# filepath: c:\Users\timon\Resilience BV\4020 SCane ESA DEMO - Documenten\General\4020 SCDEMO Team\4020 TechnicalData\WP3\smartcane\r_app\interpolate_growth_model.R
#
# INTERPOLATE_GROWTH_MODEL.R
# =========================
# This script interpolates CI (Chlorophyll Index) values between measurement dates
# to create a continuous growth model. It generates daily values and cumulative
# CI statistics for each field.
#
# Usage: Rscript interpolate_growth_model.R [project_dir]
#   - project_dir: Project directory name (e.g., "chemba")
#

# 1. Load required packages
# -----------------------
suppressPackageStartupMessages({
  library(tidyverse)
  library(lubridate)
  library(here)
})

# 2. Main function to handle interpolation
# -------------------------------------
main <- function() {
  # Process command line arguments
  args <- commandArgs(trailingOnly = TRUE)
  
  # Get project directory from arguments or use default
  if (length(args) >= 1 && !is.na(args[1])) {
    project_dir <- as.character(args[1])
  } else {
    project_dir <- "chemba"
    message("No project_dir provided. Using default:", project_dir)
  }
  
    # Make project_dir available globally so parameters_project.R can use it
  assign("project_dir", project_dir, envir = .GlobalEnv)
  
  # Initialize project configuration and load utility functions
  tryCatch({
    source("parameters_project.R")
    source("growth_model_utils.R")
  }, error = function(e) {
    warning("Default source files not found. Attempting to source from 'r_app' directory.")
    tryCatch({
      source(here::here("r_app", "parameters_project.R"))
      source(here::here("r_app", "growth_model_utils.R"))
      warning(paste("Successfully sourced files from 'r_app' directory."))

    }, error = function(e) {
      stop("Failed to source required files from both default and 'r_app' directories.")
    })
  })
  
  log_message("Starting CI growth model interpolation")
  
  # Load and process the data
  tryCatch({
    # Load the combined CI data
    CI_data <- load_combined_ci_data(cumulative_CI_vals_dir)
    
    # Validate harvesting data
    if (is.null(harvesting_data) || nrow(harvesting_data) == 0) {
      stop("No harvesting data available")
    }
    
    # Get the years from harvesting data
    years <- harvesting_data %>% 
      filter(!is.na(season_start)) %>% 
      distinct(year) %>% 
      pull(year)
    
    log_message(paste("Processing data for years:", paste(years, collapse = ", ")))
    
    # Generate interpolated CI data for each year and field
    CI_all <- generate_interpolated_ci_data(years, harvesting_data, CI_data)
    
    # Calculate growth metrics and save the results
    if (nrow(CI_all) > 0) {
      # Add daily and cumulative metrics
      CI_all_with_metrics <- calculate_growth_metrics(CI_all)
      
      # Save the processed data
      save_growth_model(
        CI_all_with_metrics, 
        cumulative_CI_vals_dir, 
        "All_pivots_Cumulative_CI_quadrant_year_v2.rds"
      )
    } else {
      log_message("No CI data was generated after interpolation", level = "WARNING")
    }
    
    log_message("Growth model interpolation completed successfully")
    
  }, error = function(e) {
    log_message(paste("Error in growth model interpolation:", e$message), level = "ERROR")
    stop(e$message)
  })
}

if (sys.nframe() == 0) {
  main()
}