library(here)
library(sf)
library(tidyverse)
library(tmap)
library(lubridate)
library(exactextractr)
library(zoo)
library(raster)
library(terra)
library(rsample)
library(caret)
library(randomForest)
library(CAST)

project_dir <- "chemba"
source(here("r_app", "parameters_project.R"))
cumulative_CI_vals_dir <- "C:/Users/timon/Resilience BV/4020 SCane ESA DEMO - Documenten/General/4020 SCDEMO Team/4020 TechnicalData/WP3/smartcane/laravel_app/storage/app/chemba/Data/extracted_ci/cumulative_vals"
CI_quadrant <- readRDS(here(cumulative_CI_vals_dir,"All_pivots_Cumulative_CI_quadrant_year_v2.rds"))# %>%

cum_ci_plot <- function(pivotName, plot_type = "value", facet_on = TRUE, x_axis = "days") {
  
  # 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),
           week_from_doy = DOY / 7) %>% 
    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 if (x_axis == "weeks") {
    g <- ggplot(data = data_ci2 %>% filter(season %in% unique_seasons)) +
      facet_wrap(~sub_field, nrow=1) +
      geom_line(aes_string(x = "week_from_doy", y = y_aesthetic, col = "season", group = "season")) +
      labs(title = paste("Plot of", y_label, "for Pivot", pivotName),
           color = "Season",
           y = y_label,
           x = "Week of Year") +
      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)) +
      facet_wrap(~sub_field, nrow=1) +
      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))
  }
  
  return(g)
}

# facet_on FALSE
g1 <- cum_ci_plot("1.16", "value", FALSE, "days")
g2 <- cum_ci_plot("3a13", "cumulative_CI", FALSE, "days")
g3 <- cum_ci_plot("3a13", "CI_rate", FALSE, "days")
g7 <- cum_ci_plot("3a13", "value", FALSE, "weeks")
g8 <- cum_ci_plot("3a13", "cumulative_CI", FALSE, "weeks")
g9 <- cum_ci_plot("3a13", "CI_rate", FALSE, "weeks")

# facet_on TRUE
g4 <- cum_ci_plot("3a13", "value", TRUE, "days")
g5 <- cum_ci_plot("3a13", "cumulative_CI", TRUE, "days")
g6 <- cum_ci_plot("3a13", "CI_rate", TRUE, "days")