Timon
07aee7bed1
- Replace raster package with terra throughout the codebase - Update map visualizations with better layout and legends - Add descriptive headers to report sections - Improve map legend positioning and sizing - Enhance error handling for missing data - Remove redundant legends in field-specific visualizations - Optimize figure dimensions to prevent page overflow - Expand documentation of CI index and report components - Update package dependencies in packages.
87 lines
2.7 KiB
R
87 lines
2.7 KiB
R
# 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
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#
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# INTERPOLATE_GROWTH_MODEL.R
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# =========================
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# This script interpolates CI (Chlorophyll Index) values between measurement dates
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# to create a continuous growth model. It generates daily values and cumulative
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# CI statistics for each field.
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#
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# Usage: Rscript interpolate_growth_model.R [project_dir]
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# - project_dir: Project directory name (e.g., "chemba")
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#
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# 1. Load required packages
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# -----------------------
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suppressPackageStartupMessages({
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library(tidyverse)
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library(lubridate)
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library(here)
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})
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# 2. Main function to handle interpolation
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# -------------------------------------
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main <- function() {
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# Process command line arguments
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args <- commandArgs(trailingOnly = TRUE)
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# Get project directory from arguments or use default
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if (length(args) >= 1 && !is.na(args[1])) {
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project_dir <- as.character(args[1])
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} else {
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project_dir <- "chemba"
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message("No project_dir provided. Using default:", project_dir)
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}
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# Initialize project configuration and load utility functions
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source("r_app/parameters_project.R")
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source("r_app/growth_model_utils.R")
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log_message("Starting CI growth model interpolation")
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# Load and process the data
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tryCatch({
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# Load the combined CI data
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CI_data <- load_combined_ci_data(cumulative_CI_vals_dir)
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# Validate harvesting data
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if (is.null(harvesting_data) || nrow(harvesting_data) == 0) {
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stop("No harvesting data available")
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}
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# Get the years from harvesting data
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years <- harvesting_data %>%
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filter(!is.na(season_start)) %>%
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distinct(year) %>%
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pull(year)
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log_message(paste("Processing data for years:", paste(years, collapse = ", ")))
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# Generate interpolated CI data for each year and field
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CI_all <- generate_interpolated_ci_data(years, harvesting_data, CI_data)
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# Calculate growth metrics and save the results
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if (nrow(CI_all) > 0) {
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# Add daily and cumulative metrics
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CI_all_with_metrics <- calculate_growth_metrics(CI_all)
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# Save the processed data
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save_growth_model(
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CI_all_with_metrics,
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cumulative_CI_vals_dir,
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"All_pivots_Cumulative_CI_quadrant_year_v2.rds"
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)
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} else {
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log_message("No CI data was generated after interpolation", level = "WARNING")
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}
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log_message("Growth model interpolation completed successfully")
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}, error = function(e) {
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log_message(paste("Error in growth model interpolation:", e$message), level = "ERROR")
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stop(e$message)
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})
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}
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# Run the main function if the script is executed directly
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main()
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