# 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, ", 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){
  # 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")
  }
    # Create the base map
  map <- tm_shape(pivot_raster, unit = "m")  # Add raster with continuous spectrum (fixed scale 1-8 for consistent comparison)
  map <- map + tm_raster(col.scale = tm_scale_continuous(values = "brewer.rd_yl_gn",
                                                       limits = c(1, 8)),
                          col.legend = tm_legend(title = "CI",
                                               orientation = if(legend_is_portrait) "portrait" else "landscape", 
                                               show = show_legend,
                                               position = c("left", "bottom")))
    # Add layout elements
  map <- map + tm_layout(main.title = paste0("Max CI week ", week,"\n",  age, " weeks 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){
  # 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")
  }
    # Create the base map
  map <- tm_shape(pivot_raster, unit = "m")  # Add raster with continuous spectrum (centered at 0 for difference maps, fixed scale)
  map <- map + tm_raster(col.scale = tm_scale_continuous(values = "brewer.rd_yl_gn",
                                                       midpoint = 0,
                                                       limits = c(-3, 3)),
                          col.legend = tm_legend(title = "CI difference",
                                               orientation = if(legend_is_portrait) "portrait" else "landscape",
                                               show = show_legend,
                                               position = c("left", "bottom")))
    # Add layout elements
  map <- map + tm_layout(main.title = paste0("CI change week ", week_1, " - week ", week_2, "\n", age, " weeks 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)
#' @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){
  # 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)
    
    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)
    
    CImap <- create_CI_map(singlePivot, AllPivots2, joined_spans2, 
                          show_legend = FALSE, legend_is_portrait = FALSE, 
                          week = week, age = age, borders = borders)
      # 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)
    
    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)
    
    # Arrange the maps
    tst <- tmap_arrange(CImap_m2, CImap_m1, CImap, CI_max_abs_last_week, CI_max_abs_three_week, nrow = 1)
    
    # Output heading and map to R Markdown
    cat(paste("## Field", pivotName, "-", age, "weeks after planting/harvest", "\n"))
    print(tst)
    
  }, error = function(e) {
    safe_log(paste("Error creating CI plot for pivot", pivotName, ":", e$message), "ERROR")
    cat(paste("## Field", pivotName, "- Error creating visualization", "\n"))
    cat(paste("Error:", e$message, "\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: "value", "CI_rate", or "cumulative_CI"
#' @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")
#' @return NULL (adds output directly to R Markdown document)
#'
cum_ci_plot <- function(pivotName, ci_quadrant_data = CI_quadrant, plot_type = "value", facet_on = FALSE, x_unit = "days") {
  # 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("value", "CI_rate", "cumulative_CI")) {
    stop("plot_type must be one of: 'value', 'CI_rate', or 'cumulative_CI'")
  }
  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 pivot", 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))
    
    # 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]
    
    # Determine the y aesthetic based on the plot type
    y_aesthetic <- switch(plot_type,
                          "CI_rate" = "mean_CIrate_rolling_10_days",
                          "cumulative_CI" = "cumulative_CI",
                          "value" = "mean_rolling_10_days")
    
    y_label <- switch(plot_type,
                      "CI_rate" = "10-Day Rolling Mean CI Rate (cumulative CI / age)",
                      "cumulative_CI" = "Cumulative CI",
                      "value" = "10-Day Rolling Mean CI")
    
    # 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 = data_ci2 %>% dplyr::filter(season %in% unique_seasons)) +
        ggplot2::facet_wrap(~season, scales = "free_x") +
        ggplot2::geom_line(ggplot2::aes_string(x = x_var, y = y_aesthetic, col = "sub_field", group = "sub_field")) +
        ggplot2::labs(title = paste("Plot of", y_label, "for Pivot", pivotName),
             color = "Field Name",
             y = y_label,
             x = x_label) +
        ggplot2::scale_x_date(date_breaks = "1 month", date_labels = "%m-%Y") +
        ggplot2::theme_minimal() +
        ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 60, hjust = 1),
              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 {
      g <- ggplot2::ggplot(data = data_ci2 %>% dplyr::filter(season %in% unique_seasons)) +
        ggplot2::geom_line(ggplot2::aes_string(x = x_var, y = y_aesthetic, col = "season", group = "season")) +
        ggplot2::labs(title = paste("Plot of", y_label, "for Pivot", pivotName),
             color = "Season",
             y = y_label,
             x = x_label) +
        ggplot2::theme_minimal() +
        ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 60, hjust = 1),
              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))
    }
    
    # Output plot to R Markdown with reduced height
    subchunkify(g, 3.2, 10)  # Reduced from 3.2 to 2.8
    
  }, 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(angle = 60, hjust = 1),
            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)
  })
}