# REPORT_UTILS.R
# =============
# Utility functions for generating SmartCane reports with visualizations.
# These functions support the creation of maps, charts and report elements
# for the CI_report_dashboard_planet.Rmd document.

#' Safe logging function that works whether log_message exists or not
#'
#' @param message The message to log
#' @param level The log level (default: "INFO")
#' @return NULL (used for side effects)
#'
safe_log <- function(message, level = "INFO") {
  if (exists("log_message")) {
    log_message(message, level)
  } else {
    if (level %in% c("ERROR", "WARNING")) {
      warning(message)
    } else {
      message(message)
    }
  }
}

#' Creates a sub-chunk for use within RMarkdown documents
#'
#' @param g A ggplot object to render in the sub-chunk
#' @param fig_height Height of the figure in inches
#' @param fig_width Width of the figure in inches
#' @return NULL (writes chunk directly to output)
#'
subchunkify <- function(g, fig_height=7, fig_width=5) {
  g_deparsed <- paste0(deparse(
    function() {g}
  ), collapse = '')
  
  sub_chunk <- paste0("
`","``{r sub_chunk_", floor(runif(1) * 10000), ", fig.height=", fig_height, ", fig.width=", fig_width, ", dpi=300, dev='png', out.width='100%', echo=FALSE}",
                      "\n(",
                      g_deparsed
                      , ")()",
                      "\n`","``
  ")
  
  cat(knitr::knit(text = knitr::knit_expand(text = sub_chunk), quiet = TRUE))
} 

#' Creates a Chlorophyll Index map for a pivot
#'
#' @param pivot_raster The raster data containing CI values
#' @param pivot_shape The shape of the pivot field
#' @param pivot_spans Additional boundary data for the field
#' @param show_legend Whether to show the legend (default: FALSE)
#' @param legend_is_portrait Whether to show the legend in portrait orientation (default: FALSE)
#' @param week Week number to display in the title
#' @param age Age of the crop in weeks
#' @param borders Whether to display field borders (default: FALSE)
#' @return A tmap object with the CI map
#' 
create_CI_map <- function(pivot_raster, pivot_shape, pivot_spans, show_legend = F, legend_is_portrait = F, week, age, borders = FALSE, colorblind = FALSE){
  # Input validation
  if (missing(pivot_raster) || is.null(pivot_raster)) {
    stop("pivot_raster is required")
  }
  if (missing(pivot_shape) || is.null(pivot_shape)) {
    stop("pivot_shape is required")
  }
  if (missing(pivot_spans) || is.null(pivot_spans)) {
    stop("pivot_spans is required")
  }
  if (missing(week) || is.null(week)) {
    stop("week parameter is required")
  }
  if (missing(age) || is.null(age)) {
    stop("age parameter is required")
  }
  
  # Choose palette based on colorblind flag
  palette <- if (colorblind) "viridis" else "brewer.rd_yl_gn"
  
  # Create the base map
  map <- tm_shape(pivot_raster, unit = "m")
  
  # Add raster with continuous spectrum (fixed scale 8-1 for consistent comparison, reversed)
  map <- map + tm_raster(col.scale = tm_scale_continuous(values = palette,
                                                       limits = c(1,8)),
                          col.legend = tm_legend(title = "CI",
                                               orientation = if(legend_is_portrait) "portrait" else "landscape", 
                                               show = show_legend,
                                               position = if(show_legend) tm_pos_out("left", "center") else c("left", "bottom"),
                                               reverse = TRUE
                                               ))
    # Add layout elements
  map <- map + tm_layout(main.title = paste0("Max CI week ", week,"\n",  age, " weeks (", age * 7, " days) old"),
                          main.title.size = 0.7)
  
  # Add borders if requested
  if (borders) {
    map <- map +
      tm_shape(pivot_shape) +
      tm_borders(lwd = 3) +
      tm_text("sub_field", size = 1/2) +
      tm_shape(pivot_spans) +
      tm_borders(lwd = 0.5, alpha = 0.5)
  }
  
  return(map)
}

#' Creates a Chlorophyll Index difference map between two weeks
#'
#' @param pivot_raster The raster data containing CI difference values
#' @param pivot_shape The shape of the pivot field
#' @param pivot_spans Additional boundary data for the field
#' @param show_legend Whether to show the legend (default: FALSE)
#' @param legend_is_portrait Whether to show the legend in portrait orientation (default: FALSE)
#' @param week_1 First week number for comparison
#' @param week_2 Second week number for comparison
#' @param age Age of the crop in weeks
#' @param borders Whether to display field borders (default: TRUE)
#' @return A tmap object with the CI difference map
#'
create_CI_diff_map <- function(pivot_raster, pivot_shape, pivot_spans, show_legend = F, legend_is_portrait = F, week_1, week_2, age, borders = TRUE, colorblind = FALSE){
  # Input validation
  if (missing(pivot_raster) || is.null(pivot_raster)) {
    stop("pivot_raster is required")
  }
  if (missing(pivot_shape) || is.null(pivot_shape)) {
    stop("pivot_shape is required") 
  }
  if (missing(pivot_spans) || is.null(pivot_spans)) {
    stop("pivot_spans is required")
  }
  if (missing(week_1) || is.null(week_1) || missing(week_2) || is.null(week_2)) {
    stop("week_1 and week_2 parameters are required")
  }
  if (missing(age) || is.null(age)) {
    stop("age parameter is required")
  }
  
  # Choose palette based on colorblind flag
  palette <- if (colorblind) "plasma" else "brewer.rd_yl_gn"
  
  # Create the base map
  map <- tm_shape(pivot_raster, unit = "m")
  
  # Add raster with continuous spectrum (centered at 0 for difference maps, fixed scale, reversed)
  map <- map + tm_raster(col.scale = tm_scale_continuous(values = palette,
                                                       midpoint = 0,
                                                       limits = c(-3, 3)),
                          col.legend = tm_legend(title = "CI diff.",
                                               orientation = if(legend_is_portrait) "portrait" else "landscape",
                                               show = show_legend,
                                               position = if(show_legend) tm_pos_out("right", "center") else c("left", "bottom"),
                                               reverse = TRUE
                                               ))
    # Add layout elements
  map <- map + tm_layout(main.title = paste0("CI change week ", week_1, " - week ", week_2, "\n", age, " weeks (", age * 7, " days) old"),
                          main.title.size = 0.7)
  
  # Add borders if requested
  if (borders) {
    map <- map +
      tm_shape(pivot_shape) +
      tm_borders(lwd = 3) +
      tm_text("sub_field", size = 1/2) +
      tm_shape(pivot_spans) +
      tm_borders(lwd = 0.5, alpha = 0.5)
  }
  
  return(map)
}

#' Creates a visualization of CI data for a specific pivot field
#'
#' @param pivotName The name or ID of the pivot field to visualize
#' @param field_boundaries Field boundaries spatial data (sf object)
#' @param current_ci Current week's Chlorophyll Index raster 
#' @param ci_minus_1 Previous week's Chlorophyll Index raster
#' @param ci_minus_2 Two weeks ago Chlorophyll Index raster
#' @param last_week_diff Difference raster between current and last week
#' @param three_week_diff Difference raster between current and three weeks ago
#' @param harvesting_data Data frame containing field harvesting/planting information
#' @param week Current week number
#' @param week_minus_1 Previous week number
#' @param week_minus_2 Two weeks ago week number
#' @param week_minus_3 Three weeks ago week number
#' @param borders Whether to display field borders (default: TRUE)
#' @param colorblind_friendly Whether to use colorblind-friendly color schemes (default: FALSE)
#' @return NULL (adds output directly to R Markdown document)
#'
ci_plot <- function(pivotName, 
                    field_boundaries = AllPivots0, 
                    current_ci = CI, 
                    ci_minus_1 = CI_m1, 
                    ci_minus_2 = CI_m2, 
                    last_week_diff = last_week_dif_raster_abs, 
                    three_week_diff = three_week_dif_raster_abs, 
                    harvesting_data = harvesting_data,
                    week = week,
                    week_minus_1 = week_minus_1,
                    week_minus_2 = week_minus_2,
                    week_minus_3 = week_minus_3,
                    borders = TRUE,
                    colorblind_friendly = FALSE){
  # Input validation
  if (missing(pivotName) || is.null(pivotName) || pivotName == "") {
    stop("pivotName is required")
  }
  if (missing(field_boundaries) || is.null(field_boundaries)) {
    stop("field_boundaries is required")
  }
  if (missing(current_ci) || is.null(current_ci)) {
    stop("current_ci is required")
  }
  if (missing(ci_minus_1) || is.null(ci_minus_1)) {
    stop("ci_minus_1 is required")
  }
  if (missing(ci_minus_2) || is.null(ci_minus_2)) {
    stop("ci_minus_2 is required")
  }
  if (missing(last_week_diff) || is.null(last_week_diff)) {
    stop("last_week_diff is required")
  }
  if (missing(three_week_diff) || is.null(three_week_diff)) {
    stop("three_week_diff is required")
  }
  if (missing(harvesting_data) || is.null(harvesting_data)) {
    stop("harvesting_data is required")
  }
  
  # Extract pivot shape and age data
  tryCatch({
    pivotShape <- field_boundaries %>% terra::subset(field %in% pivotName) %>% sf::st_transform(terra::crs(current_ci))
    age <- harvesting_data %>% 
           dplyr::filter(field %in% pivotName) %>% 
           sort("year") %>% 
           tail(., 1) %>% 
           dplyr::select(age) %>% 
           unique() %>% 
           pull() %>% 
           round()
    
    # Filter for the specific pivot
    AllPivots2 <- field_boundaries %>% dplyr::filter(field %in% pivotName)
    
    # Create crop masks for different timepoints using terra functions
    singlePivot <- terra::crop(current_ci, pivotShape) %>% terra::mask(., pivotShape)
    singlePivot_m1 <- terra::crop(ci_minus_1, pivotShape) %>% terra::mask(., pivotShape)
    singlePivot_m2 <- terra::crop(ci_minus_2, pivotShape) %>% terra::mask(., pivotShape)
    
    # Create difference maps
    abs_CI_last_week <- terra::crop(last_week_diff, pivotShape) %>% terra::mask(., pivotShape)
    abs_CI_three_week <- terra::crop(three_week_diff, pivotShape) %>% terra::mask(., pivotShape)
    
    # Get planting date
    planting_date <- harvesting_data %>% 
                     dplyr::filter(field %in% pivotName) %>% 
                     ungroup() %>% 
                     dplyr::select(season_start) %>% 
                     unique()
    
    # Create spans for borders
    joined_spans2 <- field_boundaries %>% 
                     sf::st_transform(sf::st_crs(pivotShape)) %>%                     dplyr::filter(field %in% pivotName)
    
    # Create the maps for different timepoints
    CImap_m2 <- create_CI_map(singlePivot_m2, AllPivots2, joined_spans2, 
                              show_legend = TRUE, legend_is_portrait = TRUE, 
                              week = week_minus_2, age = age - 2, borders = borders, colorblind = colorblind_friendly)
    
    CImap_m1 <- create_CI_map(singlePivot_m1, AllPivots2, joined_spans2, 
                              show_legend = FALSE, legend_is_portrait = FALSE, 
                              week = week_minus_1, age = age - 1, borders = borders, colorblind = colorblind_friendly)
    
    CImap <- create_CI_map(singlePivot, AllPivots2, joined_spans2, 
                          show_legend = FALSE, legend_is_portrait = FALSE, 
                          week = week, age = age, borders = borders, colorblind = colorblind_friendly)
      # Create difference maps - only show legend on the second one to avoid redundancy
    CI_max_abs_last_week <- create_CI_diff_map(abs_CI_last_week, AllPivots2, joined_spans2,  
                                              show_legend = FALSE, legend_is_portrait = FALSE, 
                                              week_1 = week, week_2 = week_minus_1, age = age, borders = borders, colorblind = colorblind_friendly)
    
    CI_max_abs_three_week <- create_CI_diff_map(abs_CI_three_week, AllPivots2, joined_spans2, 
                                               show_legend = TRUE, legend_is_portrait = TRUE, 
                                               week_1 = week, week_2 = week_minus_3, age = age, borders = borders, colorblind = colorblind_friendly)
      # Arrange the maps with equal widths
    tst <- tmap_arrange(CImap_m2, CImap_m1, CImap, CI_max_abs_last_week, CI_max_abs_three_week, 
                        nrow = 1, widths = c(0.23, 0.18, 0.18, 0.18, 0.23))
    
    # Output heading and map to R Markdown
    age_months <- round(age / 4.348, 1)
    cat(paste("## Field", pivotName, "-", age, "weeks/", age_months, "months after planting/harvest", "\n\n"))
    print(tst)
    
  }, error = function(e) {
    safe_log(paste("Error creating CI plot for pivot", pivotName, ":", e$message), "ERROR")
    cat(paste("# Field", pivotName, "- Error creating visualization", "\n\n"))
    cat(paste("Error:", e$message, "\n\n"))
  })
}

#' Creates a plot showing Chlorophyll Index data over time for a pivot field
#'
#' @param pivotName The name or ID of the pivot field to visualize
#' @param ci_quadrant_data Data frame containing CI quadrant data with field, sub_field, Date, DOY, cumulative_CI, value and season columns
#' @param plot_type Type of plot to generate: "absolute", "cumulative", or "both"
#' @param facet_on Whether to facet the plot by season (TRUE) or overlay all seasons (FALSE)
#' @param x_unit Unit for x-axis: "days" for DOY or "weeks" for week number (default: "days")
#' @param colorblind_friendly Whether to use colorblind-friendly color schemes (default: FALSE)
#' @param show_benchmarks Whether to show historical benchmark lines (default: FALSE)
#' @param estate_name Name of the estate for benchmark calculation (required if show_benchmarks = TRUE)
#' @param benchmark_percentiles Vector of percentiles for benchmarks (default: c(10, 50, 90))
#' @return NULL (adds output directly to R Markdown document)
#'
cum_ci_plot <- function(pivotName, ci_quadrant_data = CI_quadrant, plot_type = "absolute", facet_on = FALSE, x_unit = "days", colorblind_friendly = FALSE, show_benchmarks = FALSE, estate_name = NULL, benchmark_percentiles = c(10, 50, 90), benchmark_data = NULL) {
  # Input validation
  if (missing(pivotName) || is.null(pivotName) || pivotName == "") {
    stop("pivotName is required")
  }
  if (missing(ci_quadrant_data) || is.null(ci_quadrant_data)) {
    stop("ci_quadrant_data is required")
  }
  if (!plot_type %in% c("absolute", "cumulative", "both")) {
    stop("plot_type must be one of: 'absolute', 'cumulative', or 'both'")
  }
  if (!x_unit %in% c("days", "weeks")) {
    stop("x_unit must be either 'days' or 'weeks'")
  }
  
  # Filter data for the specified pivot
  tryCatch({
    data_ci <- ci_quadrant_data %>% dplyr::filter(field == pivotName)
    
    if (nrow(data_ci) == 0) {
      safe_log(paste("No CI data found for field", pivotName), "WARNING")
      return(cum_ci_plot2(pivotName))  # Use fallback function when no data is available
    }
    
    # Process data
    data_ci2 <- data_ci %>% 
      dplyr::mutate(CI_rate = cumulative_CI / DOY,
             week = lubridate::week(Date)) %>% 
      dplyr::group_by(field) %>%
      dplyr::mutate(mean_CIrate_rolling_10_days = zoo::rollapplyr(CI_rate, width = 10, FUN = mean, partial = TRUE),
             mean_rolling_10_days = zoo::rollapplyr(value, width = 10, FUN = mean, partial = TRUE))
    
    data_ci2 <- data_ci2 %>% dplyr::mutate(season = as.factor(season))
    
    # Compute benchmarks if requested and not provided
    if (show_benchmarks && is.null(benchmark_data)) {
      benchmark_data <- compute_ci_benchmarks(ci_quadrant_data, estate_name, benchmark_percentiles)
    }
    
    # Prepare benchmark data for plotting if available
    if (!is.null(benchmark_data)) {
      benchmark_data <- benchmark_data %>%
        dplyr::mutate(
          ci_type_label = case_when(
            ci_type == "value" ~ "10-Day Rolling Mean CI",
            ci_type == "cumulative_CI" ~ "Cumulative CI",
            TRUE ~ ci_type
          ),
          benchmark_label = paste0(percentile, "th Percentile")
        )
      safe_log("Benchmark data prepared for plotting", "INFO")
    } else if (show_benchmarks) {
      safe_log("No benchmark data available", "WARNING")
    }
    data_ci3 <- tidyr::pivot_longer(
      data_ci2, 
      cols = c("mean_rolling_10_days", "cumulative_CI"),
      names_to = "ci_type",        # This column will say "mean_rolling_10_days" or "cumulative_CI"
      values_to = "ci_value"       # This column will have the numeric values
    )
    
    # Prepare date information by season
    date_preparation_perfect_pivot <- data_ci2 %>% 
      dplyr::group_by(season) %>% 
      dplyr::summarise(min_date = min(Date),
                max_date = max(Date),
                days = max_date - min_date)
    
    # Get the 3 most recent seasons
    unique_seasons <- sort(unique(date_preparation_perfect_pivot$season), decreasing = TRUE)[1:3]
    latest_season <- unique_seasons[1]  # Identify the latest season
    
    # Create plotting function that uses data_ci3 and filters by ci_type
    create_plot <- function(ci_type_filter, y_label, title_suffix) {
      # Filter data based on ci_type
      plot_data <- data_ci3 %>% 
        dplyr::filter(season %in% unique_seasons, ci_type == ci_type_filter) %>%
        dplyr::mutate(is_latest = season == latest_season)  # Flag for latest season
      
      # Determine x-axis variable based on x_unit parameter
      x_var <- if (x_unit == "days") {
        if (facet_on) "Date" else "DOY"
      } else {
        "week"
      }
      
      x_label <- switch(x_unit,
                       "days" = if (facet_on) "Date" else "Age of Crop (Days)",
                       "weeks" = "Week Number")
      
      # Create plot with either facets by season or overlay by DOY/week
      if (facet_on) {
        g <- ggplot2::ggplot(data = plot_data) +
          ggplot2::facet_wrap(~season, scales = "free_x") +
          ggplot2::geom_line(ggplot2::aes_string(x = x_var, y = "ci_value", col = "sub_field", group = "sub_field")) +
          ggplot2::labs(title = paste("Plot of", y_label),
               color = "Field Name",
               y = y_label,
               x = x_label) +
          ggplot2::scale_x_date(date_breaks = "1 month", date_labels = "%m-%Y",
                       sec.axis = ggplot2::sec_axis(~ ., name = "Age in Months",
                                                   breaks = scales::breaks_pretty(),
                                                   labels = function(x) round(as.numeric(x - min(x)) / 30.44, 1))) +
          ggplot2::theme_minimal() +
          ggplot2::theme(axis.text.x = ggplot2::element_text(hjust = 0.5),
                axis.text.x.top = ggplot2::element_text(hjust = 0.5),
                axis.title.x.top = ggplot2::element_text(size = 8),
                legend.justification = c(1, 0), legend.position = c(1, 0),
                legend.title = ggplot2::element_text(size = 8),
                legend.text = ggplot2::element_text(size = 8)) +
          ggplot2::guides(color = ggplot2::guide_legend(nrow = 2, byrow = TRUE))
      } else {
        # Choose color palette based on colorblind_friendly flag
        color_scale <- if (colorblind_friendly) {
          ggplot2::scale_color_brewer(type = "qual", palette = "Set2")
        } else {
          ggplot2::scale_color_discrete()
        }
        
        g <- ggplot2::ggplot(data = plot_data) +
          # Add benchmark lines first (behind season lines)
          {
            if (!is.null(benchmark_data) && ci_type_filter %in% benchmark_data$ci_type) {
              benchmark_subset <- benchmark_data %>% 
                dplyr::filter(ci_type == ci_type_filter) %>%
                dplyr::mutate(
                  benchmark_x = if (x_var == "DOY") {
                    DOY
                  } else if (x_var == "week") {
                    DOY / 7  # Approximate conversion
                  } else {
                    DOY  # For Date, use DOY as is (may not align perfectly)
                  }
                )
              ggplot2::geom_smooth(
                data = benchmark_subset,
                ggplot2::aes_string(x = "benchmark_x", y = "benchmark_value", group = "factor(percentile)"),
                color = "gray70", size = 0.5, se = FALSE, inherit.aes = FALSE
              )
            }
          } +
          # Plot older seasons with lighter lines
          ggplot2::geom_line(
            data = plot_data %>% dplyr::filter(!is_latest),
            ggplot2::aes_string(x = x_var, y = "ci_value", col = "season", group = "season"),
            size = 0.7, alpha = 0.4
          ) +
          # Plot latest season with thicker, more prominent line
          ggplot2::geom_line(
            data = plot_data %>% dplyr::filter(is_latest),
            ggplot2::aes_string(x = x_var, y = "ci_value", col = "season", group = "season"),
            size = 1.5, alpha = 1
          ) +
          ggplot2::labs(title = paste("Plot of", y_label, "for Field", pivotName, title_suffix),
               color = "Season",
               y = y_label,
               x = x_label) +
          color_scale +
          {
            if (x_var == "DOY") {
              ggplot2::scale_x_continuous(breaks = seq(0, 450, by = 50), sec.axis = ggplot2::sec_axis(~ . / 30.44, name = "Age in Months", breaks = seq(0, 14, by = 1)))
            } else if (x_var == "week") {
              ggplot2::scale_x_continuous(breaks = seq(0, 64, by = 4), sec.axis = ggplot2::sec_axis(~ . / 4.348, name = "Age in Months", breaks = seq(0, 14, by = 1)))
            }
          } +
          ggplot2::theme_minimal() +
          ggplot2::theme(axis.text.x = ggplot2::element_text(hjust = 0.5),
                axis.text.x.top = ggplot2::element_text(hjust = 0.5),
                axis.title.x.top = ggplot2::element_text(size = 8),
                legend.justification = c(1, 0), legend.position = c(1, 0),
                legend.title = ggplot2::element_text(size = 8),
                legend.text = ggplot2::element_text(size = 8)) +
          ggplot2::guides(color = ggplot2::guide_legend(nrow = 2, byrow = TRUE))
      }
      
      # Add y-axis limits for absolute CI (10-day rolling mean) to fix scale at 0-7
      if (ci_type_filter == "mean_rolling_10_days") {
        g <- g + ggplot2::ylim(0, 7)
      }
      
      return(g)
    }
    
    # Generate plots based on plot_type
    if (plot_type == "absolute") {
      g <- create_plot("mean_rolling_10_days", "10-Day Rolling Mean CI", "")
      subchunkify(g, 2.8, 10)
    } else if (plot_type == "cumulative") {
      g <- create_plot("cumulative_CI", "Cumulative CI", "")
      subchunkify(g, 2.8, 10)
    } else if (plot_type == "both") {
      # Create faceted plot with both CI types using pivot_longer approach
      plot_data_both <- data_ci3 %>% 
        dplyr::filter(season %in% unique_seasons) %>%
        dplyr::mutate(
          ci_type_label = case_when(
            ci_type == "mean_rolling_10_days" ~ "10-Day Rolling Mean CI",
            ci_type == "cumulative_CI" ~ "Cumulative CI",
            TRUE ~ ci_type
          ),
          is_latest = season == latest_season  # Flag for latest season
        )
      
      # Determine x-axis variable based on x_unit parameter
      x_var <- if (x_unit == "days") {
        if (facet_on) "Date" else "DOY"
      } else {
        "week"
      }
      
      x_label <- switch(x_unit,
                       "days" = if (facet_on) "Date" else "Age of Crop (Days)",
                       "weeks" = "Week Number")
      
      # Choose color palette based on colorblind_friendly flag
      color_scale <- if (colorblind_friendly) {
        ggplot2::scale_color_brewer(type = "qual", palette = "Set2")
      } else {
        ggplot2::scale_color_discrete()
      }
      
      # Create the faceted plot
      g_both <- ggplot2::ggplot(data = plot_data_both) +
        # Add benchmark lines first (behind season lines)
        {
          if (!is.null(benchmark_data)) {
            benchmark_subset <- benchmark_data %>%
              dplyr::mutate(
                benchmark_x = if (x_var == "DOY") {
                  DOY
                } else if (x_var == "week") {
                  DOY / 7
                } else {
                  DOY
                },
                ci_type_label = case_when(
                  ci_type == "value" ~ "10-Day Rolling Mean CI",
                  ci_type == "cumulative_CI" ~ "Cumulative CI",
                  TRUE ~ ci_type
                )
              )
            ggplot2::geom_smooth(
              data = benchmark_subset,
              ggplot2::aes_string(x = "benchmark_x", y = "benchmark_value", group = "factor(percentile)"),
              color = "gray70", size = 0.5, se = FALSE, inherit.aes = FALSE
            )
          }
        } +
        ggplot2::facet_wrap(~ci_type_label, scales = "free_y") +
        # Plot older seasons with lighter lines
        ggplot2::geom_line(
          data = plot_data_both %>% dplyr::filter(!is_latest),
          ggplot2::aes_string(x = x_var, y = "ci_value", col = "season", group = "season"),
          size = 0.7, alpha = 0.4
        ) +
        # Plot latest season with thicker, more prominent line
        ggplot2::geom_line(
          data = plot_data_both %>% dplyr::filter(is_latest),
          ggplot2::aes_string(x = x_var, y = "ci_value", col = "season", group = "season"),
          size = 1.5, alpha = 1
        ) +
        ggplot2::labs(title = paste("CI Analysis for Field", pivotName),
             color = "Season",
             y = "CI Value",
             x = x_label) +
        color_scale +
        {
          if (x_var == "DOY") {
            ggplot2::scale_x_continuous(breaks = seq(0, 450, by = 50), sec.axis = ggplot2::sec_axis(~ . / 30.44, name = "Age in Months", breaks = seq(0, 14, by = 1)))
          } else if (x_var == "week") {
            ggplot2::scale_x_continuous(breaks = seq(0, 64, by = 4), sec.axis = ggplot2::sec_axis(~ . / 4.348, name = "Age in Months", breaks = seq(0, 14, by = 1)))
          } else if (x_var == "Date") {
            ggplot2::scale_x_date(breaks = "1 month", date_labels = "%b-%Y", sec.axis = ggplot2::sec_axis(~ ., name = "Age in Months", breaks = scales::breaks_pretty()))
          }
        } +
        ggplot2::theme_minimal() +
        ggplot2::theme(axis.text.x = ggplot2::element_text(hjust = 0.5),
              axis.text.x.top = ggplot2::element_text(hjust = 0.5),
              axis.title.x.top = ggplot2::element_text(size = 8),
              legend.justification = c(1, 0), legend.position = c(1, 0),
              legend.title = ggplot2::element_text(size = 8),
              legend.text = ggplot2::element_text(size = 8)) +
        ggplot2::guides(color = ggplot2::guide_legend(nrow = 2, byrow = TRUE))
      
      # For the rolling mean data, we want to set reasonable y-axis limits
      # Since we're using free_y scales, each facet will have its own y-axis
      # The rolling mean will automatically scale to its data range, 
      # but we can ensure it shows the 0-7 context by adding invisible points
      
      # Add invisible points to set the y-axis range for rolling mean facet
      dummy_data <- data.frame(
        ci_type_label = "10-Day Rolling Mean CI",
        ci_value = c(0, 7),
        stringsAsFactors = FALSE
      )
      dummy_data[[x_var]] <- range(plot_data_both[[x_var]], na.rm = TRUE)
      dummy_data[["season"]] <- factor("dummy", levels = levels(plot_data_both[["season"]]))
      
      g_both <- g_both + 
        ggplot2::geom_point(data = dummy_data, 
                   ggplot2::aes_string(x = x_var, y = "ci_value"), 
                   alpha = 0, size = 0)  # Invisible points to set scale
      
      # Display the combined faceted plot
      subchunkify(g_both, 2.8, 10)
    }
    
  }, error = function(e) {
    safe_log(paste("Error creating CI trend plot for pivot", pivotName, ":", e$message), "ERROR")
    cum_ci_plot2(pivotName)  # Use fallback function in case of error
  })
}

#' Fallback function for creating CI visualization when data is missing
#'
#' @param pivotName The name or ID of the pivot field to visualize
#' @return NULL (adds output directly to R Markdown document)
#'
cum_ci_plot2 <- function(pivotName){
  # Input validation
  if (missing(pivotName) || is.null(pivotName) || pivotName == "") {
    stop("pivotName is required")
  }
  
  # Create a simple plot showing "No data available"
  tryCatch({
    end_date <- Sys.Date()
    start_date <- end_date %m-% months(11) # 11 months ago from end_date
    date_seq <- seq.Date(from = start_date, to = end_date, by = "month")
    midpoint_date <- start_date + (end_date - start_date) / 2
    
    g <- ggplot() +
      scale_x_date(limits = c(start_date, end_date), date_breaks = "1 month", date_labels = "%m-%Y") +
      scale_y_continuous(limits = c(0, 4)) +
      labs(title = paste("14 day rolling MEAN CI rate - Field ", pivotName),
           x = "Date", y = "CI Rate") +
      theme_minimal() +
      theme(axis.text.x = element_text(hjust = 0.5),
            legend.justification = c(1, 0), legend.position = c(1, 0),
            legend.title = element_text(size = 8),
            legend.text = element_text(size = 8)) +
      annotate("text", x = midpoint_date, y = 2, label = "No data available", size = 6, hjust = 0.5)
    
    subchunkify(g, 3.2, 10)
    
  }, error = function(e) {
    safe_log(paste("Error creating fallback CI plot for pivot", pivotName, ":", e$message), "ERROR")
    cat(paste("No data available for field", pivotName, "\n"))
  })
}

#' Gets the file path for a specific week's mosaic
#'
#' @param mosaic_path Base directory containing mosaic files
#' @param input_date Reference date to calculate from
#' @param week_offset Number of weeks to offset from input date (positive or negative)
#' @return File path to the requested week's mosaic TIF file
#'
get_week_path <- function(mosaic_path, input_date, week_offset) {
  # Input validation
  if (missing(mosaic_path) || is.null(mosaic_path) || mosaic_path == "") {
    stop("mosaic_path is required")
  }
  if (missing(input_date)) {
    stop("input_date is required")
  }
  
  tryCatch({
    # Convert input_date to Date object (in case it's a string)
    input_date <- as.Date(input_date)
    if (is.na(input_date)) {
      stop("Invalid input_date. Expected a Date object or a string convertible to Date.")
    }
    
    # Validate week_offset
    week_offset <- as.integer(week_offset)
    if (is.na(week_offset)) {
      stop("Invalid week_offset. Expected an integer value.")
    }
    
    # Get the start of the week for the input date (adjust to Monday as the start of the week)
    start_of_week <- lubridate::floor_date(input_date, unit = "week", week_start = 1)
    
    # Calculate the new date after applying the week offset
    target_date <- start_of_week + lubridate::weeks(week_offset)
    
    # Get the week number and year of the target date
    target_week <- sprintf("%02d", lubridate::isoweek(target_date))  # Left-pad week number with a zero if needed
    target_year <- lubridate::isoyear(target_date)
    
    # Generate the file path for the target week
    path_to_week <- here::here(mosaic_path, paste0("week_", target_week, "_", target_year, ".tif"))
    
    # Log the path calculation
    safe_log(paste("Calculated path for week", target_week, "of year", target_year, ":", path_to_week), "INFO")
    
    # Return the path
    return(path_to_week)
    
  }, error = function(e) {
    safe_log(paste("Error calculating week path:", e$message), "ERROR")
    stop(e$message)
  })
}

#' Computes historical percentile benchmarks for CI data per estate
#'
#' @param ci_quadrant_data Data frame containing CI quadrant data with field, Date, DOY, cumulative_CI, value, season columns
#' @param estate_name Name of the estate/client to filter data for
#' @param percentiles Vector of percentiles to compute (e.g., c(10, 50, 90))
#' @param min_seasons Minimum number of seasons required for reliable benchmarks (default: 3)
#' @return Data frame with DOY, percentile, ci_type, benchmark_value, or NULL if insufficient data
#'
compute_ci_benchmarks <- function(ci_quadrant_data, estate_name, percentiles = c(10, 50, 90), min_seasons = 3) {
  # Input validation
  if (missing(ci_quadrant_data) || is.null(ci_quadrant_data)) {
    stop("ci_quadrant_data is required")
  }
  if (missing(estate_name) || is.null(estate_name) || estate_name == "") {
    stop("estate_name is required")
  }
  if (!all(percentiles >= 0 & percentiles <= 100)) {
    stop("percentiles must be between 0 and 100")
  }
  
  tryCatch({
    # Filter data for the specified estate (assuming estate is not directly in data, but we can infer from context)
    # Since the data is per field, and fields are unique to estates, we'll use all data but could add estate filtering if available
    data_filtered <- ci_quadrant_data
    
    # Check if we have enough seasons
    unique_seasons <- unique(data_filtered$season)
    if (length(unique_seasons) < min_seasons) {
      safe_log(paste("Insufficient historical seasons for estate", estate_name, ":", length(unique_seasons), "seasons found, need at least", min_seasons), "WARNING")
      return(NULL)
    }
    
    # Prepare data for both CI types
    data_prepared <- data_filtered %>%
      dplyr::ungroup() %>%  # Ensure no existing groupings
      dplyr::select(DOY, value, cumulative_CI, season) %>%
      tidyr::pivot_longer(
        cols = c("value", "cumulative_CI"),
        names_to = "ci_type",
        values_to = "ci_value"
      ) %>%
      dplyr::filter(!is.na(ci_value))  # Remove NA values
    
    # Compute percentiles for each DOY and ci_type
    benchmarks <- data_prepared %>%
      dplyr::group_by(DOY, ci_type) %>%
      dplyr::summarise(
        p10 = tryCatch(quantile(ci_value, 0.1, na.rm = TRUE), error = function(e) NA_real_),
        p50 = tryCatch(quantile(ci_value, 0.5, na.rm = TRUE), error = function(e) NA_real_),
        p90 = tryCatch(quantile(ci_value, 0.9, na.rm = TRUE), error = function(e) NA_real_),
        n_observations = n(),
        .groups = 'drop'
      ) %>%
      dplyr::filter(n_observations >= min_seasons) %>%  # Only include DOYs with sufficient data
      tidyr::pivot_longer(
        cols = c(p10, p50, p90),
        names_to = "percentile",
        values_to = "benchmark_value"
      ) %>%
      dplyr::mutate(
        percentile = case_when(
          percentile == "p10" ~ 10,
          percentile == "p50" ~ 50,
          percentile == "p90" ~ 90
        )
      ) %>%
      dplyr::filter(!is.na(benchmark_value))  # Remove any NA benchmarks
    
    # Rename columns for clarity
    benchmarks <- benchmarks %>%
      dplyr::select(DOY, ci_type, percentile, benchmark_value)
    
    safe_log(paste("Computed CI benchmarks for estate", estate_name, "with", length(unique_seasons), "seasons and", nrow(benchmarks), "benchmark points"), "INFO")
    
    return(benchmarks)
    
  }, error = function(e) {
    safe_log(paste("Error computing CI benchmarks for estate", estate_name, ":", e$message), "ERROR")
    print(paste("DEBUG: Error details:", e$message, "Call:", deparse(e$call)))
    return(NULL)
  })
}