min(5, field_age_weeks / 10) # Simple linear model
  ci_score <- max(0, min(5, 5 - 2 * abs(mean_ci - expected_ci)))
  
  # Add points for positive change (scale 0-3)
  change_score <- max(0, min(3, 1 + mean_change))
  
  # Add points for uniformity (scale 0-2)
  uniformity_score <- max(0, min(2, 2 * (1 - ci_uniformity)))
  
  # Calculate total score (0-10)
  total_score <- ci_score + change_score + uniformity_score
  
  # Create status label
  status <- dplyr::case_when(
    total_score >= 8 ~ "Excellent",
    total_score >= 6 ~ "Good",
    total_score >= 4 ~ "Fair",
    total_score >= 2 ~ "Needs Attention",
    TRUE ~ "Critical"
  )
  
  # Return results
  return(list(
    score = round(total_score, 1),
    status = status,
    components = list(
      ci = round(ci_score, 1),
      change = round(change_score, 1),
      uniformity = round(uniformity_score, 1)
    )
  ))
}

#' Create an irrigation recommendation map
#'
#' @param ci_current Current CI raster
#' @param ci_change CI change raster
#' @param field_shape Field boundary shape
#' @param title Map title
#' @return A tmap object with irrigation recommendations
#'
create_irrigation_map <- function(ci_current, ci_change, field_shape, title = "Irrigation Priority Zones") {
  # Create a new raster for irrigation recommendations
  irrigation_priority <- ci_current * 0
  
  # Extract values for processing
  ci_values <- terra::values(ci_current)
  change_values <- terra::values(ci_change)
  
  # Create priority zones:
  # 3 = High priority (low CI, negative trend)
  # 2 = Medium priority (low CI but stable, or good CI with negative trend)
  # 1 = Low priority (watch, good CI with slight decline)
  # 0 = No action needed (good CI, stable/positive trend)
  priority_values <- rep(NA, length(ci_values))
  
  # High priority: Low CI (< 2) and negative change (< 0)
  high_priority <- which(ci_values < 2 & change_values < 0 & !is.na(ci_values) & !is.na(change_values))
  priority_values[high_priority] <- 3
  
  # Medium priority: Low CI (< 2) with stable/positive change, or moderate CI (2-4) with significant negative change (< -1)
  medium_priority <- which(
    (ci_values < 2 & change_values >= 0 & !is.na(ci_values) & !is.na(change_values)) |
    (ci_values >= 2 & ci_values < 4 & change_values < -1 & !is.na(ci_values) & !is.na(change_values))
  )
  priority_values[medium_priority] <- 2
  
  # Low priority (watch): Moderate/good CI (>= 2) with mild negative change (-1 to 0)
  low_priority <- which(
    ci_values >= 2 & change_values < 0 & change_values >= -1 & !is.na(ci_values) & !is.na(change_values)
  )
  priority_values[low_priority] <- 1
  
  # No action needed: Good CI (>= 2) with stable/positive change (>= 0)
  no_action <- which(ci_values >= 2 & change_values >= 0 & !is.na(ci_values) & !is.na(change_values))
  priority_values[no_action] <- 0
  
  # Set values in the irrigation priority raster
  terra::values(irrigation_priority) <- priority_values
  
  # Create the map
  tm_shape(irrigation_priority) +
    tm_raster(
      style = "cat",
      palette = c("#1a9850", "#91cf60", "#fc8d59", "#d73027"),
      labels = c("No Action", "Watch", "Medium Priority", "High Priority"),
      title = "Irrigation Need"
    ) +
    tm_shape(field_shape) +
    tm_borders(lwd = 2) +
    tm_layout(
      main.title = title,
      legend.outside = FALSE,
      legend.position = c("left", "bottom")
    )
}

#' Simple mock function to get weather data for a field
#' In a real implementation, this would fetch data from a weather API
#'
#' @param start_date Start date for weather data
#' @param end_date End date for weather data
#' @param lat Latitude of the field center
#' @param lon Longitude of the field center
#' @return A data frame of weather data
#'
get_weather_data <- function(start_date, end_date, lat = -16.1, lon = 34.7) {
  # This is a mock implementation - in production, you'd replace with actual API call
  # to a service like OpenWeatherMap, NOAA, or other weather data provider
  
  # Create date sequence
  dates <- seq.Date(from = as.Date(start_date), to = as.Date(end_date), by = "day")
  n_days <- length(dates)
  
  # Generate some random but realistic weather data with seasonal patterns
  # More rain in summer, less in winter (Southern hemisphere)
  month_nums <- as.numeric(format(dates, "%m"))
  
  # Simplified seasonal patterns - adjust for your local climate
  is_rainy_season <- month_nums %in% c(11, 12, 1, 2, 3, 4)
  
  # Generate rainfall - more in rainy season, occasional heavy rainfall
  rainfall <- numeric(n_days)
  rainfall[is_rainy_season] <- pmax(0, rnorm(sum(is_rainy_season), mean = 4, sd = 8))
  rainfall[!is_rainy_season] <- pmax(0, rnorm(sum(!is_rainy_season), mean = 0.5, sd = 2))
  
  # Add some rare heavy rainfall events
  heavy_rain_days <- sample(which(is_rainy_season), size = max(1, round(sum(is_rainy_season) * 0.1)))
  rainfall[heavy_rain_days] <- rainfall[heavy_rain_days] + runif(length(heavy_rain_days), 20, 50)
  
  # Generate temperatures - seasonal variation
  temp_mean <- 18 + 8 * sin((month_nums - 1) * pi/6)  # Peak in January (month 1)
  temp_max <- temp_mean + rnorm(n_days, mean = 5, sd = 1)
  temp_min <- temp_mean - rnorm(n_days, mean = 5, sd = 1)
  
  # Create weather data frame
  weather_data <- data.frame(
    date = dates,
    rainfall_mm = round(rainfall, 1),
    temp_max_c = round(temp_max, 1),
    temp_min_c = round(temp_min, 1),
    temp_mean_c = round((temp_max + temp_min) / 2, 1)
  )
  
  return(weather_data)
}

#' Creates a weather summary visualization integrated with CI data
#'
#' @param pivotName Name of the pivot field
#' @param ci_data CI quadrant data
#' @param days_to_show Number of days of weather to show
#' @return ggplot object
#'
create_weather_ci_plot <- function(pivotName, ci_data = CI_quadrant, days_to_show = 30) {
  # Get field data
  field_data <- ci_data %>% 
    dplyr::filter(field == pivotName) %>%
    dplyr::arrange(Date) %>%
    dplyr::filter(!is.na(value))
  
  if (nrow(field_data) == 0) {
    return(ggplot() + 
             annotate("text", x = 0, y = 0, label = "No data available") + 
             theme_void())
  }
  
  # Get the latest date and 30 days before
  latest_date <- max(field_data$Date, na.rm = TRUE)
  start_date <- latest_date - days_to_show
  
  # Filter for recent data only
  recent_field_data <- field_data %>% 
    dplyr::filter(Date >= start_date)
  
  # Get center point coordinates for the field (would be calculated from geometry in production)
  # This is mocked for simplicity
  lat <- -16.1  # Mock latitude
  lon <- 34.7   # Mock longitude
  
  # Get weather data
  weather_data <- get_weather_data(start_date, latest_date, lat, lon)
  
  # Aggregate CI data to daily mean across subfields if needed
  daily_ci <- recent_field_data %>%
    dplyr::group_by(Date) %>%
    dplyr::summarize(mean_ci = mean(value, na.rm = TRUE))
  
  # Create combined plot with dual y-axis
  g <- ggplot() +
    # Rainfall as bars
    geom_col(data = weather_data, aes(x = date, y = rainfall_mm), 
             fill = "#1565C0", alpha = 0.7, width = 0.7) +
    # CI as a line
    geom_line(data = daily_ci, aes(x = Date, y = mean_ci * 10), 
              color = "#2E7D32", size = 1) +
    geom_point(data = daily_ci, aes(x = Date, y = mean_ci * 10), 
               color = "#2E7D32", size = 2) +
    # Temperature range as ribbon
    geom_ribbon(data = weather_data, 
                aes(x = date, ymin = temp_min_c, ymax = temp_max_c),
                fill = "#FF9800", alpha = 0.2) +
    # Primary y-axis (rainfall)
    scale_y_continuous(
      name = "Rainfall (mm)",
      sec.axis = sec_axis(~./10, name = "Chlorophyll Index & Temperature (°C)")
    ) +
    labs(
      title = paste("Field", pivotName, "- Weather and CI Relationship"),
      subtitle = paste("Last", days_to_show, "days"),
      x = "Date"
    ) +
    theme_minimal() +
    theme(
      axis.title.y.left = element_text(color = "#1565C0"),
      axis.title.y.right = element_text(color = "#2E7D32"),
      legend.position = "bottom"
    )
  
  return(g)
}