# SAR Visualization Script
# ========================
# 
# Functions for creating maps and plots of Sentinel-1 SAR data
# Integrates with existing tmap and ggplot2 workflow
#
# Author: Timon
# Date: August 2025

# Load required libraries
# source("sar_analysis_functions.R")  # Don't source here, will be sourced in main script

# Load additional plotting libraries
if (!require(RColorBrewer)) install.packages("RColorBrewer")
if (!require(scales)) install.packages("scales")
if (!require(patchwork)) install.packages("patchwork")

library(RColorBrewer)
library(scales)
library(patchwork)

# SAR Mapping Functions
# ====================

#' Create SAR backscatter map
#' 
#' @param sar_data SAR raster data
#' @param field_boundaries Field boundary polygons
#' @param title Map title
#' @param band SAR band name for legend
#' @param interactive Create interactive map (default TRUE)
#' @return tmap object
create_sar_map <- function(sar_data, field_boundaries = NULL, title = "SAR Backscatter", 
                          band = "VV_dB", interactive = TRUE) {
  
  # Create base map
  map_plot <- tm_shape(sar_data) +
    tm_raster(
      title = paste(band, "(dB)"),
      palette = "viridis",
      style = "cont",
      n = 10,
      alpha = 0.8
    ) +
    tm_layout(
      title = title,
      title.size = 1.2
    )
  
  # Add field boundaries if provided
  if (!is.null(field_boundaries)) {
    map_plot <- map_plot +
      tm_shape(field_boundaries) +
      tm_borders(col = "white", lwd = 2, alpha = 0.9) +
      tm_fill(alpha = 0.1, col = "lightblue") +
      tm_text("Name", size = 0.7, col = "black", bg.color = "white", bg.alpha = 0.7)
  }
  
  # Add interactive features if in view mode
  if (interactive && tmap_mode() == "view") {
    if (!is.null(field_boundaries)) {
      map_plot <- map_plot +
        tm_shape(field_boundaries) +
        tm_polygons(
          alpha = 0.2,
          col = "lightblue",
          border.col = "white",
          border.lwd = 2,
          popup.vars = c("Field Name" = "Name", "Area (ha)" = "Area_ha"),
          id = "field_label"
        )
    }
  }
  
  return(map_plot)
}

#' Create multi-temporal RGB SAR visualization
#' Your idea: Week1=Red, Week2=Green, Week3=Blue for change detection
#' 
#' @param week1_data SAR data for week 1 (Red channel)
#' @param week2_data SAR data for week 2 (Green channel)  
#' @param week3_data SAR data for week 3 (Blue channel)
#' @param field_boundaries Field boundaries
#' @return tmap object
create_sar_rgb_change_map <- function(week1_data, week2_data, week3_data, 
                                     field_boundaries = NULL) {
  
  cat("Creating SAR RGB change detection map...\n")
  
  # Normalize each week to 0-255 range for RGB
  week1_norm <- (week1_data - global(week1_data, min, na.rm = TRUE)[1,1]) / 
                (global(week1_data, max, na.rm = TRUE)[1,1] - global(week1_data, min, na.rm = TRUE)[1,1]) * 255
  
  week2_norm <- (week2_data - global(week2_data, min, na.rm = TRUE)[1,1]) / 
                (global(week2_data, max, na.rm = TRUE)[1,1] - global(week2_data, min, na.rm = TRUE)[1,1]) * 255
  
  week3_norm <- (week3_data - global(week3_data, min, na.rm = TRUE)[1,1]) / 
                (global(week3_data, max, na.rm = TRUE)[1,1] - global(week3_data, min, na.rm = TRUE)[1,1]) * 255
  
  # Stack for RGB
  rgb_stack <- c(week1_norm, week2_norm, week3_norm)
  names(rgb_stack) <- c("Red", "Green", "Blue")
  
  # Create RGB map
  map_plot <- tm_shape(rgb_stack) +
    tm_rgb(
      r = 1, g = 2, b = 3,
      alpha = 0.8
    ) +
    tm_layout(
      title = "SAR Change Detection (RGB)\nRed=Week1, Green=Week2, Blue=Week3",
      title.size = 1.0
    )
  
  # Add field boundaries
  if (!is.null(field_boundaries)) {
    map_plot <- map_plot +
      tm_shape(field_boundaries) +
      tm_borders(col = "white", lwd = 0.5, alpha = 0.7)
  }
  
  return(map_plot)
}

#' Create SAR comparison map (side-by-side weeks)
#' 
#' @param sar_data1 First SAR dataset
#' @param sar_data2 Second SAR dataset
#' @param title1 Title for first map
#' @param title2 Title for second map
#' @param field_boundaries Field boundaries
#' @return Combined tmap
create_sar_comparison_map <- function(sar_data1, sar_data2, title1 = "Week 1", 
                                     title2 = "Week 2", field_boundaries = NULL) {
  
  # Create individual maps
  map1 <- create_sar_map(sar_data1, field_boundaries, title1)
  map2 <- create_sar_map(sar_data2, field_boundaries, title2)
  
  # Combine maps
  combined_map <- tmap_arrange(map1, map2, ncol = 2)
  
  return(combined_map)
}

#' Create SAR metrics map (RVI, Cross-pol ratio, etc.)
#' 
#' @param metric_data Calculated SAR metric raster
#' @param metric_name Name of the metric
#' @param field_boundaries Field boundaries
#' @param color_palette Color palette to use
#' @param interactive Create interactive map
#' @return tmap object
create_sar_metrics_map <- function(metric_data, metric_name = "SAR Metric", 
                                  field_boundaries = NULL, color_palette = "RdYlGn",
                                  interactive = TRUE) {
  
  # Create map
  map_plot <- tm_shape(metric_data) +
    tm_raster(
      title = metric_name,
      palette = color_palette,
      style = "quantile",
      n = 8,
      alpha = 0.8
    ) +
    tm_layout(
      title = paste("SAR", metric_name),
      title.size = 1.2
    )
  
  # Add field boundaries with enhanced interactivity
  if (!is.null(field_boundaries)) {
    if (interactive && tmap_mode() == "view") {
      map_plot <- map_plot +
        tm_shape(field_boundaries) +
        tm_polygons(
          alpha = 0.2,
          col = "lightblue",
          border.col = "white",
          border.lwd = 2,
          popup.vars = c("Field Name" = "Name"),
          id = "field_label"
        )
    } else {
      map_plot <- map_plot +
        tm_shape(field_boundaries) +
        tm_borders(col = "white", lwd = 2, alpha = 0.9) +
        tm_text("Name", size = 0.6, col = "black", bg.color = "white", bg.alpha = 0.7)
    }
  }
  
  return(map_plot)
}

# SAR Time Series Plotting Functions
# ==================================

#' Plot SAR time series for multiple fields
#' 
#' @param field_ids Vector of field IDs to plot
#' @param field_boundaries Field boundary polygons
#' @param week_range Range of weeks
#' @param band SAR band to plot
#' @param facet_fields Create separate panels for each field
#' @return ggplot object
plot_sar_timeseries <- function(field_ids, field_boundaries, week_range = 26:33, 
                               band = "VV_dB_filtered", facet_fields = TRUE) {
  
  cat("Creating SAR time series plot for", length(field_ids), "fields...\n")
  
  # Collect data for all fields
  all_data <- data.frame()
  
  for (field_id in field_ids) {
    field_data <- analyze_sar_field_trends(field_id, field_boundaries, week_range, band)
    if (!is.null(field_data)) {
      all_data <- rbind(all_data, field_data$timeseries)
    }
  }
  
  if (nrow(all_data) == 0) {
    stop("No data found for specified fields")
  }
  
  # Create plot
  p <- ggplot(all_data, aes(x = week, y = value, color = field_id)) +
    geom_line(size = 1.2, alpha = 0.8) +
    geom_point(size = 2.5, alpha = 0.9) +
    labs(
      title = paste("SAR Time Series -", band),
      subtitle = paste("Weeks", min(week_range), "to", max(week_range), "2025"),
      x = "Week Number",
      y = paste(band, "Backscatter (dB)"),
      color = "Field ID"
    ) +
    theme_minimal() +
    theme(
      plot.title = element_text(size = 14, face = "bold"),
      plot.subtitle = element_text(size = 12),
      axis.text = element_text(size = 10),
      axis.title = element_text(size = 12),
      legend.title = element_text(size = 11),
      legend.text = element_text(size = 10)
    ) +
    scale_color_viridis_d(option = "plasma") +
    scale_x_continuous(breaks = week_range)
  
  # Add faceting if requested
  if (facet_fields && length(field_ids) > 1) {
    p <- p + facet_wrap(~field_id, scales = "free_y", ncol = 2)
  }
  
  return(p)
}

#' Plot SAR vs Optical CI comparison
#' 
#' @param field_id Field to analyze
#' @param field_boundaries Field boundaries
#' @param week_range Week range
#' @param ci_data_dir Directory with CI mosaics
#' @return ggplot object
plot_sar_vs_ci_comparison <- function(field_id, field_boundaries, week_range = 26:33,
                                     ci_data_dir = "data/aura/weekly_CI_mosaic") {
  
  cat("Creating SAR vs CI comparison for field:", field_id, "\n")
  
  # Get SAR data
  sar_data <- analyze_sar_field_trends(field_id, field_boundaries, week_range, "VV_dB_filtered")
  
  # Get CI data (adapt this to your CI loading function)
  ci_data <- data.frame()
  field_poly <- field_boundaries[field_boundaries$Name == field_id, ]
  
  for (week in week_range) {
    ci_file <- file.path(ci_data_dir, paste0("week_", sprintf("%02d", week), "_2025.tif"))
    if (file.exists(ci_file)) {
      ci_raster <- rast(ci_file)
      ci_value <- extract(ci_raster, field_poly, fun = mean, na.rm = TRUE)[1, 2]
      ci_data <- rbind(ci_data, data.frame(week = week, ci_value = ci_value))
    }
  }
  
  # Combine datasets
  if (nrow(ci_data) > 0 && !is.null(sar_data)) {
    combined_data <- merge(sar_data$timeseries, ci_data, by = "week", all = TRUE)
    
    # Create dual-axis plot
    p1 <- ggplot(combined_data, aes(x = week)) +
      geom_line(aes(y = value, color = "SAR VV_dB"), size = 1.2) +
      geom_point(aes(y = value, color = "SAR VV_dB"), size = 2.5) +
      labs(
        title = paste("SAR vs CI Comparison - Field", field_id),
        x = "Week Number",
        y = "SAR Backscatter (dB)",
        color = "Data Type"
      ) +
      theme_minimal() +
      scale_color_manual(values = c("SAR VV_dB" = "#440154")) +
      scale_x_continuous(breaks = week_range)
    
    # Add CI on secondary axis (approximation)
    if (!all(is.na(combined_data$ci_value))) {
      p1 <- p1 +
        geom_line(aes(y = scales::rescale(ci_value, to = range(value, na.rm = TRUE)), 
                     color = "CI"), size = 1.2) +
        geom_point(aes(y = scales::rescale(ci_value, to = range(value, na.rm = TRUE)), 
                      color = "CI"), size = 2.5) +
        scale_color_manual(values = c("SAR VV_dB" = "#440154", "CI" = "#35B779"))
    }
    
    return(p1)
  } else {
    warning("Insufficient data for comparison")
    return(NULL)
  }
}

#' Create SAR metrics dashboard
#' 
#' @param week_num Week to analyze
#' @param field_boundaries Field boundaries
#' @param interactive Create interactive dashboard
#' @return List of individual maps for interactive display
create_sar_dashboard <- function(week_num = 33, field_boundaries, interactive = TRUE) {
  
  cat("Creating SAR dashboard for week", week_num, "...\n")
  
  # Load different SAR bands
  vv_linear <- load_sar_mosaic(week_num, band = "VV")
  vh_linear <- load_sar_mosaic(week_num, band = "VH")
  vv_db <- load_sar_mosaic(week_num, band = "VV_dB_filtered")
  vh_db <- load_sar_mosaic(week_num, band = "VH_dB_filtered")
  
  # Calculate metrics
  rvi <- calculate_rvi(vv_linear, vh_linear)
  cross_ratio <- calculate_cross_pol_ratio(vv_db, vh_db)
  csi <- calculate_crop_structure_index(vv_linear, vh_linear)
  
  # Create individual maps for interactive display
  map1 <- create_sar_map(vv_db, field_boundaries, "VV Backscatter", "VV_dB", interactive)
  map2 <- create_sar_map(vh_db, field_boundaries, "VH Backscatter", "VH_dB", interactive)
  map3 <- create_sar_metrics_map(rvi, "RVI", field_boundaries, "RdYlGn", interactive)
  map4 <- create_sar_metrics_map(cross_ratio, "Cross-Pol Ratio", field_boundaries, "RdBu", interactive)
  
  # Return list of maps for individual display in RMarkdown
  return(list(
    vv_map = map1,
    vh_map = map2, 
    rvi_map = map3,
    cross_ratio_map = map4,
    rvi_data = rvi,
    cross_ratio_data = cross_ratio
  ))
}
}

cat("SAR visualization functions loaded successfully!\n")
cat("Available functions:\n")
cat("- create_sar_map(): Basic SAR backscatter map\n")
cat("- create_sar_rgb_change_map(): Multi-temporal RGB change detection\n")
cat("- create_sar_comparison_map(): Side-by-side week comparison\n")
cat("- create_sar_metrics_map(): SAR indices visualization\n")
cat("- plot_sar_timeseries(): Time series plots for fields\n")
cat("- plot_sar_vs_ci_comparison(): SAR vs optical CI comparison\n")
cat("- create_sar_dashboard(): Complete SAR metrics dashboard\n")