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))
}

create_CI_map <- function(pivot_raster, pivot_shape, pivot_spans, show_legend = F, legend_is_portrait = F, week, age, borders = FALSE){
  map <- tm_shape(pivot_raster, unit = "m") +
    tm_raster(breaks = c(0,0.5,1,2,3,4,5,6,7,Inf), palette = "RdYlGn",legend.is.portrait = legend_is_portrait ,midpoint = NA) +
    tm_layout(main.title = paste0("\nMax CI week ", week,"\n",  age, " weeks old"),
              main.title.size = 0.7, legend.show = show_legend)
  
  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)
}

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){
  map <- tm_shape(pivot_raster, unit = "m") +
    tm_raster(breaks = c(-3,-2,-1,0,1,2,3), palette = "RdYlGn",legend.is.portrait = legend_is_portrait, midpoint = 0, title = "CI difference") +
    tm_layout(main.title = paste0("CI change week ", week_1, " - week ", week_2, "\n", age, " weeks old"),
              main.title.size = 0.7, legend.show = show_legend)
  
  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)
}

ci_plot <- function(pivotName){
  # pivotName = "1.1"
  pivotShape <- AllPivots0 %>% terra::subset(field %in% pivotName)  %>% st_transform(crs(CI))
  age <- harvesting_data %>% dplyr::filter(field %in% pivotName) %>% sort("year") %>% tail(., 1) %>% dplyr::select(age) %>% unique() %>% pull() %>% round()
  
  AllPivots2 <- AllPivots0 %>% dplyr::filter(field %in% pivotName)
  
  singlePivot <- CI %>% crop(., pivotShape) %>% mask(., pivotShape)
  
  singlePivot_m1 <- CI_m1 %>% crop(., pivotShape) %>% mask(., pivotShape)
  singlePivot_m2 <- CI_m2 %>% crop(., pivotShape) %>% mask(., pivotShape)
  # singlePivot_m3 <- CI_m3 %>% crop(., pivotShape) %>% mask(., pivotShape)
  
  abs_CI_last_week <- last_week_dif_raster_abs %>% crop(., pivotShape) %>% mask(., pivotShape)
  abs_CI_three_week <- three_week_dif_raster_abs %>% crop(., pivotShape) %>% mask(., pivotShape)
  
  planting_date <- harvesting_data %>% dplyr::filter(field %in% pivotName) %>% ungroup()  %>% dplyr::select(season_start)  %>% unique()
  
  joined_spans2 <- AllPivots0 %>% st_transform(crs(pivotShape))  %>%  dplyr::filter(field %in% pivotName) #%>% unique() %>% st_crop(., pivotShape)
  
  CImap_m2 <- create_CI_map(singlePivot_m2, AllPivots2,  joined_spans2, show_legend= T, legend_is_portrait = T, week = week_minus_2, age = age  -2, borders = borders)
  CImap_m1 <- create_CI_map(singlePivot_m1, AllPivots2,  joined_spans2, show_legend= F, legend_is_portrait = F, week = week_minus_1, age = age  -1, borders = borders)
  CImap <- create_CI_map(singlePivot, AllPivots2,  joined_spans2, show_legend= F, legend_is_portrait = F, week = week, age = age, borders = borders)
  
  
  CI_max_abs_last_week <- create_CI_diff_map(abs_CI_last_week,AllPivots2,  joined_spans2,  show_legend = T, legend_is_portrait = T, 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 = T, legend_is_portrait = T, week_1 = week, week_2 = week_minus_3, age = age, borders = borders)
  
  tst <- tmap_arrange(CImap_m2, CImap_m1, CImap,CI_max_abs_last_week, CI_max_abs_three_week, nrow = 1)
  
  cat(paste("## Field", pivotName, "-", age, "weeks after planting/harvest", "\n"))
  # cat("\n")
  # cat('<h2> Pivot', pivotName, '- week', week, '-', age$Age, 'weeks after planting/harvest <h2>')
  # cat(paste("# Pivot",pivots$pivot[i],"\n"))
  print(tst)
  
}

cum_ci_plot <- function(pivotName){
  
  # pivotName = "3a13"
  data_ci <- CI_quadrant %>% filter(field == pivotName)
  
  if (nrow(data_ci) == 0) {
    return(cum_ci_plot2(pivotName))  # Return an empty data frame if no data is found
  }
  
  data_ci2 <- data_ci  %>% mutate(CI_rate = cumulative_CI/DOY,
                                  week = week(Date))%>% group_by(field) %>%
    mutate(mean_rolling10 = rollapplyr(CI_rate  , width = 10, FUN = mean, partial = TRUE))
  
  date_preperation_perfect_pivot <- data_ci2 %>% group_by(season)  %>% summarise(min_date = min(Date),
                                                                                 max_date = max(Date),
                                                                                 days = max_date - min_date)
  
  
  unique_seasons <- unique(date_preperation_perfect_pivot$season)
  
  g <- ggplot(data= data_ci2 %>% filter(season %in% unique_seasons)) +
    facet_wrap(~season, scales = "free_x") +
    geom_line( aes(Date, mean_rolling10, col = sub_field, group = sub_field)) +
    labs(title = paste("14 day rolling MEAN CI rate - Pivot ", pivotName),
         color = "Field name")+
    scale_x_date(date_breaks = "1 month", date_labels =  "%m-%Y") +
    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)) +
    guides(color = guide_legend(nrow = 2, byrow = TRUE))
  
  subchunkify(g, 3.2, 10)
  
}

cum_ci_plot <- function(pivotName, plot_type = "value", facet_on = TRUE) {
  
  # pivotName = "3a13"
  data_ci <- CI_quadrant %>% filter(field == pivotName)
  
  if (nrow(data_ci) == 0) {
    return(cum_ci_plot2(pivotName))  # Return an empty data frame if no data is found
  }
  
  data_ci2 <- data_ci %>% 
    mutate(CI_rate = cumulative_CI / DOY,
           week = week(Date)) %>% 
    group_by(field) %>%
    mutate(mean_CIrate_rolling_10_days = rollapplyr(CI_rate, width = 10, FUN = mean, partial = TRUE),
           mean_rolling_10_days = rollapplyr(value, width = 10, FUN = mean, partial = TRUE))
  
  data_ci2 <- data_ci2 %>% mutate(season = as.factor(season))
  
  date_preperation_perfect_pivot <- data_ci2 %>% 
    group_by(season) %>% 
    summarise(min_date = min(Date),
              max_date = max(Date),
              days = max_date - min_date)
  
  unique_seasons <- sort(unique(date_preperation_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")
  
  if (facet_on) {
    g <- ggplot(data = data_ci2 %>% filter(season %in% unique_seasons)) +
      facet_wrap(~season, scales = "free_x") +
      geom_line(aes_string(x = "Date", y = y_aesthetic, col = "sub_field", group = "sub_field")) +
      labs(title = paste("Plot of", y_label, "for Pivot", pivotName),
           color = "Field Name",
           y = y_label) +
      scale_x_date(date_breaks = "1 month", date_labels = "%m-%Y") +
      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)) +
      guides(color = guide_legend(nrow = 2, byrow = TRUE))
  } else {
    g <- ggplot(data = data_ci2 %>% filter(season %in% unique_seasons)) +
      geom_line(aes_string(x = "DOY", y = y_aesthetic, col = "season", group = "season")) +
      labs(title = paste("Plot of", y_label, "for Pivot", pivotName),
           color = "Season",
           y = y_label,
           x = "Age of Crop (Days)") +
      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)) +
      guides(color = guide_legend(nrow = 2, byrow = TRUE))
  }
  
  subchunkify(g, 3.2, 10)
}

cum_ci_plot2 <- function(pivotName){
  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(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)
}